CN111866111A - Edge deployment method for sensing user demand growth trend - Google Patents

Edge deployment method for sensing user demand growth trend Download PDF

Info

Publication number
CN111866111A
CN111866111A CN202010668788.9A CN202010668788A CN111866111A CN 111866111 A CN111866111 A CN 111866111A CN 202010668788 A CN202010668788 A CN 202010668788A CN 111866111 A CN111866111 A CN 111866111A
Authority
CN
China
Prior art keywords
user
edge
time
task
time period
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.)
Granted
Application number
CN202010668788.9A
Other languages
Chinese (zh)
Other versions
CN111866111B (en
Inventor
秦肖雲
王博
曹洁
崔霄
王昌海
张志锋
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhengzhou University of Light Industry
Original Assignee
Zhengzhou University of Light Industry
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Zhengzhou University of Light Industry filed Critical Zhengzhou University of Light Industry
Priority to CN202010668788.9A priority Critical patent/CN111866111B/en
Publication of CN111866111A publication Critical patent/CN111866111A/en
Application granted granted Critical
Publication of CN111866111B publication Critical patent/CN111866111B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network arrangements or protocols for supporting network services or applications
    • H04L67/01Protocols
    • H04L67/10Protocols in which an application is distributed across nodes in the network
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08Configuration management of networks or network elements
    • H04L41/0893Assignment of logical groups to network elements

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention relates to the technical field of information technology, in particular to an edge deployment method for perceiving the growth trend of user demand, which provides an edge deployment scheme meeting the growth demand of users for service providers; the method is characterized by comprising the following steps: step 1, dividing historical operation time of an edge computing center into m time periods; step 2, calculating the edge resource amount required by the user in each time period divided in the step 1; and 3, calculating the resource quantity required by the user in the central historical operation time period according to the m edges obtained in the step 2 by using a time sequence analysis method, and predicting the edge resource quantity required by the user in a future time period.

