Spatial Task Scheduling for Cost Minimization in Distributed Green Cloud Data Centers

Document Type

Article

Publication Date

4-1-2019

Abstract

The infrastructure resources in distributed green cloud data centers (DGCDCs) are shared by multiple heterogeneous applications to provide flexible services to global users in a high-performance and low-cost way. It is highly challenging to minimize the total cost of a DGCDC provider in a market, where bandwidth prices of Internet service providers (ISPs), electricity prices, and the availability of renewable green energy all vary with geographical locations. Unlike existing studies, this paper proposes a spatial task scheduling and resource optimization (STSRO) method to minimize the total cost of their provider by cost-effectively scheduling all arriving tasks of heterogeneous applications to meet tasks' delay-bound constraints. STSRO well exploits spatial diversity in DGCDCs. In each time slot, the cost minimization problem for DGCDCs is formulated as a constrained optimization one and solved by the proposed simulated annealing-based bat algorithm (SBA). Trace-driven experiments demonstrate that STSRO achieves lower total cost and higher throughput than two typical scheduling methods. Note to Practitioners - This paper investigates the cost minimization problem for DGCDCs while meeting delay-bound constraints for all arriving tasks. Previous task scheduling methods do not jointly investigate the spatial diversity in bandwidth prices of ISPs, electricity prices, and the availability of renewable green energy. Therefore, they fail to cost-effectively schedule all arriving tasks of heterogeneous applications during their delay-bound constraints. In this paper, a new method that overcomes the shortcomings of the existing methods is proposed. It is obtained by using the proposed SBA that solves a constrained optimization problem. Simulation results demonstrate that compared with two typical scheduling methods, it increases the throughput and decreases the cost. It can be readily implemented and integrated into real-world industrial DGCDCs. The future work needs to investigate the indeterminacy of renewable energy and the uncertainty in arriving tasks with rough deep neural network approaches on STSRO.

Identifier

85059111353 (Scopus)

Publication Title

IEEE Transactions on Automation Science and Engineering

External Full Text Location

https://doi.org/10.1109/TASE.2018.2857206

ISSN

15455955

First Page

729

Last Page

740

Issue

2

Volume

16

Grant

61703011

Fund Ref

National Natural Science Foundation of China

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