An Energy-Aware Algorithm for Virtual Machine Placement in Cloud Computing

Zhao, Da-ming; Zhou, Jian; Li, Keqin

doi:10.1109/access.2019.2913175

Cited by 34 publications

(13 citation statements)

References 22 publications

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“…An energy-efficient algorithm named GATA for VM allocation is designed by Zhao et al [25]. ey generated the proposed algorithm with a combination of genetic algorithm and tabu search algorithm.…”

Section: Related Workmentioning

confidence: 99%

Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment

Samriya

Patel

Khurana

et al. 2021

Mathematical Problems in Engineering

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Cloud computing is the most prominent established framework; it offers access to resources and services based on large-scale distributed processing. An intensive management system is required for the cloud environment, and it should gather information about all phases of task processing and ensuring fair resource provisioning through the levels of Quality of Service (QoS). Virtual machine allocation is a major issue in the cloud environment that contributes to energy consumption and asset utilization in distributed cloud computing. Subsequently, in this paper, a multiobjective Emperor Penguin Optimization (EPO) algorithm is proposed to allocate the virtual machines with power utilization in a heterogeneous cloud environment. The proposed method is analyzed to make it suitable for virtual machines in the data center through Binary Gravity Search Algorithm (BGSA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). To compare with other strategies, EPO is energy-efficient and there are significant differences. The results of the proposed system have been evaluated through the JAVA simulation platform. The exploratory outcome presents that the proposed EPO-based system is very effective in limiting energy consumption, SLA violation (SLAV), and enlarging QoS requirements for giving capable cloud service.

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Section: Related Workmentioning

confidence: 99%

Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment

Samriya

Patel

Khurana

et al. 2021

Mathematical Problems in Engineering

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“…While a study showed that a scalable network topology; flattened butterfly with commercial switch chips provides full utilization with reduced energy proportional datacenter [28]. Whereas, in [29], VM placement is addressed by combining two approaches that are genetic and the tabue search algorithms to reduce power consumption in cloud environment. Tabue search algorithm is used to enhance genetic algorithm searching by performing mutation operations.…”

Section: Sdn-based Cloud Computing Resource Management In Energy Effimentioning

confidence: 99%

Resource Management in SDN-Based Cloud and SDN-Based Fog Computing: Taxonomy Study

et al. 2021

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Software-defined networks (SDN) is an evolution in networking field where the data plane is separated from the control plane and all the controlling and management tasks are deployed in a centralized controller. Due to its features regarding ease management, it is emerged in other fields such as cloud and fog computing in order to manage asymmetric communication across nodes, thus improving the performance and reducing the power consumption. This study focused on research that were conducted in SDN-based clouds and SDN-based fogs. It overviewed the important contributions in SDN clouds in terms of improving network performances and energy optimization. Moreover, state-of-the-art studies in SDN fogs are presented. The features, methods, environment, dataset, simulation tool and main contributions are highlighted. Finally, the open issues related to both SDN clouds and SDN fogs are defined and discussed.

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“…The power consumption rate of hosts can be modeled as follows. The CPU energy consumption of a host and the utilization of the CPU can be expressed linearly [47], including three stages: idle, busy, and off. The energy consumption rate can be calculated as:…”

Section: Resource Allocation Modelmentioning

confidence: 99%

Online Offloading Scheduling and Resource Allocation Algorithms for Vehicular Edge Computing System

Wang

Zheng

et al. 2020

IEEE Access

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To accommodate the exponentially increasing computation demands of vehicle-based applications, vehicular edge computing (VEC) system was introduced. This paper considers a three-layer VEC architecture and proposes an online offloading scheduling and resource allocation (OOSRA) algorithm to improve the system performance. Specifically, this study designs a game-theoretic online algorithm to solve the problem of computation task offloading scheduling, and employs an online bin-packing algorithm to compute the resource allocation modified from the First Fit algorithm, which can be adapted to various traffic flow and service attributes. Extensive simulations are conducted, and a numerical analysis of simulation results verifies the effectiveness of the OOSRA-VEC system. The algorithms proposed in this paper are online, adaptive, and distributed, which can provide useful references for future development in VEC system protocols. INDEX TERMS Intelligent vehicles, intelligent transportation systems, edge-computing, game theory.

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An Energy-Aware Algorithm for Virtual Machine Placement in Cloud Computing

Cited by 34 publications

References 22 publications

Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment

Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment

Resource Management in SDN-Based Cloud and SDN-Based Fog Computing: Taxonomy Study

Online Offloading Scheduling and Resource Allocation Algorithms for Vehicular Edge Computing System

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