Spatio-temporal scheduling of heterogeneous delay-constrained tasks in geo-distributed green clouds

Document Type

Conference Proceeding

Publication Date

5-1-2019

Abstract

The skyrocketing growth in types and number of heterogeneous applications dramatically increases the amount of energy consumed by distributed green data centers (DGDCs). The spatial and temporal variations in prices of power grid and availability of renewable energy make it highly challenging to minimize the energy cost by intelligently scheduling arriving tasks of heterogeneous applications among green data centers while meeting their expected delay bound constraints. Unlike existing studies, this work proposes a Spatio-Temporal Task Scheduling (STTS) algorithm to minimize the energy cost by cost-effectively scheduling all arriving tasks to meet their delay bound constraints. It well uses spatial and temporal variations to achieve DGDC cost reduction and throughput improvement. In each time slot, the energy cost minimization problem for DGDC providers is formulated as a nonlinear constrained optimization one, and addressed with the proposed Genetic Simulated-annealing-based Particle swarm optimization. Trace-driven experiments show that STTS achieves larger throughput and lower energy cost than several typical task scheduling approaches while strictly meeting all tasks' delay bound constraints.

Identifier

85068742484 (Scopus)

ISBN

[9781728100838]

Publication Title

Proceedings of the 2019 IEEE 16th International Conference on Networking Sensing and Control Icnsc 2019

External Full Text Location

https://doi.org/10.1109/ICNSC.2019.8743294

First Page

287

Last Page

292

Grant

41401020401

Fund Ref

National Natural Science Foundation of China

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