CN115866678B - Mobile edge cache resource optimization method based on network energy consumption hot spot detection - Google Patents
Mobile edge cache resource optimization method based on network energy consumption hot spot detection Download PDFInfo
- Publication number
- CN115866678B CN115866678B CN202310133020.5A CN202310133020A CN115866678B CN 115866678 B CN115866678 B CN 115866678B CN 202310133020 A CN202310133020 A CN 202310133020A CN 115866678 B CN115866678 B CN 115866678B
- Authority
- CN
- China
- Prior art keywords
- energy consumption
- file
- time period
- network
- requested
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000005265 energy consumption Methods 0.000 title claims abstract description 263
- 238000000034 method Methods 0.000 title claims abstract description 69
- 238000001514 detection method Methods 0.000 title claims abstract description 26
- 238000005457 optimization Methods 0.000 title claims abstract description 24
- 230000002159 abnormal effect Effects 0.000 claims abstract description 24
- 238000004364 calculation method Methods 0.000 claims abstract description 15
- 238000012544 monitoring process Methods 0.000 claims abstract description 8
- 230000005540 biological transmission Effects 0.000 claims description 122
- 230000015654 memory Effects 0.000 claims description 13
- 238000004891 communication Methods 0.000 claims description 12
- 238000004146 energy storage Methods 0.000 claims description 12
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 238000012546 transfer Methods 0.000 claims description 3
- 238000007619 statistical method Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 9
- 230000008569 process Effects 0.000 description 9
- 238000004590 computer program Methods 0.000 description 7
- 230000003139 buffering effect Effects 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 230000003287 optical effect Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000013468 resource allocation Methods 0.000 description 3
- 238000006467 substitution reaction Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 230000002354 daily effect Effects 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 230000003321 amplification Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000006403 short-term memory Effects 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention belongs to the technical field of edge calculation, in particular relates to a mobile edge cache resource optimization method, a system and electronic equipment based on network energy consumption hot spot detection, and aims to solve the problems that in the prior art, burst hot spots in an MEC network cannot be monitored and a cache strategy cannot be dynamically adjusted, so that cache efficiency is low and robustness is poor. The method comprises the following steps: predicting the probability of each file being requested in each time period of each area on the same day; calculating the energy consumption of the whole network of each area on the same day; based on total time delay in each time period of the day or the energy consumption of the whole network in each area of the day, offline caching is carried out according to a preset first caching strategy; after offline caching, monitoring the network energy consumption state in real time, if the network energy consumption state is abnormal, extracting hot content data, and caching according to a preset second caching strategy. The invention improves the cache efficiency and the robustness of the existing MEC network.
Description
Technical Field
The invention belongs to the technical field of edge calculation, and particularly relates to a mobile edge cache resource optimization method, a system and electronic equipment based on network energy consumption hot spot detection.
Background
In a 5G mobile communication network, the edge calculation and buffering technology can effectively reduce transmission delay and improve user experience. In practical applications, network power management of a large number of distributed edge cache servers becomes a challenge, particularly when network conditions change, how to reconfigure network resources efficiently. How to optimize resource allocation at different locations in a MEC (Mobile Edge Caching, mobile edge cache) network when network hot spot content occurs, this optimization needs to meet the quality of service requirements of different users while minimizing the energy consumption of the network resource allocation.
In the prior art, two optimization schemes exist, one is to optimize edge network resources according to the data transmission speed of a transmission path and node information in the path, so that the edge computing application resource optimization allocation is more efficient, and the system performance can reach the optimum [1]; another is to reduce computational latency by using edge computation and caching resources, to study the problem of ultra-reliable and low-latency communication in edge networks by actively edge caching computation results, to ensure reliability by allowing a user node UN to offload its computation tasks to multiple edge compute nodes according to the concept of an undershoot request, and to minimize computation latency by combining task offloading and active caching of popular cacheable task computation results by using the computation and storage resources of cloudlets [2]. However, the two optimization schemes cannot monitor burst hot spots in the network and dynamically adjust the cache in real time. Based on the method, the invention provides a mobile edge cache resource optimization method based on network energy consumption hot spot detection.
The following documents are background information related to the present invention:
[1] yu Dan, lanyuqing, xing Zhihuan, a resource optimizing distribution method and system suitable for edge computing environment, 2021-10-18, cn 1141384573 b;
[2] deng Xiaoheng, liu Jin, grandchild, liu Mengjie, methods of edge cache based task offloading and resource optimization 2020-04-23, CN111552564A.
Disclosure of Invention
In order to solve the above problems in the prior art, that is, to solve the problem that the prior art cannot monitor burst hot spots in an MEC network and dynamically adjust a caching policy in real time, resulting in poor caching efficiency and robustness, the invention provides a mobile edge caching resource optimization method based on network energy consumption hot spot detection, which is applied to a mobile edge caching network, wherein the mobile edge caching network comprises a cloud server, a central node, an edge node and mobile equipment which are connected through a communication link, and the method comprises the following steps:
step S100, collecting historical content request data of a central node of each area, which is arranged in the mobile edge cache network; predicting the file requested in each time period of each area on the same day and the probability of each file being requested by combining the historical content request data and the area type corresponding to each area;
Step S200, according to the probability that each file is requested, the total time delay and the network energy consumption of the mobile edge cache network in each time period of the day are calculated by combining the transmission energy consumption data when each file is transmitted after being requested; based on the network energy consumption, calculating the overall network energy consumption of each area on the same day by combining the transmission energy consumption of the files to be updated and replaced in each time period; the transmission energy consumption data are the energy consumption data after the mobile equipment requests, wherein the file exists on a cloud server or a central node or an edge node; the transmission energy consumption data comprises transmission time delay, transmission energy consumption and storage energy consumption;
step S300, performing offline caching according to a preset first caching strategy based on the total time delay of the mobile edge caching network in each time period of the current day or the total network energy consumption of each area of the current day;
step S400, after offline caching, monitoring the network energy consumption state of the mobile edge cache network in real time; and if the network energy consumption state is abnormal, acquiring current content request data of each mobile device in the mobile edge cache network, extracting hot content data, and caching according to a preset second cache policy.
In some preferred embodiments, the probability of the requested file and the requested files within each time period of the day for each region is predicted by: predicting files requested by each region and the probability of each file being requested in each time period by a big data prediction method based on historical content request data of a central node of each region; the big data prediction method comprises a popularity statistical method and a prediction method based on a time-cycle neural network.
