Fair energy-efficient virtual machine scheduling for Internet of Things applications in cloud environment

With the rapid resource requirements of Internet of Things applications, cloud computing technology is regarded as a promising paradigm for resource provision. To improve the efficiency and effectiveness of cloud services, it is essential to improve the resource fairness and achieve energy savings. However, it is still a challenge to schedule the virtual machines in an energy-efficient manner while taking into consideration the resource fairness. In view of this challenge, a fair energy-efficient virtual machine scheduling method for Internet of Things applications is designed in this article. Specifically, energy and fairness are analyzed in a formal way. Then, a virtual machine scheduling method is proposed to achieve the energy efficiency and further improve the resource fairness during the executions of Internet of Things applications. Finally, experimental evaluation demonstrates the validity of our proposed method.

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Virtual Machines Scheduling Algorithm Based on Multi-Objective Optimization in Cloud Computing

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1046.508 ◽

2014 ◽

Vol 1046 ◽

pp. 508-511

Author(s):

Jian Rong Zhu ◽

Yi Zhuang ◽

Jing Li ◽

Wei Zhu

Keyword(s):

Cloud Computing ◽

Energy Consumption ◽

Virtual Machine ◽

Virtual Machines ◽

Scheduling Algorithm ◽

Machine Scheduling ◽

Multi Objective Optimization ◽

Multi Objective ◽

Virtual Machine Scheduling ◽

Set Up

How to reduce energy consumption while improving utility of datacenter is one of the key technologies in the cloud computing environment. In this paper, we use energy consumption and utility of data center as objective functions to set up a virtual machine scheduling model based on multi-objective optimization VMSA-MOP, and design a virtual machine scheduling algorithm based on NSGA-2 to solve the model. Experimental results show that compared with other virtual machine scheduling algorithms, our algorithm can obtain relatively optimal scheduling results.

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A Cloud-based Conceptual Framework for Multi-Objective Virtual Machine Scheduling using Whale Optimization Algorithm

International Journal of Innovative Computing ◽

10.11113/ijic.v8n3.199 ◽

2018 ◽

Vol 8 (3) ◽

Cited By ~ 3

Author(s):

Nadim Rana ◽

Muhammad Shafie Abd Latiff ◽

Shafi'i Muhammad Abdulhamid

Keyword(s):

Conceptual Framework ◽

Virtual Machine ◽

Optimization Algorithm ◽

Virtual Machines ◽

Optimal Solution ◽

Machine Scheduling ◽

Whale Optimization Algorithm ◽

Multi Objective ◽

Whale Optimization ◽

Virtual Machine Scheduling

Virtual machine scheduling in the cloud is considered one of the major issue to solve optimal resource allocation problem on the heterogeneous datacenters. With respect to that, the key concern is to map the virtual machines (VMs) with physical machines (PMs) in a way that maximum resource utilization can be achieved with minimum cost. Due to the fact that scheduling is an NP-hard problem, a metaheuristic approach is proven to achieve a better optimal solution to solve this problem. In a rapid changing heterogeneous environment, where millions of resources can be allocated and deallocate in a fraction of the time, modern metaheuristic algorithms perform well due to its immense power to solve the multidimensional problem with fast convergence speed. This paper presents a conceptual framework for solving multi-objective VM scheduling problem using novel metaheuristic Whale optimization algorithm (WOA). Further, we present the problem formulation for the framework to achieve multi-objective functions.

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