Fine-Grained Resource Provisioning and Task Scheduling for Heterogeneous Applications in Distributed Green Clouds  

作　　者：Haitao Yuan Meng Chu Zhou Qing Liu Abdullah Abusorrah 

机构地区：[1]Department of Electrical and Computer Engineering,New Jersey Institute of Technology,Newark,NJ 07102 USA [2]Department of Electrical and Computer Engineering,Faculty of Engineering,and the Center of Research Excellence in Renewable Energy and Power Systems,King Abdulaziz University,Jeddah 21589,Saudi Arabia [3]IEEE

出　　处：《IEEE/CAA Journal of Automatica Sinica》2020年第5期1380-1393,共14页自动化学报（英文版）

基　　金：supported in part by the National Natural Science Foundation of China(61802015,61703011);the Major Science and Technology Program for Water Pollution Control and Treatment of China(2018ZX07111005);the National Defense Pre-Research Foundation of China(41401020401,41401050102);the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah(D-422-135-1441)。

摘　　要：An increasing number of enterprises have adopted cloud computing to manage their important business applications in distributed green cloud(DGC)systems for low response time and high cost-effectiveness in recent years.Task scheduling and resource allocation in DGCs have gained more attention in both academia and industry as they are costly to manage because of high energy consumption.Many factors in DGCs,e.g.,prices of power grid,and the amount of green energy express strong spatial variations.The dramatic increase of arriving tasks brings a big challenge to minimize the energy cost of a DGC provider in a market where above factors all possess spatial variations.This work adopts a G/G/1 queuing system to analyze the performance of servers in DGCs.Based on it,a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based bees algorithm(SBA)to find SBA can minimize the energy cost of a DGC provider by optimally allocating tasks of heterogeneous applications among multiple DGCs,and specifying the running speed of each server and the number of powered-on servers in each GC while strictly meeting response time limits of tasks of all applications.Realistic databased experimental results prove that SBA achieves lower energy cost than several benchmark scheduling methods do.

关 键 词：Bees algorithm data centers distributed green cloud(DGC) energy optimization intelligent optimization simulated annealing task scheduling machine learning 

分 类 号：TP368.5[自动化与计算机技术—计算机系统结构] F273[自动化与计算机技术—计算机科学与技术]