Description

Edge deployment method for sensing user demand growth trend
Technical Field
The invention relates to the technical field of information technology, in particular to an edge deployment method for sensing the increase trend of user demand.
Background
Many smart devices (e.g., smartphones) are unable to efficiently perform user tasks due to their limited resource capacity and battery power. Some service providers then employ edge computing techniques to reduce the computing latency of tasks and reduce the computing power consumption of devices by offloading some of the user tasks of smart devices (especially mobile devices) to nearby edge computing centers. However, due to the limitations of the site space and the cooling conditions, only limited server resources can be deployed by one edge computing center, and how to efficiently use edge resources is one of the problems that needs to be solved, where designing an efficient edge deployment scheme is one of the effective methods for solving the problem.
In the edge computing environment, the edge deployment scheme reasonably deploys corresponding edge resources for each computing center, so that the idle edge resource amount during operation is optimized, and the user requirements during edge operation are met. Some works have designed effective edge deployment schemes by using historical service information of operation, however, all of the works only adopt the existing information and do not consider the future change trend of user requirements.
With the development of information technology and the increasing demand of people for nice life, smart devices, such as smart phones, wearable devices and smart homes, are becoming more popular. And with the rapid development of artificial intelligence algorithms (such as deep learning) and communication technologies (such as 5G, WiFi6/7) in recent years, the internet services facing intelligent devices are rapidly increased in diversity and complexity, which results in that the existing edge server deployment method is likely to design a scheme which cannot meet the requirements of users during operation.
Disclosure of Invention
In order to solve the technical problem, the invention provides an edge deployment method for providing an edge deployment scheme meeting the user growth requirement for a service provider, and the edge deployment method is used for sensing the user requirement growth trend.
The invention discloses an edge deployment method for sensing the increase trend of user demand, which comprises the following steps:
step 1, dividing historical operation time of an edge computing center into m time periods;
step 2, calculating the edge resource amount required by the user in each time period divided in the step 1;
and 3, calculating the resource quantity required by the user in the central historical operation time period according to the m edges obtained in the step 2 by using a time sequence analysis method, and predicting the edge resource quantity required by the user in a future time period.
The invention discloses an edge deployment method for sensing the increase trend of user demand, which further comprises the following steps:
recording the data acquisition time of the edge computing center during historical operation as { tauj1.. T }, wherein τ is when 1 ≦ j1 < j2 ≦ Tj1<τj2(ii) a The collected information comprises task information requested by a user to the edge computing center, and the set of tasks requested by the user is recorded as { taski1., n }, and siAnd fiTask respectivelyiThe start time and the end time of (2), note riIs taskiThe amount of resources consumed during execution; the kth time period comprises the time of acquiring the execution information of the user task { tauj|j=τ(k-1)·Δ,...,τk·ΔIn which τ isjRepresenting the jth information acquisition time;
Figure BDA0002581447830000022
Figure BDA0002581447830000023
is a rounded-down symbol; t represents the number of information collection times during the historical operation of the edge computing center.
The invention discloses an edge deployment method for sensing the increase trend of user demand, which further comprises the following steps:
in step 2, the calculation formula for calculating the amount of the edge resources required by the user in each time period divided in step 1 is as follows:
Figure BDA0002581447830000021
wherein R iskRepresenting the amount of edge resources required by the user in the kth time period; r isiIs taskiThe amount of resources consumed during execution; taskiRepresenting the ith task requested by a user during the operation of the edge computing center; z is a radical ofi,jRepresenting taskiWhether to run at jth data acquisition time: when s isi≤τj≤fiWhen z isi,j1 is ═ 1; in other cases zi,j=0,siAnd fiTask respectivelyiThe start time and the end time of (c); tau isjAnd showing the collection time of the j-th user task execution information.
The invention discloses an edge deployment method for sensing the increase trend of user demand, which further comprises the following steps:
step 3 is used for predicting the edge resource amount required by the user in a future time period, and the calculation formula is as follows:
Figure BDA0002581447830000031
wherein R ism+1Representing the amount of edge resources required by the user for a period of time in the future; rkRepresenting the edge resource amount required by the user in the kth time period, and calculating the edge resource amount in the step 2; and the self-correlation offset factor set for the user meets the condition that m-1 is more than or equal to 1.
Compared with the prior art, the invention has the beneficial effects that: the invention uses the task information of the edge computing center during the historical operation to compute the edge resource amount needed by the user in each time period, and uses the time sequence prediction method to obtain the change trend of the edge resource amount needed by the user, and predicts the edge resource amount needed by the user in a future time period, thereby providing an edge deployment scheme which accords with the change trend of the user requirement for the service provider.
Drawings
FIG. 1 is a logic flow diagram of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Examples
Recording the data acquisition time of the edge computing center during historical operation as { tauj1.. T }, wherein τ is when 1 ≦ j1 < j2 ≦ Tj1<τj2. The collected information comprises task information requested by a user to the edge computing center, and the set of tasks requested by the user is recorded as { taski1., n }, and siAnd fiTask respectivelyiThe start time and the end time of (2), note riIs taskiThe amount of resources consumed during execution.
As shown in fig. 1, a specific flow of an edge deployment method for sensing a user demand growth trend is as follows:
step 1: dividing the historical operation time of the edge computing center into m time periods, wherein the kth time period comprises the user task execution information acquisition time { tauj|j=τ(k-1)·Δ,...,τk·ΔIn which τ isjRepresenting the jth information acquisition time;
Figure BDA0002581447830000041
Figure BDA0002581447830000042
is a rounded-down symbol; t represents the number of information collection times during the historical operation of the edge computing center.
Step 2: calculating the edge resource amount required by the user in each time period divided in the step 1, wherein the calculation formula is as follows:
Figure BDA0002581447830000043
Wherein R iskRepresenting the amount of edge resources required by the user in the kth time period; r isiIs taskiThe amount of resources consumed during execution; taskiRepresenting the ith task requested by a user during the operation of the edge computing center; z is a radical ofi,jRepresenting taskiWhether to run at jth data acquisition time: when s isi≤τj≤fiWhen z isi,j1, otherwise zi,j=0;siAnd fiTask respectivelyiThe start time and the end time of (c); tau isjAnd showing the collection time of the j-th user task execution information.
And step 3: and (3) calculating the resource quantity required by the user in the historical operation time period of the center according to the m edges obtained in the step (2) by using a time series analysis method, predicting the edge resource quantity required by the user in a future time period, wherein the calculation formula is as follows:
Figure BDA0002581447830000044
wherein R ism+1Representing the amount of edge resources required by the user for a period of time in the future; rkRepresenting the edge resource amount required by the user in the kth time period, and calculating the edge resource amount in the step 2; and the self-correlation offset factor set for the user meets the condition that m-1 is more than or equal to 1.
The invention uses the task information of the edge computing center during the historical operation to compute the edge resource amount needed by the user in each time period, and uses the time sequence prediction method to obtain the change trend of the edge resource amount needed by the user, and predicts the edge resource amount needed by the user in a future time period, thereby providing an edge deployment scheme which accords with the change trend of the user requirement for the service provider.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (4)