In some preferred embodiments, if the mobile edge cache network includes a central node and a plurality of edge nodes, the total delay in each time period is calculated by the following method:
wherein ,
representation->
Total time delay in time period, +.>
Representing the transmission delay of a file from a central node to a mobile device,/->
Representing the transmission delay of a file from a central node to an edge node,/->
Representing transmission delay from cloud server to central node, < >>
Transmission delay of file buffered at central node connected with it, representing mobile device request, +.>
Transmission delay of a file representing a mobile device request buffered at other edge nodes of its connected central node,/for>
Representing the transmission delay of a file requested by the mobile device cached on the cloud server,
representation->
The corresponding proportion of each transmission delay in the time period,
，/>
representing the number of requests made by the mobile device to the file, < >>
Representing a time period.
In some preferred embodiments, if the mobile edge cache network includes a central node and a plurality of edge nodes, the network energy consumption in each time period is calculated by the following method:
wherein ,
representation->
Network energy consumption during a time period +. >
Representation->
First transmission energy consumption in time period, +.>
Representation->
First energy storage consumption in time period, +.>
Representation->
Request +.>
Size of individual file->
Representing the transmission energy consumption of a file from an edge node to a mobile device,/->
Representing the transmission energy consumption of a file from a central node to an edge node,/->
Representing the energy consumption of the file transfer from the cloud server to the central node, < >>
Transmission energy consumption representing that the file requested by the mobile device is cached at the central node to which it is connected, < >>
Transmission energy consumption, for example, representing that a file requested by a mobile device is cached on other edge nodes of a central node to which it is connected,/->
Transmission energy consumption representing that a file requested by a mobile device is cached on a cloud server, +.>
Storage energy consumption on a central node representing a file cache requested by a mobile device, < >>
Storage energy consumption on edge node representing file cache requested by mobile device,/for mobile device>
The file representing the mobile device request is cached in the storage energy of the cloud server.
In some preferred embodiments, based on the network energy consumption, the overall network energy consumption of each area on the same day is calculated by combining the transmission energy consumption of the file to be updated and replaced in each time period, and the method comprises the following steps:
wherein ,
Representation->
Transmission energy consumption of files to be updated and replaced in time period,/-for>
Representation->
The +.f. of the replacement to be updated during the time period>
Size of individual file->
Representing the number of file formats>
Representing the number of divided time periods per day.
In some preferred embodiments, based on the total time delay of the mobile edge caching network in each time period of the day or the overall network energy consumption of each area in the day, offline caching is performed according to a preset first caching policy, and the method comprises the following steps:
acquiring a file requested by the minimum time delay in each time period as a file to be cached offline in each time period; or acquiring a file requested by each time period when the energy consumption of the whole network of each area is minimum in the current day, and taking the file as a file to be cached offline in each time period;
sequentially carrying out hierarchical caching on each edge node and the center node based on the files to be cached offline in each time period;
when in cache, the strategy for updating and replacing the file is as follows:
acquiring a time period corresponding to a file to be cached offline as a first time period;
if the first time period is in a preset off-peak time period, directly transmitting a file to be cached offline in the current time period from the cloud server to an edge node for caching;
If the first time period is in the preset peak time period, when the mobile equipment requests a file from a corresponding edge node and the edge node does not cache the requested file, judging whether the requested file is a file to be cached offline, and if so, caching the requested file from the cloud server to the corresponding edge node;
if the cache space of the edge node in the current area is full, judging whether the file cached offline in the previous time period of the first time period is not in the high popularity list of the first time period, and if so, directly replacing; if not, judging whether a central node connected with the edge node has a storage space, and if so, caching the central node to the central node in an offline manner; the high popularity list is a file list with the probability that the file is requested being greater than a set probability threshold;
and if the caching spaces of the edge nodes and the center nodes in the current area are full, ordering the files cached in the center nodes and the edge nodes in descending order according to the probability that each file is requested, and then replacing the files to be cached offline in sequence from low to high.
In some preferred embodiments, the network energy consumption state is obtained by the following steps:
If the total overall network energy consumption in the second time period is greater than the set first energy consumption threshold, the network energy consumption state of the mobile edge cache network is abnormal;
or if the first transmission energy consumption in the second time period is greater than a set second energy consumption threshold, the network energy consumption state of the mobile edge cache network is abnormal;
or if the difference between the overall network energy consumption in the second time period and the overall network energy consumption in the third time period is greater than a set first difference threshold, and if the difference between the first transmission energy consumption in the second time period and the first transmission energy consumption in the third time period is greater than a set second difference threshold, the network energy consumption state of the mobile edge cache network is abnormal; the second time period is the previous time period of the current time period; the third time period is a time period preceding the second time period.
In some preferred embodiments, the caching is performed according to a preset second caching policy, and the method includes:
if the network energy consumption state is detected to be abnormal for the first time within the set time period, the extracted hot content data is cached to a central node of the mobile edge caching network: replacing the file with the smallest probability of being requested in the central node of the mobile edge cache network with the extracted hot content data;
Otherwise, caching the extracted hot content data to an edge node of the mobile edge caching network: and replacing the extracted hot content data with the file with the smallest probability of being requested in the edge nodes of the mobile edge cache network.
In a second aspect of the present invention, a mobile edge cache resource optimization system based on network energy consumption point detection is provided, and the system is applied to a mobile edge cache network, where the mobile edge cache network includes a cloud server, a central node, an edge node, and a mobile device connected by a communication link, and the system includes: the system comprises a prediction module, an energy consumption calculation module, a first cache strategy module and a second cache strategy module;
the prediction module is configured to collect historical content request data of a central node of each area, which is arranged by the mobile edge cache network; predicting the file requested in each time period of each area on the same day and the probability of each file being requested by combining the historical content request data and the area type corresponding to each area;
the energy consumption calculation module is configured to calculate the total time delay and the network energy consumption of the mobile edge cache network in each time period of the day according to the probability that each file is requested and in combination with the transmission energy consumption data when each file is transmitted after being requested; based on the network energy consumption, calculating the overall network energy consumption of each area on the same day by combining the transmission energy consumption of the files to be updated and replaced in each time period; the transmission energy consumption data are the energy consumption data after the mobile equipment requests, wherein the file exists on a cloud server or a central node or an edge node; the transmission energy consumption data comprises transmission time delay, transmission energy consumption and storage energy consumption;
The first caching strategy module is configured to perform offline caching according to a preset first caching strategy based on total time delay of the mobile edge caching network in each time period of the same day or overall network energy consumption of each area of the same day;
the second cache policy module is configured to monitor the network energy consumption state of the mobile edge cache network in real time after offline cache; and if the network energy consumption state is abnormal, acquiring current content request data of each mobile device in the mobile edge cache network, extracting hot content data, and caching according to a preset second cache policy.
