CN114630183B - Edge equipment caching method and evaluation method based on scalable coding - Google Patents
Edge equipment caching method and evaluation method based on scalable coding Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000011156 evaluation Methods 0.000 title abstract description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000013461 design Methods 0.000 claims description 3
- 235000019633 pungent taste Nutrition 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 abstract description 4
- 230000005540 biological transmission Effects 0.000 description 4
- 238000001303 quality assessment method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 239000000523 sample Substances 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/47—End-user applications
- H04N21/472—End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification, for manipulating displayed content
- H04N21/47202—End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification, for manipulating displayed content for requesting content on demand, e.g. video on demand
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/44—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream, rendering scenes according to MPEG-4 scene graphs
- H04N21/44004—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream, rendering scenes according to MPEG-4 scene graphs involving video buffer management, e.g. video decoder buffer or video display buffer
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/44—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream, rendering scenes according to MPEG-4 scene graphs
- H04N21/4402—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream, rendering scenes according to MPEG-4 scene graphs involving reformatting operations of video signals for household redistribution, storage or real-time display
- H04N21/440281—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream, rendering scenes according to MPEG-4 scene graphs involving reformatting operations of video signals for household redistribution, storage or real-time display by altering the temporal resolution, e.g. by frame skipping
Abstract
The invention discloses an edge equipment caching method and an evaluation method based on scalable coding. The algorithm comprises the following steps: collecting video request information in an area received by the equipment on an edge equipment; the collected video request information is classified according to the request time, the video number and the code rate of the video; counting the request quantity of each code rate of each video of the past time slices including a specified time slice when the specified time slice is finished; calculating the cache cost performance of each video code rate according to the request quantity; and adjusting the video content cached by the device in the next time slice according to the size sequence of the cache price. The invention also provides a method for quantifying the magnitude scores provided by the edge device for caching content for the user in a time period. The algorithm and the evaluation method thereof utilize the advantage of SVC scalable coding on the cache, can effectively improve the cache efficiency of single equipment, and improve the capability of providing video services for users by utilizing the edge cache technology.
Description
Technical Field
The invention belongs to the technical field of computer networks, and particularly relates to an edge equipment caching method and an evaluation method based on scalable coding.
Background
With the increasing number of network video streaming services, video streaming services are becoming the hottest service on the internet. According to Cisco's predictions, by the end of 2021, the spent traffic of video streaming service would be up to 82% of all network traffic. However, efficient delivery of video content remains a challenging task due to network heterogeneity in resolution, frame rate, bit depth, etc. among users with different preferences and limitations.
Two important criteria for video on demand quality of service are video response latency and bandwidth occupation. The video response time delay directly influences the experience of the user when watching the video, and the shorter the video response time delay is, the more comfortable the user can obtain when watching the video, so that the server quality of the video on demand service is improved. The bandwidth occupied by the video influences network resources consumed in the video transmission, if the bandwidth occupied by the video is too much, network congestion can be caused when a large number of video-on-demand requests occur in one area, time delay and stability of video transmission are influenced, and viewing experience of all video-on-demand users in the area is reduced.
In order to provide high quality video on demand services, currently video providers always employ content delivery networks CDN to provide video on demand. The CDN adds a new network architecture in the existing network to deliver and transmit the content of the source station to the edge area closest to the user, so that the user can access the desired content nearby, and the response speed of the user access is improved. The basic principle of CDN is that the content needed by users is deployed to the place closest to the users by means of the cache servers placed in various places and the functional modules such as global scheduling and content distribution, so that time delay generated by physical distance when the users perform video-on-demand service is reduced, the originally inefficient and unreliable network is converted into an efficient and reliable intelligent network, the higher requirement of the users on the content access quality is met, the problem of congestion of the Internet network is solved, and the response speed of the users to access websites is improved. Furthermore, the performance of using video content caching devices at the network edge is comparable to that used at the network intermediate node.
However, with the popularization of emerging short video platforms and the increasing demand of users for video services, the video traffic in the internet has increased explosively, and it is increasingly difficult for conventional CDN technologies to meet the increasing demand of users for video services, and at this time, the concept of edge caching is proposed. The traditional network deploys the content (video, web page, etc.) in a data center or a regional CDN cache server, and has the problems of end-to-end delay time of the content acquisition, limited return bandwidth, low-efficiency redundant transmission, etc., and cannot meet the requirements of low delay of 5G and future applications, etc. The core idea of the mobile edge cache is to sink the content to a network access unit (such as a 5G base station, a roadbed unit and the like), realize one-hop nearby content service, remarkably reduce end-to-end delay and improve network transmission efficiency.