1. An edge deployment method for sensing the increase trend of user demand is characterized by comprising the following steps:
step 1, dividing historical operation time of an edge computing center into m time periods;
step 2, calculating the edge resource amount required by the user in each time period divided in the step 1;
and 3, calculating the resource quantity required by the user in the central historical operation time period according to the m edges obtained in the step 2 by using a time sequence analysis method, and predicting the edge resource quantity required by the user in a future time period.
2. The edge deployment method for sensing the increasing trend of user demand as claimed in claim 1, wherein the data collection time during the historical operation of the edge computing center is recorded as { τ }j1.. T }, wherein τ is when 1 ≦ j1 < j2 ≦ Tj1<τj2(ii) a The collected information comprises task information requested by a user to the edge computing center, and the set of tasks requested by the user is recorded as { task i1., n }, and siAnd fiTask respectivelyiThe start time and the end time of (2), note riIs taskiThe amount of resources consumed during execution; the kth time period comprises the time of acquiring the execution information of the user task { tauj|j=τ(k-1)·Δ,...,τk·ΔIn which τ isjRepresenting the jth information acquisition time;
Figure FDA0002581447820000011
Figure FDA0002581447820000012
is a rounded-down symbol; t represents the number of information collection times during the historical operation of the edge computing center.
3. The edge deployment method for sensing the increasing trend of the user demand as claimed in claim 2, wherein the calculation formula for calculating the amount of the edge resources required by the user in each time segment divided in step 1 in step 2 is as follows:
Figure FDA0002581447820000013
wherein R iskRepresenting the amount of edge resources required by the user in the kth time period; r isiIs taskiThe amount of resources consumed during execution;taskirepresenting the ith task requested by a user during the operation of the edge computing center; z is a radical ofi,jRepresenting taskiWhether to run at jth data acquisition time: when s isi≤τj≤fiWhen z isi,j1, otherwise zi,j=0;siAnd fiTask respectivelyiThe start time and the end time of (c); tau isjAnd showing the collection time of the j-th user task execution information.
4. The edge deployment method for sensing the increasing trend of the user demand as claimed in claim 3, wherein the step 3 is used for predicting the amount of the edge resources required by the user in a future time period, and the calculation formula is as follows:
Figure FDA0002581447820000021
Wherein R ism+1Representing the amount of edge resources required by the user for a period of time in the future; rkRepresenting the edge resource amount required by the user in the kth time period, and calculating the edge resource amount in the step 2; and the self-correlation offset factor set for the user meets the condition that m-1 is more than or equal to 1.
CN202010668788.9A 2020-07-13 2020-07-13 Edge deployment method for sensing user demand growth trend Active CN111866111B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010668788.9A CN111866111B (en) 2020-07-13 2020-07-13 Edge deployment method for sensing user demand growth trend

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010668788.9A CN111866111B (en) 2020-07-13 2020-07-13 Edge deployment method for sensing user demand growth trend

Publications (2)

Publication Number Publication Date
CN111866111A true CN111866111A (en) 2020-10-30
CN111866111B CN111866111B (en) 2021-03-30

Family

ID=72983843

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010668788.9A Active CN111866111B (en) 2020-07-13 2020-07-13 Edge deployment method for sensing user demand growth trend

Country Status (1)

Country Link
CN (1) CN111866111B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114816690A (en) * 2021-01-29 2022-07-29 中移(苏州)软件技术有限公司 Task allocation method, device, equipment and storage medium

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090209349A1 (en) * 2008-02-19 2009-08-20 Microsoft Corporation Prediction of network path quality among peer networking devices
CN104932898A (en) * 2015-06-30 2015-09-23 东北大学 Method for selecting to-be-increased components based on improved multi-target particle swam optimization algorithm
CN105491079A (en) * 2014-09-16 2016-04-13 华为技术有限公司 Method and device for adjusting resources needed by application in cloud computing environment
CN106375115A (en) * 2016-08-30 2017-02-01 东软集团股份有限公司 Resource distribution method and device
US20190339688A1 (en) * 2016-05-09 2019-11-07 Strong Force Iot Portfolio 2016, Llc Methods and systems for data collection, learning, and streaming of machine signals for analytics and maintenance using the industrial internet of things
CN110602723A (en) * 2019-08-27 2019-12-20 华侨大学 Two-stage bidirectional prediction data acquisition method based on underwater edge equipment
CN111124689A (en) * 2019-12-31 2020-05-08 中国电子科技集团公司信息科学研究院 Dynamic allocation method for container resources in cluster
CN111178555A (en) * 2019-12-24 2020-05-19 重庆特斯联智慧科技股份有限公司 Community security equipment repair reporting method and system based on edge calculation
CN111224806A (en) * 2018-11-27 2020-06-02 华为技术有限公司 Resource allocation method and server