In a third aspect of the present invention, an electronic device is provided, including: at least one processor; and a memory communicatively coupled to at least one of the processors; the memory stores instructions executable by the processor, where the instructions are configured to be executed by the processor to implement the above-described method for optimizing mobile edge cache resources based on network energy consumption hot spot detection.
The invention has the beneficial effects that:
the invention improves the cache efficiency and the robustness of the existing MEC network.
The invention researches the root speculation and reconfiguration trigger strategy of the energy hot spot on the network, adopts a layered sub-service deployment strategy to detect the network energy hot spot and determine the reconfiguration range, and utilizes two modes of off-line and on-line to cope with different network conditions, and ensures that the network service quality and the network energy consumption are optimal by balancing the relationship between the network energy consumption caused by the on-line mode and the off-line mode.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings.
FIG. 1 is a flow chart of a method for optimizing mobile edge cache resources based on network energy consumption hot spot detection according to an embodiment of the invention;
FIG. 2 is a diagram illustrating a network architecture of a mobile edge cache network according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a mobile edge cache resource optimization system based on network energy consumption hot spot detection according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of a method for optimizing mobile edge cache resources based on network energy consumption hot spot detection according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating network energy consumption in different modes according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The present application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other.
The method for optimizing the mobile edge cache resource based on network energy consumption hot spot detection according to the first embodiment of the present invention, as shown in fig. 1, includes:
the method is applied to a mobile edge cache network, the mobile edge cache network comprises a cloud server, a central node, an edge node and mobile equipment which are connected through communication links, and the method comprises the following steps:
step S100, collecting historical content request data of a central node of each area, which is arranged in the mobile edge cache network; predicting the file requested in each time period of each area on the same day and the probability of each file being requested by combining the historical content request data and the area type corresponding to each area;
step S200, according to the probability that each file is requested, the total time delay and the network energy consumption of the mobile edge cache network in each time period of the day are calculated by combining the transmission energy consumption data when each file is transmitted after being requested; based on the network energy consumption, calculating the overall network energy consumption of each area on the same day by combining the transmission energy consumption of the files to be updated and replaced in each time period; the transmission energy consumption data are the energy consumption data after the mobile equipment requests, wherein the file exists on a cloud server or a central node or an edge node; the transmission energy consumption data comprises transmission time delay, transmission energy consumption and storage energy consumption;
Step S300, performing offline caching according to a preset first caching strategy based on the total time delay of the mobile edge caching network in each time period of the current day or the total network energy consumption of each area of the current day;
step S400, after offline caching, monitoring the network energy consumption state of the mobile edge cache network in real time; and if the network energy consumption state is abnormal, acquiring current content request data of each mobile device in the mobile edge cache network, extracting hot content data, and caching according to a preset second cache policy.
In order to more clearly describe the mobile edge cache resource optimization method based on network energy consumption point detection, each step in one embodiment of the method of the present invention is described in detail below with reference to the accompanying drawings.
The application designs an off-line method and an on-line method for energy-efficient MEC network resource allocation. In the off-line methods such as calculation, caching, and transmission resource pre-calculation and allocation, a certain number of resource configurations are determined according to the positions and types of different edge nodes, for example, statistics and prediction of content popularity are respectively performed according to different areas (different types of residential areas, commercial areas, schools, etc.). These pre-computed offline configuration schemes are then mapped to a pre-computed resource configuration that has minimal error and energy consumption penalty for a given request set.
The trigger strategy will be studied for root cause speculation and reconfiguration of energy hotspots on the network. Although the resources used to process daily user requests have been preconfigured in the network, network uncertainty can occur at any time and result in bursty energy hotspots in the network. The present application contemplates employing a hierarchical sub-service deployment strategy to detect network energy hotspots and determine the scope of reconfiguration. When the pre-computed configuration is deployed in a network and has an expected service usage pattern, certain error thresholds will be pre-set according to the hierarchy in the network to monitor the expected network energy consumption. Once the energy threshold is reached due to unusual service patterns in the area, the lowest level network sub-service module will perform anomaly detection on the service request based on classification of the preprocessing of user, service group and current service usage pattern changes in the area, send a query to the higher level network module to decide the range of reconfiguration, and finally guarantee the service quality of the hot spot area. The method comprises the following steps:
step S100, collecting historical content request data of a central node of each area, which is arranged in the mobile edge cache network; predicting the file requested in each time period of each area on the same day and the probability of each file being requested by combining the historical content request data and the area type corresponding to each area;
In this embodiment, the mobile edge cache network includes a cloud server, a central node, an edge node, and a mobile device; cloud servers, central nodes, edge nodes (i.e., distributed nodes), mobile devices are connected by communication links, as shown in fig. 2.
By dynamically detecting energy hotspots in MEC network facilities in real time, and based on MEC services and different applications running on these network facilities, the network content that causes the source of the energy hotspots can be determined. In addition, the optimization method of network energy consumption caused by the reallocation of MEC network resources due to the change of network conditions is also provided. Since a two-stage architecture of a Centralized central node CU (central node) and a Distributed node DU (Distributed node) is adopted in the 5G access network, the complexity of the edge cache policy is increased. Two different nodes can allocate computing and buffering resources, and different edge buffering deployment scenes and corresponding network parameters.
In the above architecture, one CU node is connected to a plurality of DU nodes, and in general, the buffering capacity of one CU node is much greater than that of each DU, and the working energy consumption of the CU is also higher than that of the DU. The hot content may be cached either at the CU node or at the DU node. The optimized edge caching strategy meets the content request of the mobile equipment as much as possible, namely, as much request content as possible is cached in advance on the CU node or the DU node, so that the service quality of the user can be improved. If the content requested by the mobile device is not found in the edge cache, it needs to be read from the cloud server. The content on the cloud server is transmitted to the mobile device through the internet, and needs to be transmitted through a backbone network, an access network and other network devices, so that the transmission delay is increased sharply, the transmission pressure of the backbone network is increased, and the network energy consumption is increased.
The most interesting issue for content networks and edge cache nodes is how to distribute content of interest to users to the edge nodes closest to the users at the best cost performance. In order to maximize the utilization of the limited edge cache space, it is necessary to determine what the user is most likely to request and cache in advance.
Based on the above purpose, the edge cache nodes are classified according to the characteristics of different areas, and the content popularity of each area is calculated respectively, so that the edge cache efficiency is improved. The classification of different areas can be divided based on the area function, and also can be divided according to the personnel type of the area. Common classifications are residential areas, commercial areas, universities, transportation hubs, stadiums, etc., and the present invention preferably classifies according to the classification method. Historical request data (i.e., historical content request data) is counted on CU nodes of each area respectively, and time-based content request data distribution in different area types is established.
