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QoS-Aware energy-Efficient Task Scheduling on HPC Cloud Infrastructures Using Swarm-Intelligence meta-heuristics

Computers, Materials & Continua 2020年第8期64卷 813-834页

作者： Amit Chhabra Gurvinder Singh Karanjeet Singh Kahlon Department of Computer Engineering&Technology Guru Nanak Dev UniversityAmritsar143005India Department of Computer Science Guru Nanak Dev UniversityAmritsar143005India

Cloud computing infrastructure has been evolving as a cost-effective platform for providing computational resources in the form of high-performance computing as a service(HPCaaS)to users for executing HPC ***,the broader use of the Cloud services,the rapid increase in the size,and the capacity of Cloud data centers bring a remarkable rise in energy consumption leading to a significant rise in the system provider expenses and carbon emissions in the *** this,users have become more demanding in terms of Quality-of-service(QoS)expectations in terms of execution time,budget cost,utilization,and *** situation calls for the design of task scheduling policy,which ensures efficient task sequencing and allocation of computing resources to tasks to meet the trade-off between QoS promises and service provider ***,the task scheduling in the Cloud is a prevalent NP-Hard *** by these concerns,this paper introduces and implements a QoS-aware energy-Efficient Scheduling policy called as CSPSO,for scheduling tasks in Cloud systems to reduce the energy consumption of cloud resources and minimize the makespan of *** proposed multi-objective CSPSO policy hybridizes the search qualities of two robust metaheuristics *** search(CS)and particle swarm optimization(PSO)to overcome the slow convergence and lack of diversity of standard CS algorithm.A fitness-aware resource allocation(FARA)heuristic was developed and used by the proposed policy to allocate resources to tasks efficiently.A velocity update mechanism for cuckoo individuals is designed and incorporated in the proposed CSPSO ***,the proposed scheduling policy has been implemented in the CloudSim simulator and tested with real supercomputing workload *** comparative analysis validated that the proposed scheduling policy can produce efficient schedules with better performance over other well-known heuristics and meta-heuristic

关键词： HPC-as-a-Service task scheduling quality-of-service meta-heuristics and energy-efficiency

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