@Article{cmc.2020.010934,
AUTHOR = {Amit Chhabra, Gurvinder Singh, Karanjeet Singh Kahlon},
TITLE = {QoS-Aware Energy-Efficient Task Scheduling on HPC Cloud Infrastructures Using Swarm-Intelligence Meta-Heuristics},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {2},
PAGES = {813--834},
URL = {http://www.techscience.com/cmc/v64n2/39331},
ISSN = {1546-2226},
ABSTRACT = {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 applications. However, 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 environment. Besides this, users have 
become more demanding in terms of Quality-of-service (QoS) expectations in terms of 
execution time, budget cost, utilization, and makespan. This 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 requirements. Moreover, the task scheduling in the Cloud is a prevalent NPHard problem. Motivated 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 workload. The proposed multi-objective CSPSO policy hybridizes the 
search qualities of two robust metaheuristics viz. cuckoo 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 
policy. Further, the proposed scheduling policy has been implemented in the CloudSim 
simulator and tested with real supercomputing workload traces. The comparative analysis 
validated that the proposed scheduling policy can produce efficient schedules with better 
performance over other well-known heuristics and meta-heuristics scheduling policies.},
DOI = {10.32604/cmc.2020.010934}
}