The user clustering result, popularity knowledge base, popularity level, caching strategy, distribution strategy and the like are integrated to facilitate the establishment of a complete offline mode edge caching strategy.
According to the study, the idle period of network requests is 2-6 a.m. each day. In order not to put stress on the transmission of the backbone network, it is assumed that the edge cache content of the day is updated at 2 a.m. every day. First, the time of day is divided into time intervals
The smaller the time interval, which means that the accuracy of prediction is more accurate, the more efficient the edge buffering is, but additional replacement transmission of the buffered content is also required. It should be noted that the time period may be non-fixed, e.g. during peak user request times, it may take 30 minutes, whereas by off-peak times such as night, the time interval may reach several hours.
According to the statistical historical content request data, carrying out popularity statistics or a prediction method based on a time-cycle neural network such as a Long Short-Term Memory (LSTM) network and the like on the content request data of each region respectively, analyzing and calculating the popular content of each time period in the day to obtain a file list of the region with the high user request probability from high to low
， />
The number of file formats (i.e. total file format) is +. >
The list is ordered according to popularity, that is to say files +.>
At->
The probability that a time period is requested is highest. Assume that the size of each file is +.>
， />
In megabits (Mb), the probability of being requested is +.>
. It should be noted that the time period is not fixed, and may be 30 minutes during peak demand time of the user, and may reach several hours at off-peak time such as night. />
Step S200, according to the probability that each file is requested, the total time delay and the network energy consumption of the mobile edge cache network in each time period of the day are calculated by combining the transmission energy consumption data when each file is transmitted after being requested; based on the network energy consumption, calculating the overall network energy consumption of each area on the same day by combining the transmission energy consumption of the files to be updated and replaced in each time period; the transmission energy consumption data are the energy consumption data after the mobile equipment requests, wherein the file exists on a cloud server or a central node or an edge node; the transmission energy consumption data comprises transmission time delay, transmission energy consumption and storage energy consumption.
In this embodiment, according to the buffer space size of the edge node, the buffer space size is as follows
Time period, list files 
Files with high popularity are cached on edge node CUs or DUs. It is assumed here that each file can be stored on any DU or CU without being stored after splitting due to the file being too large, and even if a situation that splitting is required due to the file being too large is encountered, the split file can be treated as an independent file (i.e. the size of the file has no influence on the algorithm, if one file is too large, and exceeds the storage capacity of one DU, splitting into multiple files is required to ensure that the size of a single file is smaller than the storage space of one DU. The mobile device has the following 4 cases per request:
1) Mobile device requested file
Just buffered on the DU to which it is connected, the transmission delay is +.>
The transmission energy consumption is->
The energy consumption of storage is->
；
2) Mobile device requested file
Buffered on CU, file needs to be transferred from CU to DU to mobile device, then transmission delay is +.>
，The transmission energy consumption is->
The energy consumption of storage is->
；
3) Mobile device requested file 
The file is buffered on other DUs connected with the same CU, and the file needs to be transferred through the CU, so that the transmission delay is +.>
The transmission energy consumption is->
The energy consumption of storage is->
；
4) Mobile device requested file
Not found in the edge cache, needs to be obtained from the cloud server, then the transmission delay is +.>
The transmission energy consumption is->
The energy consumption of storage is->
；
wherein , 
、 />
、 />
representing the transmission delay from the DU to the mobile device (i.e. the file requested by the mobile device is cached in its connection, respectivelyTransmission delay on edge nodes of (c), transmission delay from CU to DU, transmission delay from cloud server to CU. In general, a->
、 />
Relatively small, and->
Relatively large; the transmission energy consumption is mainly composed of unit energy consumption of a core router, an edge router, a broadband network gateway and an Ethernet switch, wherein the unit is J/bit, < >>
、 />
、 />
Respectively representing transmission energy consumption from DU to mobile device, transmission energy consumption from CU to DU and transmission energy consumption from cloud server to CU; the energy consumption of the edge cache server and the unit cache energy consumption of the data center cloud server can be calculated according to the working power, the energy storage capacity and the cache time of the servers, wherein the unit is J/bit >
、/>
、 />
The method respectively represents the energy consumption of the file requested by the mobile device cached on the edge node, the energy consumption of the file requested by the mobile device cached on the center node and the energy consumption of the file requested by the mobile device cached on the cloud server.
Is assumed to be in
Within the range covered by one CU in the time period, the mobile device initiates M requests, and the proportion of the above 4 cases is +.>
Then the total delay ∈>
The method comprises the following steps: />
wherein , 
representation->
Total time delay in time period, +.>
Transmission delay of file buffered at central node connected with it, representing mobile device request, +.>
Transmission delay of a file representing a mobile device request buffered at other edge nodes of its connected central node,/for>
Transmission delay of file cached on cloud server representing mobile device request,/for mobile device request>
Indicating the corresponding proportion of each transmission delay,
， />
indicating the number of requests the mobile device requests to the file.
Network systemEnergy consumption
Can be determined from the first transmission energy consumption +.>
And a first energy storage consumption->
The calculation is as follows:
wherein , 
representation->
Network energy consumption during a time period +.>
Representation->
First transmission energy consumption in time period, +.>
Representation->
First energy storage consumption in time period, +. >
Representation->
Request +.>
Size of individual file->
Representing the transmission energy consumption of a file from an edge node to a mobile device,/->
Representing the transmission energy consumption of a file from a central node to an edge node,/->
Representing the energy consumption of the file transfer from the cloud server to the central node, < >>
Transmission energy consumption representing that the file requested by the mobile device is cached at the central node to which it is connected, < >>
Transmission energy consumption, for example, representing that a file requested by a mobile device is cached on other edge nodes of a central node to which it is connected,/->
Representing the transmission energy consumption of a file requested by a mobile device cached on a cloud server.
List of files most likely to be requested by the user due to each time period
There is a difference that the files with the top request probabilities are subjected to edge caching in the above manner, so that the cached files need to be updated and replaced between each time period. Let per time period +.>
The list of the cache files needing to be updated and replaced is
For simplicity of calculation, the number of files is also N, and if the number of files replaced is less than N, the files in the back of the list are empty. The size of the files is +.>
， 
In megabits (Mb), is given in units of +.>
Representation->
The +.f. of the replacement to be updated during the time period >
Size of individual file->
Representation->
The +.f. of the replacement to be updated during the time period>
A file. These updates and substitutions bring additional transmission power consumption
The calculation can be performed by the following formula:
from the above analysis it can be seen that the cost of minimizing the transmission delay in order to optimize the user experience is to update more content in the cache. In a network energy consumption priority scenario, an offline caching scheme may be set from an energy consumption optimization perspective. The overall energy consumption optimization formula of the daily network is as follows:
because the problem is not a convex optimization problem and cannot be directly solved, the optimization formula can be solved by adjusting the cache content of each time period, so that the cache scheme with optimal energy consumption is obtained. The scheme can be used as a caching scheme with optimal energy consumption in an offline mode.