Disclosure of Invention
Because of the limited capacity of the edge cache, it is a problem how to make maximum use of the limited cache space to provide high quality video services to video on demand users.
In order to solve the problems, the invention discloses an edge device caching method based on scalable coding, SVC-BEC (SVC based on edge caching),
the method comprises the following steps:
s1, collecting video request information in an area received by an edge device;
s2, the collected video request information requests video numbers according to the request time, and the video code rate classification is requested;
s3, counting the number of requests of each video code rate of a plurality of time slices in the past including a specified time slice when the specified time slice is finished;
s4, calculating the cache cost performance of each video code rate according to the request quantity;
s5, adjusting the video content cached by the equipment in the next time slice according to the size sequence of the cache cost performance.
The present invention further preferably: the step S1 specifically includes the following steps:
s11, selecting an edge cache device which operates normally, and determining the size of a cache space of the edge cache device;
s12, counting video cache services which can be provided by the equipment, and designing a data structure for collecting request information;
s13, data acquisition is carried out in an actual environment, and the request time is recorded.
The present invention further preferably: in step S2, the method specifically includes the following steps:
s21, requesting video numbers and requesting video code rate classification according to the request time from the collected information;
s22, classifying the collected data into a data structure according to the previous design;
s23, storing the classified data information into the device.
The present invention further preferably: in step S3, the method specifically includes the following steps:
s31, designing the time slice length, and suspending the video request response when one time slice is finished;
s32, counting the number of requests of each video code rate of a plurality of time slices including the time slice in the past;
s33, classifying the data after statistics according to the video type and the code rate.
The present invention further preferably: in step S4, the method specifically includes the following steps:
s41, considering the number of video requests to represent video heat according to the time limitation;
s42, extracting the request quantity of past m time slices of each code rate of each video in the file and summing;
s43, dividing the heat by the space occupied by the cached video to obtain the cache cost performance of the video in the past m time slices;
s45, storing the cache cost performance of each code rate of each video into a file.
The present invention further preferably: the step S41 specifically comprises the following steps:
s411, calling a file storing video request information;
s412, checking whether each video has received a request from a user within the past m time periods;
s43, if a code rate of a video is requested in the past m time slices, the heat is counted.
The present invention further preferably: the step S5 specifically includes the following steps:
s51, ordering the cache cost performance of all videos requested in the past from high to low;
s52, starting to store the video with highest cost performance into the current edge cache device;
s53, in the process of embedding, if the current video i is found to be already embedded into the device, if the code rate of the already-stored video i is higher than the code rate of the current processing, skipping the version of the current code rate, and if the already-stored code rate is lower than the code rate of the currently-processed video i, covering the corresponding code rate of the currently-processed video i with the previously-stored video i;
s54, repeating the process until the space occupied by the video stored by the device D reaches the upper storage limit.
The invention discloses a scalable coding-based edge equipment cache quality assessment method, which specifically comprises the following steps:
s6, because the peak signal-to-noise ratio PSNR is in direct proportion to the video code rate, the logarithm of the video code rate is used for representing the peak signal-to-noise ratio and representing the experience score obtained by the user from the video;
s7, calculating the sum of user experience scores provided by all videos cached in the device in a past time period;
and S8, dividing the sum by the number of user requests in the past time period to normalize, and obtaining the user experience score S of the caching scheme of the equipment in the past time period.
The present invention further preferably: the step S7 specifically includes the following steps:
s71, for a video code rate, multiplying the user request number by the PSNR value, namely the logarithm of the code rate, to obtain a video score S;
s72, counting the sum of scores S of all kinds of video code rates;
s73, counting the sum S of scores of videos requested in a past time slice.
The present invention further preferably: in step S8, if S is larger, it is indicated that the quality of video service provided by the device to the user is higher in a past time slice; if S is smaller, the lower the quality of video service provided by the device to the user over the past time period is generally indicated
The method has the beneficial effects that aiming at video on demand service, the cache quality of the edge equipment can be effectively improved based on the time limitation principle by utilizing the advantages of the scalable coding in the aspect of storage, and the evaluation can be carried out through user scoring.
Drawings
FIG. 1 schematically shows a flow chart of a statistical method provided by an embodiment of the invention;
FIG. 2 schematically illustrates a video request information collection structure provided by an embodiment of the present invention;
fig. 3 schematically shows a flowchart of cache content replacement provided by an embodiment of the present invention.
Detailed Description
The present invention is further illustrated in the following drawings and detailed description, which are to be understood as being merely illustrative of the invention and not limiting the scope of the invention. It should be noted that the words "front", "rear", "left", "right", "upper" and "lower" used in the following description refer to directions in the drawings, and the words "inner" and "outer" refer to directions toward or away from, respectively, the geometric center of a particular component.
