Task Scheduling Techniques for Energy Efficiency in the Cloud
Keywords:CDC (Cloud Datacentre), CSP (Cloud Service Provider), DA (Dragonfly Algorithm), EDA-GA (Estimation of Distribution Algorithm and GA), FF (Firefly), GA (Genetic Algorithm), IaaS (Infrastructure-as-a-Service), MGWO (Modified Mean Grey Wolf Optimization Algorithm), PaaS (Platform-as-a-Service), SaaS (Software-as-a-Service), SAW (Sample Additive Weighting), SLA-LB (Service Level Agreement Based Load Balancing), TBTS (Threshold Based Task Scheduling Algorithm), TS (Task Scheduling)
Energy efficiency is a key goal in cloud datacentre since it saves money and complies with green computing standards. When energy efficiency is taken into account, task scheduling becomes much more complicated and crucial. Execution overhead and scalability are major concerns in current research on energy-efficient task scheduling. Machine learning has been widely utilized to solve the problem of energy-efficient task scheduling, however, it is usually used to anticipate resource usage rather than selecting the schedule. The bulk of machine learning approaches are used to anticipate resource consumption, and heuristic or metaheuristic algorithms utilize these predictions to choose which computer resource should be assigned to a certain activity. As per the knowledge and research, none of the algorithms have independently used machine learning to make an energy-efficient scheduling decision. Heuristic or meta-heuristic approaches, as well as approximation algorithms, are frequently used to solve NP-complete problems. In this paper, we discuss various studies that have been used to solve the problem of task scheduling which belongs to a class of NP-hard. We have proposed a model to achieve the objective of reduced energy consumption and CO2 emission in a cloud environment. In the future, the model shall be implemented in MATLAB and would be assessed on various parameters like makespan, execution time, resource utilization, QoS, Energy utilization, etc.
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