Step S300, performing offline caching according to a preset first caching strategy based on the total time delay of the mobile edge caching network in each time period of the current day or the total network energy consumption of each area of the current day;
in this embodiment, a file requested by the time with the smallest total time delay in each time period is obtained and used as a file to be cached offline in each time period; or acquiring the file requested by each time period when the energy consumption of the whole network in each area is minimum, taking the file as the file to be cached offline in each time period, and carrying out hierarchical caching on the file to be cached offline in the CU and the DU. Since file popularity of different time periods often varies, it is necessary to update and replace cached content. Updating the replaced files requires acquisition from the cloud server, which brings about backbone pressure and additional network power consumption. The treatment can be carried out according to the network condition:
1) Acquiring a time period corresponding to a file to be cached offline as a first time period; if the first time period is in the preset off-peak time period, directly transmitting the file to be cached offline in the current time period from the cloud server to an edge node for caching. In the off-peak period of the network, the content to be cached in the next period of time can be directly transmitted from the cloud server to the edge node for caching in advance, so that the service quality of a user can be improved;
2) If the first time period is in the preset peak time period, when the mobile equipment requests a file from a corresponding edge node and the edge node does not cache the requested file, judging whether the requested file is a file to be cached offline, and if so, caching the requested file from the cloud server to the corresponding edge node. In the network peak period, the file requested by the mobile equipment is not transmitted from the cloud server in advance, but is not buffered at the edge node, when the file needs to be acquired from the cloud server, whether the file is to be buffered or not is judged, and if so, the file is buffered on the corresponding DU node;
3) If the cache space of the edge node in the current area is full, judging whether the file cached offline in the previous time period of the first time period is not in the high popularity list of the first time period, and if so, directly replacing; if not, judging whether a central node connected with the edge node has a storage space, and if so, caching the central node to the central node in an offline manner; the high popularity list is a file list with the probability that the file is requested greater than a set probability threshold. If the buffer space of the DU node is full, judging whether the files in the high popularity list are not in the next time, and if so, replacing the files. If not, judging whether the CU connected with the DU has a storage space. Until the CU and all DUs are full;
4) And if the caching spaces of the edge nodes and the center nodes in the current area are full, ordering the files cached in the center nodes and the edge nodes in descending order according to the probability that each file is requested, and then replacing the files to be cached offline in sequence from low to high. When the CU and all DUs are full, judging the popularity of each file, and replacing according to the popularity, so that the cached files are files with the highest popularity in the next time period.
Step S400, after offline caching, monitoring the network energy consumption state of the mobile edge cache network in real time; and if the network energy consumption state is abnormal, acquiring current content request data of each mobile device in the mobile edge cache network, extracting hot content data, and caching according to a preset second cache policy.
In this embodiment, the network energy consumption calculation method in the previous step is used to monitor the network energy consumption in real time, and once the network energy consumption in a certain period of time is found to increase sharply and reaches a preset threshold (i.e. the network energy consumption state is abnormal), the reason for causing the sudden increase of the network energy consumption needs to be analyzed. Such as on-line teaching or on-line meetings at universities, sports or singing at stadiums, etc. When these situations occur, users in the area can request a large amount of the same video content, resulting in a rapid increase in network traffic in the area, and because these content are sudden hot spots and are not in the list of high popularity files in the offline mode, they generally need to be acquired from the server, so that network energy consumption increases dramatically.
Based on the statistical historical content request data, overall network energy consumption for a previous time period (e.g., 30 minutes)
And first transmission energy consumption->
Setting corresponding threshold +.>
and />
When the following is detected:
alternatively, the overall network energy consumption for the immediately preceding time period
And first transmission energy consumption->
Compared with the abrupt amplification, the network energy consumption is judged to be abnormal, and hot content monitoring and edge caching are needed. If the total overall network energy consumption in the second time period is greater than the set first energy consumption threshold, the network energy consumption state of the mobile edge cache network is abnormal; or if the first transmission energy consumption in the second time period is greater than a set second energy consumption threshold, the network energy consumption state of the mobile edge cache network is abnormal; or if the difference between the overall network energy consumption in the second time period and the overall network energy consumption in the third time period is greater than a set first difference threshold, and if the difference between the first transmission energy consumption in the second time period and the first transmission energy consumption in the third time period is greater than a set second difference threshold, the network energy consumption state of the mobile edge cache network is abnormal; the second time period is the current time period (i.e kTime period) of the previous time period; the third time period is a time period preceding the second time period.
When the abnormal state of the network energy consumption is monitored, the network content request condition is analyzed in real time on line, and sudden hot spot content is extracted. And the files with the lowest popularity are replaced by the contents and are cached on the central node CU preferentially, so that the service quality of users is improved, and meanwhile, the pressure of a backbone network is relieved. And continuously monitoring the network energy consumption, and if the network energy consumption is still high, further caching the hot spot contents on the distributed node DU. If the network energy consumption state is detected to be abnormal for the first time within the set time period, the extracted hot content data is cached to a central node of the mobile edge caching network: replacing the file with the smallest probability of being requested in the central node of the mobile edge cache network with the extracted hot content data; otherwise, caching the extracted hot content data to an edge node of the mobile edge caching network: and replacing the extracted hot content data with the file with the smallest probability of being requested in the edge nodes of the mobile edge cache network.
After the buffer memory space in the CU and DU is occupied by the temporary hot content, the storage space of the high popularity content in the offline mode becomes smaller, so that the request of the part of content cannot be satisfied in the edge buffer memory, further more normal request content needs to be acquired from the cloud server, and the network energy consumption of the part of request also increases. More temporary hot content is stored on the edge node DU, so that the network energy consumption caused by this part of content is reduced. The game between the two can finally reach a balance, so that the energy consumption of the whole network reaches the minimum.
The duration of the burst temporary hot content is often short, and when the heat of the temporary hot content is reduced, the network energy consumption caused by the normal request content monitored at the CU is increased, and when the temporary hot content reaches a critical point, the online real-time caching mode is ended, and the offline caching mode is continuously entered, as shown in fig. 4.
