Energy consumption analysis of Virtual Machine migration in cloud using hybrid swarm optimization (ABC–BA)

2018 ◽  
Vol 76 (5) ◽  
pp. 3374-3390 ◽  
Author(s):  
K. Karthikeyan ◽  
R. Sunder ◽  
K. Shankar ◽  
S. K. Lakshmanaprabu ◽  
V. Vijayakumar ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machine Migration ◽  
Hybrid Swarm ◽  
Swarm Optimization ◽  
Energy Consumption Analysis

A Hybrid Swarm Optimization Algorithm For Reducing Energy Consumption In The Cloud

2018 ◽  
Vol 11 ◽  
Author(s):  
Mohammed Mostafa Abdulghafoor ◽  
Raed Abdulkareem Hasan ◽  
Zeyad Hussein Salih ◽  
Hayder Ali Nemah Alshara ◽  
Nicolae Tapus
Keyword(s):  
Energy Consumption ◽  
Optimization Algorithm ◽  
Hybrid Swarm ◽  
Swarm Optimization ◽  
Reducing Energy

Advances in Dynamic Virtual Machine Management for Cloud Data Centers

2017 ◽  
pp. 91-114
Author(s):  
Rashmi Rai ◽  
G. Sahoo
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Large Scale ◽  
Data Centers ◽  
Economic Modeling ◽  
User Preferences ◽  
Virtual Machine Migration ◽  
Carbon Footprints ◽  
Cloud Data ◽  
Computing Services

The ever-rising demand for computing services and the humongous amount of data generated everyday has led to the mushrooming of power craving data centers across the globe. These large-scale data centers consume huge amount of power and emit considerable amount of CO2.There have been significant work towards reducing energy consumption and carbon footprints using several heuristics for dynamic virtual machine consolidation problem. Here we have tried to solve this problem a bit differently by making use of utility functions, which are widely used in economic modeling for representing user preferences. Our approach also uses Meta heuristic genetic algorithm and the fitness is evaluated with the utility function to consolidate virtual machine migration within cloud environment. The initial results as compared with existing state of art shows marginal but significant improvement in energy consumption as well as overall SLA violations.

Towards A Virtual Machine Migration Algorithm Based On Multi-Objective Optimization

2017 ◽  
Vol 8 (3) ◽  
pp. 79-89 ◽  
Author(s):  
Xiang Chen ◽  
Jun-rong Tang ◽  
Yong Zhang
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Resource Competition ◽  
Service Level Agreement ◽  
Service Level ◽  
Multi Objective Optimization ◽  
Virtual Machine Migration ◽  
Multi Objective ◽  
Time Migration ◽  
Management Method

In the cloud computing, the virtual machine (VM) dynamical management method needs to consider VM resource re-configuration caused by system computation resource status changing and load fluctuation. Based on migration objectives as QoS (Quality of Service), resource competition and energy consumption, the VM migration time, migration objective node selection and VM placement strategies are designed in this work. The Multi-Criteria Decision-Making (MCDM) method is also introduced for migration destination host selection. Experiment results show that the multi-objective optimization management method with TOPSIS can achieve lower service-level agreement (SLA) violation rate, less energy consumption and better balance among different objectives.

scholarly journals Server Consolidation Based on Culture Multiple-Ant-Colony Algorithm in Cloud Computing

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2724 ◽  
Author(s):  
Yuan ◽  
Sun
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Ant Colony Algorithm ◽  
Data Centers ◽  
Virtual Machines ◽  
Ant Colony ◽  
Virtual Machine Migration ◽  
Server Consolidation ◽  
Cloud Data ◽  
Cloud Data Centers

