Energy Cost and Performance-Sensitive Bi-objective Scheduling of Tasks in Clouds

Document Type

Conference Proceeding

Publication Date

10-30-2020

Abstract

Cloud computing attracts a growing number of organizations to deploy their applications in distributed data centers for low latency and cost-effectiveness. The growth of arriving instructions makes it challenging to minimize their energy cost and improve Quality of Service (QoS) of applications by optimizing resource provisioning and instruction scheduling. This work formulates a bi-objective constrained optimization problem, and solves it with a Simulated-annealing-based Adaptive Differential Evolution (SADE) algorithm to jointly minimize both energy cost and instruction response time. The minimal Manhattan distance method is adopted to obtain a knee for good tradeoff between energy cost minimization and QoS maximization. Real-life data-based experiments demonstrate SADE achieves lower instruction response time, and smaller energy cost than several state-of-the-art peers.

Identifier

85096351366 (Scopus)

ISBN

[9781728168531]

Publication Title

2020 IEEE International Conference on Networking Sensing and Control Icnsc 2020

External Full Text Location

https://doi.org/10.1109/ICNSC48988.2020.9238080

Grant

61703011

Fund Ref

National Natural Science Foundation of China

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