A Novel Energy and Communication Aware Scheduling on Green Cloud Computing  

作　　者：Laila Almutairi Shabnam Mohamed Aslam 

机构地区：[1]Department of Computer Engineering,Computer Science and Information Technology College,Majmaah University,Al Majmaah,11952,Saudi Arabia [2]Department of Information Technology,Computer Science and Information Technology College,Majmaah University,Al Majmaah,11952,Saudi Arabia

出　　处：《Computers, Materials & Continua》2023年第12期2791-2811,共21页计算机、材料和连续体（英文）

摘　　要：The rapid growth of service-oriented and cloud computing has created large-scale data centres worldwide.Modern data centres’operating costs mostly come from back-end cloud infrastructure and energy consumption.In cloud computing,extensive communication resources are required.Moreover,cloud applications require more bandwidth to transfer large amounts of data to satisfy end-user requirements.It is also essential that no communication source can cause congestion or bag loss owing to unnecessary switching buffers.This paper proposes a novel Energy and Communication(EC)aware scheduling(EC-scheduler)algorithm for green cloud computing,which optimizes data centre energy consumption and traffic load.The primary goal of the proposed EC-scheduler is to assign user applications to cloud data centre resources with minimal utilization of data centres.We first introduce a Multi-Objective Leader Salp Swarm(MLSS)algorithm for task sorting,which ensures traffic load balancing,and then an Emotional Artificial Neural Network(EANN)for efficient resource allocation.EC-scheduler schedules cloud user requirements to the cloud server by optimizing both energy and communication delay,which supports the lower emission of carbon dioxide by the cloud server system,enabling a green,unalloyed environment.We tested the proposed plan and existing cloud scheduling methods using the GreenCloud simulator to analyze the efficiency of optimizing data centre energy and other scheduler metrics.The EC-scheduler parameters Power Usage Effectiveness(PUE),Data Centre Energy Productivity(DCEP),Throughput,Average Execution Time(AET),Energy Consumption,and Makespan showed up to 26.738%,37.59%,50%,4.34%,34.2%,and 33.54%higher efficiency,respectively,than existing state of the art schedulers concerning number of user applications and number of user requests.

关 键 词：EC-scheduler green cloud energy efficiency task scheduling task sorting resource allocation 

分 类 号：TP38[自动化与计算机技术—计算机系统结构]