In addition, in order to verify the effectiveness of the method, the network energy consumption in different modes is compared, as shown in fig. 5, it can be seen that when burst hot spots occur, the transmission energy consumption from the cloud server to the CU is obviously increased, and then the judgment condition in the network energy consumption monitoring module is triggered. At this time, the hot content is temporarily cached according to the monitored energy consumption source, so that the transmission energy consumption is greatly reduced, and when the cache space size is 100 video files, the transmission energy consumption from the cloud server to the CU can be reduced by 52%.
The mobile edge cache resource optimization system based on network energy consumption point detection according to the second embodiment of the present invention is applied to a mobile edge cache network as shown in fig. 3, where the mobile edge cache network includes a cloud server, a central node, an edge node, and a mobile device connected by a communication link, and the system includes: a prediction module 100, an energy consumption calculation module 200, a first cache policy module 300, and a second cache policy module 400;
The prediction module 100 is configured to collect historical content request data of a central node of each area of the mobile edge cache network; predicting the file requested in each time period of each area on the same day and the probability of each file being requested by combining the historical content request data and the area type corresponding to each area;
the energy consumption calculation module 200 is configured to calculate the total time delay and the network energy consumption of the mobile edge cache network in each time period of the day according to the probability that each file is requested and in combination with the transmission energy consumption data when each file is transmitted after being requested; based on the network energy consumption, calculating the overall network energy consumption of each area on the same day by combining the transmission energy consumption of the files to be updated and replaced in each time period; the transmission energy consumption data are the energy consumption data after the mobile equipment requests, wherein the file exists on a cloud server or a central node or an edge node; the transmission energy consumption data comprises transmission time delay, transmission energy consumption and storage energy consumption;
the first caching policy module 300 is configured to perform offline caching according to a preset first caching policy based on a total time delay of the mobile edge caching network in each time period of the day or an overall network energy consumption of each area in the day;
The second cache policy module 400 is configured to monitor the network energy consumption state of the mobile edge cache network in real time after offline cache; and if the network energy consumption state is abnormal, acquiring current content request data of each mobile device in the mobile edge cache network, extracting hot content data, and caching according to a preset second cache policy.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working processes and related descriptions of the above-described system may refer to corresponding processes in the foregoing method embodiments, which are not repeated herein.
It should be noted that, in the mobile edge cache resource optimization system based on network energy consumption point detection provided in the foregoing embodiment, only the division of the foregoing functional modules is illustrated, in practical application, the foregoing functional allocation may be completed by different functional modules according to needs, that is, the modules or steps in the foregoing embodiment of the present invention are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further split into multiple sub-modules, so as to complete all or part of the functions described above. The names of the modules and steps related to the embodiments of the present invention are merely for distinguishing the respective modules or steps, and are not to be construed as unduly limiting the present invention.
An electronic device of a third embodiment of the present invention includes at least one processor; and a memory communicatively coupled to at least one of the processors; the memory stores instructions executable by the processor, where the instructions are configured to be executed by the processor to implement the above-described method for optimizing mobile edge cache resources based on network energy consumption hot spot detection.
A computer readable storage medium according to a fourth embodiment of the present invention stores computer instructions for execution by the computer to implement the above-described method for optimizing mobile edge cache resources based on network energy consumption hot spot detection.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working processes of the electronic device, the computer readable storage medium and related descriptions of the electronic device and the computer readable storage medium described above may refer to corresponding processes in the foregoing method examples, which are not described herein again.
Reference is now made to FIG. 6, which is a block diagram illustrating a computer system suitable for use in implementing embodiments of the methods, systems, and apparatus of the present application. The server illustrated in fig. 6 is merely an example, and should not be construed as limiting the functionality and scope of use of the embodiments herein.
As shown in fig. 6, the computer system includes a central processing unit (CPU, central Processing Unit) 601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a random access Memory (RAM, random Access Memory) 603. In the RAM603, various programs and data required for system operation are also stored. The CPU601, ROM602, and RAM603 are connected to each other through a bus 604. An Input/Output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, mouse, etc.; an output portion 607 including a Cathode Ray Tube (CRT), a liquid crystal display (LCD, liquid Crystal Display), and the like, a speaker, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN (local area network ) card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to the I/O interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.
In particular, according to embodiments of the present invention, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 609 and/or installed from the removable medium 611. The computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination thereof, a more specific example of a computer-readable storage medium may include, but is not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof A program for use by or in connection with an instruction execution system, apparatus, or device is propagated or transmitted. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present application may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The terms "first," "second," and the like, are used for distinguishing between similar objects and not for describing a particular sequential or chronological order.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus/apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus/apparatus.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will fall within the scope of the present invention.
Claims (8)
1. The mobile edge cache resource optimization method based on network energy consumption hot spot detection is applied to a mobile edge cache network, and the mobile edge cache network comprises a cloud server, a center node, an edge node and mobile equipment which are connected through a communication link, and is characterized in that the method comprises the following steps:
Step S100, collecting historical content request data of a central node of each area, which is arranged in the mobile edge cache network; predicting the file requested in each time period of each area on the same day and the probability of each file being requested by combining the historical content request data and the area type corresponding to each area;
step S200, according to the probability that each file is requested, the total time delay and the network energy consumption of the mobile edge cache network in each time period of the day are calculated by combining the transmission energy consumption data when each file is transmitted after being requested; based on the network energy consumption, calculating the overall network energy consumption of each area on the same day by combining the transmission energy consumption of the files to be updated and replaced in each time period; the transmission energy consumption data are the energy consumption data after the mobile equipment requests, wherein the file exists on a cloud server or a central node or an edge node; the transmission energy consumption data comprises transmission time delay, transmission energy consumption and storage energy consumption;
step S300, based on the total time delay of the mobile edge cache network in each time period of the current day or the overall network energy consumption of each area of the current day, performing offline cache according to a preset first cache policy:
acquiring a file requested by the minimum time delay in each time period as a file to be cached offline in each time period; or acquiring a file requested by each time period when the energy consumption of the whole network of each area is minimum in the current day, and taking the file as a file to be cached offline in each time period;
Sequentially carrying out hierarchical caching on each edge node and the center node based on the files to be cached offline in each time period;
when in cache, the strategy for updating and replacing the file is as follows:
acquiring a time period corresponding to a file to be cached offline as a first time period;
if the first time period is in a preset off-peak time period, directly transmitting a file to be cached offline in the current time period from the cloud server to an edge node for caching;
if the first time period is in the preset peak time period, when the mobile equipment requests a file from a corresponding edge node and the edge node does not cache the requested file, judging whether the requested file is a file to be cached offline, and if so, caching the requested file from the cloud server to the corresponding edge node;
if the cache space of the edge node in the current area is full, judging whether the file cached offline in the previous time period of the first time period is not in the high popularity list of the first time period, and if so, directly replacing; if not, judging whether a central node connected with the edge node has a storage space, and if so, caching the central node to the central node in an offline manner; the high popularity list is a file list with the probability that the file is requested being greater than a set probability threshold;
If the caching spaces of the edge nodes and the center nodes in the current area are full, ordering the files cached in the center nodes and the edge nodes in descending order according to the probability that each file is requested, and then replacing the files to be cached offline in sequence from low to high;
step S400, after offline caching, monitoring the network energy consumption state of the mobile edge cache network in real time; if the network energy consumption state is abnormal, acquiring current content request data of each mobile device in the mobile edge cache network, extracting hot content data, and caching according to a preset second cache strategy;
the method comprises the following steps of:
if the network energy consumption state is detected to be abnormal for the first time within the set time period, the extracted hot content data is cached to a central node of the mobile edge caching network: replacing the file with the smallest probability of being requested in the central node of the mobile edge cache network with the extracted hot content data;
otherwise, caching the extracted hot content data to an edge node of the mobile edge caching network: and replacing the extracted hot content data with the file with the smallest probability of being requested in the edge nodes of the mobile edge cache network.