As shown in fig. 1, an embodiment of the present invention provides an edge device caching method based on scalable coding, including:
s1, collecting video request information in an area received by an edge device. Fig. 2 schematically shows an information acquisition structure, and referring to fig. 2, the following is a specific description.
The specific implementation content is as follows:
firstly, selecting edge equipment, recording the cache space D of the edge equipment, deploying a probe on the edge equipment to acquire user request information, acquiring by using multiple network cards on hardware, and shunting original request flow to each network card by a switch through a load balancing technology so as to meet the information acquisition requirement.
Then, a double buffer mechanism is applied to the buffer of the network card, and the effective sequence of the buffer is controlled through a time slice, so that the network card can be ensured to be in a usable state all the time. When a certain network card buffer is full, the working thread can transmit the buffer address to the re-entrant data collection interface.
Finally, in order to collect the request data of all network cards in parallel, a multithreading technology is adopted. A DPDK suite is deployed on an acquisition server, and the rapid data packet processing function of the DPDK suite can be used for information acquisition after adjustment; the acquisition server transmits the video request information of the user to the storage area.
S2, the collected video request information is requested for video numbers according to the request time, and the video code rate is requested for classification;
the specific implementation content is as follows: extracting the collected video request information from the storage area; the collected information is requested for video numbers according to the request time, and the video code rate classification is requested; counting the total number of times of requesting video contents per time slice; the collected data is sorted into data structures according to previous designs.
And S3, counting the heat of each code rate of each video of the past several time slices including the time slice when one specified time slice is finished. The specific implementation steps are as follows:
first, for a code rate j of a video i, the number C of times it is requested by the user in the past m time slices is extracted ij 。
Then, in order to eliminate the distinction between the time slices with the large number of requests and the time slices with the small number of requests, the number of requests of the extracted specific video rate is divided by the total number of requests Rt of each corresponding time slice, thereby performing normalization.
Finally, the normalized m data are added to obtain the heat of the video code rate in the past m time slices, namely
The above operations are repeated for all video rates that have been requested during the past m time slices.
S4, calculating the cache cost performance of each video code rate according to the request quantity;
because of the time limitation principle, the video with high heat in the past is also high in future, namely more user requests can be received, but because the equipment cache space is limited, if the video with overlarge cache can reduce the total number of the videos which can be cached, the overall request hit rate of the equipment is reduced, so that the cache cost performance of each video code rate is calculated for improving the overall cache quality of the cache equipment, and the cache video is selected based on the cost performance.
For the past m time slices, locating all video rate heat information files within the time period; reading a file, and extracting heat values C of all video code rates in the file ij ' dividing the hotness value by the code rate v of the video version ij Representing the size of the space occupied by the video, thereby obtaining the cache cost performance W of the video version ij 。
S5, adjusting the video content cached by the equipment in the next time slice according to the size sequence of the cache price.
For all code rate versions of all videos, W will be ij Ordering from big to small, storing in edge device from first bit, during the storing process, if the current video i is found to have been stored in device, if the code rate of the stored video i is higher than the code rate of the current processing, skipping the current code rate version, if the code rate is lower than the code rate of the current processing, then covering the previously stored video i with the corresponding code rate of the current processing video i, repeating this process until the space occupied by the video stored in device D reaches the upper storage limit M, the specific example of the process is shown in FIG. 3
The invention also provides a cache quality assessment method, which comprises the following steps:
s6, because the peak signal-to-noise ratio PSNR is in direct proportion to the video code rate, the logarithm of the video code rate is used for representing the peak signal-to-noise ratio and representing the experience score obtained by the user from the video, namely S is considered ij ＝PSNR ij ＝lgv ij Representing experience scores obtained by a user from a hit of a code rate j version of a request video i;
and S7, calculating the sum of user experience scores provided by all videos cached in the device in the past time period. For a video code rate, multiplying the user request number by the PSNR value, namely the logarithm of the code rate, to obtain a video score s; counting the sum of scores s of all kinds of video code rates; counting the sum S of scores of videos requested in a past time slice;
s8, dividing the sum by the number of user requests in the past time period to normalize, namelyThereby obtaining a caching scheme user experience score S for the device over a period of time. If S is larger, the higher the quality of video service provided by the device for the user in the past time slice is indicated; if S is smaller, the quality of video service provided by the device to the user over the past time period is generally indicated as lower.
The technical means disclosed by the scheme of the invention is not limited to the technical means disclosed by the embodiment, and also comprises the technical scheme formed by any combination of the technical features.
