Profit-Maximized Task Offloading with Simulated-annealing-based Migrating Birds Optimization in Hybrid Cloud-Edge Systems

Document Type

Conference Proceeding

Publication Date

10-11-2020

Abstract

As an emerging framework, edge computing achieves Internet of Things by providing computing, storage and network resources. It moves computation to edge devices located near users. Nevertheless, nodes in the edge often own limited resources and constrained energy capacities. It is impossible to entirely execute tasks in the edge due to their unsatisfied quality of service. Cloud data centers (CDCs) own almost unlimited resources yet they might cause large transmission delay and high resource cost. Consequently, it is highly needed to intelligently offload tasks between CDC and edge. This work proposes a task offloading algorithm for hybrid cloud-edge systems to achieve profit maximization of a system provider with response time bound assurance. It comprehensively investigates CPU, memory and bandwidth limits of nodes in the edge, and constraints of available energy and servers in CDC. These factors are integrated into a single-objective constrained optimization problem, which is solved by a simulated-annealing-based migrating birds optimization algorithm to yield a close-to-optimal offloading policy between CDC and the edge. Real-life data-driven experimental results show that its profit outperforms its four typical peers.

Identifier

85098844416 (Scopus)

ISBN

[9781728185262]

Publication Title

Conference Proceedings IEEE International Conference on Systems Man and Cybernetics

External Full Text Location

https://doi.org/10.1109/SMC42975.2020.9283467

ISSN

1062922X

First Page

1218

Last Page

1223

Volume

2020-October

Grant

61703011

Fund Ref

National Natural Science Foundation of China

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