Abstract
The cloud-computing concept has emerged as a powerful mechanism for data storage by providing a suitable platform for data centers. Recent studies show that the energy consumption of cloud computing systems is a key issue. Therefore, we should reduce the energy consumption to satisfy performance requirements, minimize power consumption, and maximize resource utilization. This paper introduces a novel algorithm that could allocate resources in a cloud-computing environment based on an energy optimization method called Sharing with Live Migration (SLM). In this scheduler, we used the Cloud-Sim toolkit to manage the usage of virtual machines (VMs) based on a novel algorithm that learns and predicts the similarity between the tasks, and then allocates each of them to a suitable VM. On the other hand, SLM satisfies the Quality of Services (QoS) constraints of the hosted applications by adopting a migration process. The experimental results show that the algorithm exhibits better performance, while saving power and minimizing the processing time. Therefore, the SLM algorithm demonstrates improved virtual machine efficiency and resource utilization compared to an adapted state-of-the-art algorithm for a similar problem.
Highlights
Cloud computing represents the preferred alternative for on-demand computation and storage where clients can save, retrieve, and share any measure of data in the cloud [1]
The results showed energy consumption improvement compared with two benchmarks—Dynamic Voltage and Frequency Scaling (DVFS) and an Energy-aware Scheduling algorithm using Workload-aware Consolidation Technique (ESWCT)
We present a novel algorithm for a cloud-computing environment that could allocate resources based on energy optimization methods called Sharing with Live Migration (SLM)
Summary
Cloud computing represents the preferred alternative for on-demand computation and storage where clients can save, retrieve, and share any measure of data in the cloud [1] Aside from their benefits, cloud-computing data centers are facing many problems, with high power consumption being one of the most significant ones [2]. Task scheduling is one of the critical determinants in energy consumption for a cloud data center. Innovative green task-scheduling algorithms are designed and established to reduce energy consumption by determining the optimal processing speed to execute all tasks before a deadline. Hybrid scheduling combines multiple scheduling parameters in one scheduling technique to decrease the execution time and produce a hybrid scheduling Most of these algorithms are either novel or designed by combining one or more of the old predesigned schedulers.
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