2. The method for optimizing mobile edge cache resources based on network energy consumption point detection according to claim 1, wherein predicting the requested file and the requested probability of each file in each time period of each regional day comprises the following steps: predicting files requested by each region and the probability of each file being requested in each time period by a big data prediction method based on historical content request data of a central node of each region; the big data prediction method comprises a popularity statistical method and a prediction method based on a time-cycle neural network.
3. The method for optimizing mobile edge cache resources based on network energy consumption point detection according to claim 2, wherein if the mobile edge cache network includes a central node and a plurality of edge nodes, the total time delay in each time period is calculated by the following steps:
；
wherein ,
representation->
Total time delay in time period, +.>
Representing the transmission delay of a file from a central node to a mobile device,/->
Representing the transmission delay of a file from a central node to an edge node,/->
Representing transmission delay from cloud server to central node, < >>
Representing the transmission delay of a file requested by the mobile device buffered at the central node to which it is connected, 
Transmission delay of a file representing a mobile device request buffered at other edge nodes of its connected central node,/for>
Representing the transmission delay of a file requested by the mobile device cached on the cloud server,
representation->
The corresponding proportion of each transmission delay in the time period,
，/>
representing the number of requests made by the mobile device to the file, < >>
Representing a time period.
4. The method for optimizing mobile edge cache resources based on network energy consumption point detection according to claim 3, wherein if the mobile edge cache network comprises a central node and a plurality of edge nodes, the network energy consumption in each time period is calculated by the following steps:
wherein ,
representation->
Network energy consumption during a time period +.>
Representation->
The energy consumption of the first transmission in the time period,
representation->
First energy storage consumption in time period, +.>
Representation->
Request +.>
Size of individual file->
Representing the transmission energy consumption of a file from an edge node to a mobile device,/->
Representing the transmission energy consumption of a file from a central node to an edge node,/->
Representing the energy consumption of the file transfer from the cloud server to the central node, < >>
Transmission energy consumption representing that the file requested by the mobile device is cached at the central node to which it is connected, < > >
Transmission energy consumption, for example, representing that a file requested by a mobile device is cached on other edge nodes of a central node to which it is connected,/->
Transmission energy consumption representing that a file requested by a mobile device is cached on a cloud server, +.>
Representing the storage energy consumption of the file cache requested by the mobile device on the central node,
storage energy consumption on edge node representing file cache requested by mobile device,/for mobile device>
The file representing the mobile device request is cached in the storage energy of the cloud server.
5. The method for optimizing mobile edge cache resources based on network energy consumption point detection according to claim 4, wherein the method for calculating the overall network energy consumption of each area on the same day based on the network energy consumption and combined with the transmission energy consumption of the file to be updated and replaced in each time period comprises the following steps:
wherein ,
representation->
Transmission energy consumption of files to be updated and replaced in time period,/-for>
Representation->
The +.f. of the replacement to be updated during the time period>
Size of individual file->
Representing the number of file formats>
Representing the number of divided time periods per day.
6. The method for optimizing mobile edge cache resources based on network energy consumption point detection according to claim 5, wherein the network energy consumption state is obtained by the following steps:
If the total overall network energy consumption in the second time period is greater than the set first energy consumption threshold, the network energy consumption state of the mobile edge cache network is abnormal;
or (b)
If the first transmission energy consumption in the second time period is larger than a set second energy consumption threshold, the network energy consumption state of the mobile edge cache network is abnormal;
or (b)
If the difference between the overall network energy consumption in the second time period and the overall network energy consumption in the third time period is larger than a set first difference threshold, and if the difference between the first transmission energy consumption in the second time period and the first transmission energy consumption in the third time period is larger than a set second difference threshold, the network energy consumption state of the mobile edge cache network is abnormal; the second time period is the previous time period of the current time period; the third time period is a time period preceding the second time period.
