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

Bo Wang and Mingchu Li
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Bo Wang: School of Software Technology, Dalian University of Technology, Dalian, China & School of Applied Technology, University of Science and Technology Liaoning, Anshan, China & Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, China
Mingchu Li: School of Software Technology, Dalian University of Technology, Dalian, China & Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, China

International Journal of Web Services Research (IJWSR), 2021, vol. 18, issue 2, 1-24

Abstract: 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.

Date: 2021
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