Claims (7)
1. A scalable coding-based edge equipment caching method is characterized by comprising the following steps of: the method comprises the following steps:
s1, collecting video request information in an area received by an edge device;
s2, the collected video request information requests video numbers according to the request time, and the video code rate classification is requested;
s3, counting the number of requests of each video code rate of a plurality of time slices in the past including a specified time slice when the specified time slice is finished; the specific implementation steps are as follows:
first, for a code rate j of a video i, the number C of times it is requested by the user in the past m time slices is extracted ij ；
Then, in order to eliminate the distinction between the time slices with more request times and the time slices with less request times, dividing the extracted request times of the specific video code rate by the corresponding total request times Rt of each time slice, thereby carrying out standardization;
finally, the normalized m data are added to obtain the heat of the video code rate in the past m time slices, namelyRepeating the implementation steps on all video code rates requested in the past m time slices;
s4, calculating the cache cost performance of each video code rate according to the request quantity; the method specifically comprises the following steps:
s41, considering the number of video requests to represent video heat according to the time limitation; the method comprises the following steps:
s411, calling a file storing video request information;
s412, checking whether each video has received a request from a user within the past m time periods;
s413, if a code rate of a video is requested in the past m time slices, counting the heat of the video;
s42, extracting the request quantity of past m time slices of each code rate of each video in the file and summing;
s43, dividing the heat by the space occupied by the cached video to obtain the cache cost performance of the video in the past m time slices;
s45, storing the cache cost performance of each code rate of each video into a file;
wherein for the past m time slices, all video rate heat information files within the time period are located; reading a file, and extracting heat values C of all video code rates in the file ij ' dividing the hotness value by the code rate v of the video version ij Representing the size of the space occupied by the video, thereby obtaining the cache cost performance W of the video version ij ；
S5, adjusting the video content cached by the equipment in the next time slice according to the size sequence of the cache cost performance; the method specifically comprises the following steps:
s51, ordering the cache cost performance of all videos requested in the past from high to low;
s52, starting to store the video with highest cost performance into the current edge cache device;
s53, in the process of embedding, if the current video i is found to be already embedded into the device, if the code rate of the already-stored video i is higher than the code rate of the current processing, skipping the version of the current code rate, and if the already-stored code rate is lower than the code rate of the currently-processed video i, covering the corresponding code rate of the currently-processed video i with the previously-stored video i;
s54, repeating the process until the space occupied by the video stored by the device D reaches the upper storage limit.
2. The scalable coding-based edge device caching method according to claim 1, wherein:
the step S1 specifically includes the following steps:
s11, selecting an edge cache device which operates normally, and determining the size of a cache space of the edge cache device;
s12, counting video cache services which can be provided by the equipment, and designing a data structure for collecting request information;
s13, data acquisition is carried out in an actual environment, and the request time is recorded.
3. The scalable coding-based edge device caching method according to claim 2, wherein: in step S2, the method specifically includes the following steps:
s21, requesting video numbers and requesting video code rate classification according to the request time from the collected information;
s22, classifying the collected data into a data structure according to the previous design;
s23, storing the classified data information into the device.
4. The scalable coding-based edge device caching method according to claim 1, wherein: in step S3, the method specifically includes the following steps:
s31, designing the time slice length, and suspending the video request response when one time slice is finished;
s32, counting the number of requests of each video code rate of a plurality of time slices including the time slice in the past;
s33, classifying the data after statistics according to the video type and the code rate.
5. The method for evaluating the cache quality of the edge device based on the scalable coding according to claim 1, wherein the method comprises the following steps: the method specifically comprises the following steps:
s6, because the peak signal-to-noise ratio PSNR is in direct proportion to the video code rate, the logarithm of the video code rate is used for representing the peak signal-to-noise ratio and representing the experience score obtained by the user from the video;
s7, calculating the sum of user experience scores provided by all videos cached in the device in a past time period;
and S8, dividing the sum by the number of user requests in the past time period to normalize, and obtaining the user experience score S of the caching scheme of the equipment in the past time period.
6. The method for evaluating the cache of the edge device based on the scalable coding according to claim 5, wherein the method comprises the following steps: the step S7 specifically includes the following steps:
s71, for a video code rate, multiplying the user request number by the PSNR value, namely the logarithm of the code rate, to obtain a video score S;
s72, counting the sum of scores S of all kinds of video code rates;
s73, counting the sum S of scores of videos requested in a past time slice.
7. The method for evaluating the cache of the edge device based on the scalable coding according to claim 5, wherein the method comprises the following steps: in step S8, if S is larger, it is indicated that the quality of video service provided by the device to the user is higher in a past time slice; if S is smaller, the quality of video service provided by the device to the user over the past time period is generally indicated as lower.
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