Geography-Aware Task Scheduling for Profit Maximization in Distributed Green Data Centers

作     者：Yuan, Haitao Bi, Jing Zhou, MengChu 

作者机构：New Jersey Inst Technol Dept Elect & Comp Sci Newark NJ 07102 USA Beijing Univ Technol Sch Software Engn Fac Informat Technol Beijing 100124 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON CLOUD COMPUTING》 (IEEE Trans. Cloud Comput.)

年 卷 期：2022年第10卷第3期

页      面：1864-1874页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Major Science and Technology Program for Water Pollution Control and Treatment of China [2018ZX07111005] National Natural Science Foundation of China (NSFC) [61703011, 61802015] National Defense PreResearch Foundation of China [41401020401, 41401050102] 

主　　题：Green data centers distributed computing task scheduling profit maximization convex optimization 

摘      要：Infrastructure in Distributed Green Data Centers (DGDCs) is concurrently shared by multiple different applications to flexibly provide a growing number of services to global users in a cost-effective way. A highly challenging problem is how to maximize the total profit of the DGDC provider in a market where Internet Service Provider (ISP) bandwidth price, availability of green energy, price of power grid, and revenue brought by the execution of tasks all vary with geographical locations. Unlike existing studies, this article proposes a Geography-Aware Task Scheduling (GATS) approach by considering spatial variations in DGDCs to maximize the total profit of the DGDC provider by intelligently scheduling tasks of all applications. In each time slot, the formulated profit maximization problem is solved as a convex optimization one via the interior point method. Trace-driven simulations show that GATS achieves larger total profit and higher throughput than two typical task scheduling approaches.