Multi-objective Virtual Machine Selection in Cloud Data Centers Using Optimized Scheduling
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Multi‑objective Virtual Machine Selection in Cloud Data Centers Using Optimized Scheduling Banavath Balaji Naik1 · Dhananjay Singh2 · Arun Barun Samaddar1 Accepted: 11 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In cloud computing, more often times cloud assets are underutilized because of poor allocation of task in virtual machine (VM). There exist inconsistent factors affecting the scheduling tasks to VMs. In this paper, an effective scheduling with multi-objective VM selection in cloud data centers is proposed. The proposed multi-objective VM selection and optimized scheduling is described as follows. Initially the input tasks are gathered in a task queue and tasks computational time and trust parameters are measured in the task manager. Then the tasks are prioritized based on the computed measures. Finally, the tasks are scheduled to the VMs in host manager. Here, multi-objectives are considered for VM selection. The objectives such as power usage, load volume, and resource wastage are evaluated for the VMs and the entropy is calculated for the measured objectives and based on the entropy value krill herd optimization algorithm prioritized tasks are scheduled to the VMs. The experimental results prove that the proposed entropy based krill herd optimization scheduling outperforms the existing general krill herd optimization, cuckoo search optimization, cloud list scheduling, minimum completion cloud, cloud task partitioning scheduling and round robin techniques. Keywords Task prioritization · Multi-objective measures · VM selection · Entropy measure · Optimization · Scheduling
* Banavath Balaji Naik [email protected] Dhananjay Singh [email protected] Arun Barun Samaddar [email protected] 1
Department of Computer Science and Engineering, National Institute of Technology, Ravangla 737139, Sikkim, India
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Department of Electronic Engineering, Hankuk (Korea) University of Foreign Studies Global Campus, Gyeonggi‑do, South Korea
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1 Introduction Cloud computing provides on-demand computing and storage services with high adaptability. Notwithstanding, the expanding energy utilization of cloud Data Centers (DCs) has become a conspicuous issue. Task scheduling is a significant advance to improve the general execution of the distributed computing [1–3]. As of late, the issue of task allocation has grabbed the consideration of investigators. Task allotment is viewed as a basic issue in the cloud computing environment by seeing various elements like completion time, the all-out expense for executing every one of clients’ assignments, usage of the asset, power utilization, and adaptation to internal failure [4]. Structuring and actualizing techniques and calculations for explicit tasks with realtime deadlines or energy efficient tasks such as dependent or independent. There is certain inherent problem associated with resource provisioning and task scheduling. Today, the main endeavours such as, Amazon and Micro
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