7. A mobile edge cache resource optimization system based on network energy consumption point detection, which is applied to a mobile edge cache network, wherein the mobile edge cache network comprises a cloud server, a central node, an edge node and mobile equipment which are connected through a communication link, and the system is characterized in that the system comprises: the system comprises a prediction module, an energy consumption calculation module, a first cache strategy module and a second cache strategy module;
The prediction module is configured to collect historical content request data of a central node of each area, which is arranged by the mobile edge cache network; predicting the file requested in each time period of each area on the same day and the probability of each file being requested by combining the historical content request data and the area type corresponding to each area;
the energy consumption calculation module is configured to calculate the total time delay and the network energy consumption of the mobile edge cache network in each time period of the day according to the probability that each file is requested and in combination with the transmission energy consumption data when each file is transmitted after being requested; based on the network energy consumption, calculating the overall network energy consumption of each area on the same day by combining the transmission energy consumption of the files to be updated and replaced in each time period; the transmission energy consumption data are the energy consumption data after the mobile equipment requests, wherein the file exists on a cloud server or a central node or an edge node; the transmission energy consumption data comprises transmission time delay, transmission energy consumption and storage energy consumption;
the first caching policy module is configured to perform offline caching according to a preset first caching policy based on total time delay of the mobile edge caching network in each time period of the day or overall network energy consumption of each area in the day:
Acquiring a file requested by the minimum time delay in each time period as a file to be cached offline in each time period; or acquiring a file requested by each time period when the energy consumption of the whole network of each area is minimum in the current day, and taking the file as a file to be cached offline in each time period;
sequentially carrying out hierarchical caching on each edge node and the center node based on the files to be cached offline in each time period;
when in cache, the strategy for updating and replacing the file is as follows:
acquiring a time period corresponding to a file to be cached offline as a first time period;
if the first time period is in a preset off-peak time period, directly transmitting a file to be cached offline in the current time period from the cloud server to an edge node for caching;
if the first time period is in the preset peak time period, when the mobile equipment requests a file from a corresponding edge node and the edge node does not cache the requested file, judging whether the requested file is a file to be cached offline, and if so, caching the requested file from the cloud server to the corresponding edge node;
if the cache space of the edge node in the current area is full, judging whether the file cached offline in the previous time period of the first time period is not in the high popularity list of the first time period, and if so, directly replacing; if not, judging whether a central node connected with the edge node has a storage space, and if so, caching the central node to the central node in an offline manner; the high popularity list is a file list with the probability that the file is requested being greater than a set probability threshold;
If the caching spaces of the edge nodes and the center nodes in the current area are full, ordering the files cached in the center nodes and the edge nodes in descending order according to the probability that each file is requested, and then replacing the files to be cached offline in sequence from low to high;
the second cache policy module is configured to monitor the network energy consumption state of the mobile edge cache network in real time after offline cache; if the network energy consumption state is abnormal, acquiring current content request data of each mobile device in the mobile edge cache network, extracting hot content data, and caching according to a preset second cache strategy;
the method comprises the following steps of:
if the network energy consumption state is detected to be abnormal for the first time within the set time period, the extracted hot content data is cached to a central node of the mobile edge caching network: replacing the file with the smallest probability of being requested in the central node of the mobile edge cache network with the extracted hot content data;
otherwise, caching the extracted hot content data to an edge node of the mobile edge caching network: and replacing the extracted hot content data with the file with the smallest probability of being requested in the edge nodes of the mobile edge cache network.
8. An electronic device, comprising:
at least one processor; and a memory communicatively coupled to at least one of the processors;
wherein the memory stores instructions executable by the processor for execution by the processor to implement the network energy consumption hot spot detection based mobile edge cache resource optimization method of any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310133020.5A CN115866678B (en) | 2023-02-20 | 2023-02-20 | Mobile edge cache resource optimization method based on network energy consumption hot spot detection |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310133020.5A CN115866678B (en) | 2023-02-20 | 2023-02-20 | Mobile edge cache resource optimization method based on network energy consumption hot spot detection |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115866678A CN115866678A (en) | 2023-03-28 |
| CN115866678B true CN115866678B (en) | 2023-05-09 |
Family
ID=85658397
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310133020.5A Active CN115866678B (en) | 2023-02-20 | 2023-02-20 | Mobile edge cache resource optimization method based on network energy consumption hot spot detection |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115866678B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113115340A (en) * | 2021-04-14 | 2021-07-13 | 重庆邮电大学 | Popularity prediction-based cache optimization method in cellular network |
| CN115002113A (en) * | 2022-05-26 | 2022-09-02 | 南京邮电大学 | Mobile base station edge computing power resource scheduling method, system and electronic equipment |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090059823A1 (en) * | 2007-09-05 | 2009-03-05 | Bolduc Timothy D | System and method of edge caching when communicating data |
| CN106998353B (en) * | 2017-02-27 | 2020-07-31 | 上海大学 | Optimal caching configuration method for files in content-centric networking |
| CN110995872B (en) * | 2019-12-25 | 2020-07-17 | 中国传媒大学 | Method, system and device for calculating energy consumption of edge cache network |
| CN114328291A (en) * | 2021-12-18 | 2022-04-12 | 中国科学院深圳先进技术研究院 | Industrial Internet edge service cache decision method and system |
| CN114760311B (en) * | 2022-04-22 | 2023-09-05 | 南京邮电大学 | Optimized service caching and computing unloading method for mobile edge network system |
-
2023
- 2023-02-20 CN CN202310133020.5A patent/CN115866678B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113115340A (en) * | 2021-04-14 | 2021-07-13 | 重庆邮电大学 | Popularity prediction-based cache optimization method in cellular network |
| CN115002113A (en) * | 2022-05-26 | 2022-09-02 | 南京邮电大学 | Mobile base station edge computing power resource scheduling method, system and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115866678A (en) | 2023-03-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP3989443B2 (en) | Method for controlling a web farm and web farm | |
| CN107592345B (en) | Transaction current limiting device, method and transaction system | |
| Mehdipour et al. | FOG-Engine: Towards big data analytics in the fog | |
| CN111930511A (en) | Identifier resolution node load balancing device based on machine learning | |
| CN111459617A (en) | Containerized application automatic allocation optimization system and method based on cloud platform | |
| WO2018120802A1 (en) | Collaborative content cache control system and method | |
| US20060280161A1 (en) | System and method for autonomic system management through modulation of network controls | |
| Li et al. | Method of resource estimation based on QoS in edge computing | |
| US11856246B2 (en) | CDN optimization platform | |
| CN106452919A (en) | Fog node optimization method based on fussy theory | |
| CN115794407A (en) | Computing resource allocation method and device, electronic equipment and nonvolatile storage medium | |
| CN113918240A (en) | Task unloading method and device | |
| CN112148381A (en) | Software definition-based edge computing priority unloading decision method and system | |
| US9621438B2 (en) | Network traffic management | |
| JP2012141885A (en) | Cache system, cache arrangement method, cache control device and cache control program | |
| CN113159539A (en) | Joint green energy scheduling and dynamic task allocation method in multilayer edge computing system | |
| CN115866678B (en) | Mobile edge cache resource optimization method based on network energy consumption hot spot detection | |
| CN117076882A (en) | Dynamic prediction management method for cloud service resources | |
| CN116109058A (en) | Substation inspection management method and device based on deep reinforcement learning | |
| Aiswarya et al. | Latency Reduction in Medical IoT Using Fuzzy Systems by Enabling Optimized Fog Computing | |
| US10951690B2 (en) | Near real-time computation of scaling unit's load and availability state | |
| CN116684472A (en) | Service deployment system and service deployment method for terminal-side computing network | |
| CN114462506B (en) | Communication network auxiliary planning method supporting preference strategy and application thereof | |
| Liu et al. | Monitoring of Grid Performance Based-on Agent | |
| Liu et al. | ECC: Edge Collaborative Caching Strategy for Differentiated Services Load-Balancing. |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |