Resource Allocation Scheduling Algorithm Based on Incomplete Information Dynamic Game for Edge Computing

With the advent of the 5G era, the demands for features such as low latency and high concurrency are becoming increasingly significant. These sophisticated new network applications and services require huge gaps in network transmission bandwidth, network transmission latency, and user experience, ma...

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Vydáno v:International journal of web services research Ročník 18; číslo 2; s. 1 - 24
Hlavní autoři: Wang, Bo, Li, Mingchu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hershey IGI Global 01.04.2021
Témata:
Algorithms
Analysis
Auctions
Client satisfaction
Cloud computing
Computation offloading
Computer centers
Data
Delay
Edge computing
End users
Energy consumption
Games
Latency
Markov analysis
Markov chains
Markov processes
Mobility
Model accuracy
Network latency
Networks
Prediction models
Prices
Resource allocation
Resource scheduling
Satisfaction
Storage
Time lag
User experience
User satisfaction
China
ISSN:1545-7362, 1546-5004
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Shrnutí:With the advent of the 5G era, the demands for features such as low latency and high concurrency are becoming increasingly significant. These sophisticated new network applications and services require huge gaps in network transmission bandwidth, network transmission latency, and user experience, making cloud computing face many technical challenges in terms of applicability. In response to cloud computing's shortcomings, edge computing has come into its own. However, many factors affect task offloading and resource allocation in the edge computing environment, such as the task offload latency, energy consumption, smart device mobility, end-user power, and other issues. This paper proposes a dynamic multi-winner game model based on incomplete information to solve multi-end users' task offloading and edge resource allocation. First, based on the history of end-users storage in edge data centers, a hidden Markov model can predict other end-users' bid prices at time t. Based on these predicted auction prices, the model determines their bids. A dynamic multi-winner game model is used to solve the offload strategy that minimizes latency, energy consumption, cost, and to maximizes end-user satisfaction at the edge data center. Finally, the authors designed a resource allocation algorithm based on different priorities and task types to implement resource allocation in edge data centers. To ensure the prediction model's accuracy, the authors also use the expectation-maximization algorithm to learn the model parameters. Comparative experimental results show that the proposed model can better results in time delay, energy consumption, and cost.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1545-7362
1546-5004
DOI:10.4018/IJWSR.2021040101