An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment DOI

Mohamed Abdel‐Basset,

Laila Abdle-Fatah,

Arun Kumar Sangaiah

и другие.

Cluster Computing, Год журнала: 2018, Номер 22(S4), С. 8319 - 8334

Опубликована: Янв. 24, 2018

Язык: Английский

A Survey of Virtual Machine Placement Techniques in a Cloud Data Center DOI Open Access

Zoha Usmani,

Shailendra Singh

Procedia Computer Science, Год журнала: 2016, Номер 78, С. 491 - 498

Опубликована: Янв. 1, 2016

Energy consumption of massive-scale cloud data centers is increasing unacceptably. There a need to improve the energy efficiency such using Server Consolidation which aims at minimizing number Active Physical Machines (APMs) in center. Effective VM placement and migration techniques act as key optimum consolidation. Many recently proposed realize dynamic consolidation while optimizing placement. This paper presents comprehensive study state-of-the-art used green focus on improving efficiency. A detailed comparison presented, revealing pitfalls suggesting improvisation methods along this direction.

Язык: Английский

Процитировано

130

Energy-aware virtual machine allocation for cloud with resource reservation DOI
Xinqian Zhang,

Tingming Wu,

Mingsong Chen

и другие.

Journal of Systems and Software, Год журнала: 2018, Номер 147, С. 147 - 161

Опубликована: Окт. 20, 2018

Язык: Английский

Процитировано

125

Multi-Objective Energy Efficient Virtual Machines Allocation at the Cloud Data Center DOI
Neeraj Sharma, Ram Mohana Reddy Guddeti

IEEE Transactions on Services Computing, Год журнала: 2016, Номер 12(1), С. 158 - 171

Опубликована: Июль 29, 2016

Due to the growing demand of cloud services, allocation energy efficient resources (CPU, memory, storage, etc.) and utilization are major challenging issues a large data center. In this paper, we propose an Euclidean distance based multi-objective in form virtual machines (VMs) designed VM migration policy at Further VMs Physical Machines (PMs) is carried out by our proposed hybrid approach Genetic Algorithm (GA) Particle Swarm Optimization (PSO) referred as HGAPSO. The HGAPSO not only saves consumption minimizes wastage but also avoids SLA violation To check performance algorithm technique consumption, violation, performed extended amount experiment both heterogeneous homogeneous center environments. with migration, compared work branch-and-bound exact algorithm. experimental results show superiority over terms efficiency, optimal utilization, violation.

Язык: Английский

Процитировано

119

Approaches for optimizing virtual machine placement and migration in cloud environments: A survey DOI
Manoel C. Silva Filho,

Claudio C. Monteiro,

Pedro R. M. Inácio

и другие.

Journal of Parallel and Distributed Computing, Год журнала: 2017, Номер 111, С. 222 - 250

Опубликована: Сен. 20, 2017

Язык: Английский

Процитировано

114

An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment DOI

Mohamed Abdel‐Basset,

Laila Abdle-Fatah,

Arun Kumar Sangaiah

и другие.

Cluster Computing, Год журнала: 2018, Номер 22(S4), С. 8319 - 8334

Опубликована: Янв. 24, 2018

Язык: Английский

Процитировано

113