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090209349A1 (en) * 2008-02-19 2009-08-20 Microsoft Corporation Prediction of network path quality among peer networking devices
CN105491079A (en) * 2014-09-16 2016-04-13 华为技术有限公司 Method and device for adjusting resources needed by application in cloud computing environment
CN104932898A (en) * 2015-06-30 2015-09-23 东北大学 Method for selecting to-be-increased components based on improved multi-target particle swam optimization algorithm
US20190339688A1 (en) * 2016-05-09 2019-11-07 Strong Force Iot Portfolio 2016, Llc Methods and systems for data collection, learning, and streaming of machine signals for analytics and maintenance using the industrial internet of things
CN106375115A (en) * 2016-08-30 2017-02-01 东软集团股份有限公司 Resource distribution method and device
CN111224806A (en) * 2018-11-27 2020-06-02 华为技术有限公司 Resource allocation method and server
CN110602723A (en) * 2019-08-27 2019-12-20 华侨大学 Two-stage bidirectional prediction data acquisition method based on underwater edge equipment
CN111178555A (en) * 2019-12-24 2020-05-19 重庆特斯联智慧科技股份有限公司 Community security equipment repair reporting method and system based on edge calculation
CN111124689A (en) * 2019-12-31 2020-05-08 中国电子科技集团公司信息科学研究院 Dynamic allocation method for container resources in cluster

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
JUN-BO WANG ET AL: ""Joint Optimization of Offloading and Resources Allocation in Secure Mobile Edge Computing Systems"", 《 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY》 *
田瑞云: ""基于Hadoop的CDN-P2P系统中内容预测机制研究与实现"", 《中国优秀硕士学位论文全文数据库(电子期刊)》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114816690A (en) * 2021-01-29 2022-07-29 中移(苏州)软件技术有限公司 Task allocation method, device, equipment and storage medium

Also Published As

Publication number Publication date
CN111866111B (en) 2021-03-30

Similar Documents

Publication Publication Date Title
US11551154B1 (en) Predictive power management in a wireless sensor network
US11204591B2 (en) Modeling and calculating normalized aggregate power of renewable energy source stations
CN108804227B (en) Method for computing-intensive task unloading and optimal resource allocation based on mobile cloud computing
US8005654B2 (en) Method, apparatus and computer program product for intelligent workload control of distributed storage
CN109478144A (en) A kind of data processing equipment and method
Kumar Efficient resource allocation and QoS enhancements of IoT with fog network
CN103338461B (en) Based on network plan method and the device of Traffic prediction
Xu et al. Accelerating Semi-Asynchronous Federated Learning
CN106500341A (en) A kind of control method of intelligent water heater and system
CN112383931B (en) Method for optimizing cost and time delay in multi-user mobile edge computing system
CN111866111B (en) Edge deployment method for sensing user demand growth trend
CN113286317A (en) Task scheduling method based on wireless energy supply edge network
CN110658725A (en) Energy supervision and prediction system and method based on artificial intelligence
CN109255000A (en) A kind of the dimension management method and device of label data
CN104767833A (en) Cloud transferring method for computing tasks of mobile terminal
Wang et al. Cloud workload analytics for real-time prediction of user request patterns
Dahihande et al. Reducing energy waste in households through real-time recommendations
CN115185650A (en) Task scheduling method for heterogeneous edge computational power network
US11853936B2 (en) Minimizing the environmental impact of workloads
CN113806094A (en) Cloud platform resource dynamic scheduling method based on deep learning
Rathnayake et al. Predicting network availability using user context
CN111162852B (en) Ubiquitous power Internet of things access method based on matching learning
CN112306406A (en) Intelligent storage automatic grading method and device, storage medium and electronic equipment
CN113300375A (en) Temperature control load demand response control method considering user participation desire difference
CN111796942B (en) Edge deployment method for sensing cloud resource cost expenditure

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