High-energy consumption in data centers has become a critical issue. The dynamic server consolidation has significant effects on saving energy of a data center. An effective way to consolidate virtual machines is to migrate virtual machines in real time so that some light load physical machines can be turned off or switched to low-power mode. The present challenge is to reduce the energy consumption of cloud data centers. In this paper, for the first time, a server consolidation algorithm based on the culture multiple-ant-colony algorithm was proposed for dynamic execution of virtual machine migration, thus reducing the energy consumption of cloud data centers. The server consolidation algorithm based on the culture multiple-ant-colony algorithm (CMACA) finds an approximate optimal solution through a specific target function. The simulation results show that the proposed algorithm not only reduces the energy consumption but also reduces the number of virtual machine migration.

scholarly journals HSO: A Hybrid Swarm Optimization Algorithm for Re-Ducing Energy Consumption in the Cloudlets

2018 ◽  
Vol 16 (5) ◽  
pp. 2144 ◽  
Author(s):  
Raed A. Hasan ◽  
Mostafa A. Mohammed ◽  
Zeyad Hussein Salih ◽  
Mohammed Ariff Bin Ameedeen ◽  
Nicolae Ţăpuş ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Optimization Algorithm ◽  
Hybrid Swarm ◽  
Swarm Optimization

scholarly journals Modelling energy consumption of network transfers and virtual machine migration

2016 ◽  
Vol 56 ◽  
pp. 388-406 ◽  
Author(s):  
Vincenzo De Maio ◽  
Radu Prodan ◽  
Shajulin Benedict ◽  
Gabor Kecskemeti
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machine Migration

scholarly journals Low Power Consumption on Cloud Data Centers Using HSA Algorithm

2020 ◽  
pp. 78-84
Author(s):  
Louay Al Nuaimy ◽  
Tadele Debisa Deressa ◽  
Mohammad Mastan ◽  
Syed Umar
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Search Algorithm ◽  
Rapid Development ◽  
Electricity Consumption ◽  
Cloud Infrastructure ◽  
Virtual Machine Migration ◽  
Cloud Data ◽  
The Right

The rapid development of knowledge and communication has created a new processing style called cloud computing. One of the key issues facing cloud infrastructure providers is minimizing costs and maximizing profitability. Power management in cloud centres is very important to achieve this. Energy consumption can be reduced by releasing inactive nodes or by reducing the migration of virtual machines. The second is one of the challenges of choosing the virtual machine deployment method to migrate to the right node. This article proposes an approach to reduce electricity consumption in cloud centres. This approach adapts Harmony's search algorithm to move virtual machines. Positioning is done by sorting nodes and virtual machines according to their priorities in descending order. Priority is calculated based on the workload. The proposed procedure is envisaged. The evaluation results show less virtual machine migration, greater efficiency and lower energy consumption.

Optimal Green Virtual Machine Migration Model

2013 ◽  
Vol 9 (3) ◽  
pp. 35-52 ◽  
Author(s):  
Noah Sabry ◽  
Paul Krause
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Carbon Footprint ◽  
Virtual Machines ◽  
Clean Energy ◽  
Cloud Services ◽  
Virtual Machine Migration ◽  
Migration Model ◽  
Data Centres

Cloud computing provides the opportunity to migrate virtual machines to “follow-the-green” data centres. That is, to migrate virtual machines between green data centres on the basis of clean energy availability, to mitigate the environmental impact of carbon footprint emissions and energy consumption. The virtual machine migration problem can be modelled to maximize the utility of computing resources or minimizing the cost of using computing resources. However, this would ignore the network energy consumption and its impact on the overall CO2 emissions. Unless this is taken into account the extra data traffic due to migration of data could then cause an increase in brown energy consumption and eventually lead to an unintended increase in carbon footprint emissions. Energy consumption is a key aspect in deploying distributed service in cloud networks within decentralized service delivery architectures. In this paper, the authors address an optimization view of the problem of locating a set of cloud services on a set of sites green data centres managed by a service provider or hybrid cloud computing brokerage. The authors’ goal is to minimize the overall network energy consumption and carbon footprint emissions for accessing the cloud services for any pair of data centres i and j. The authors propose an optimization migration model based on the development of integer linear programming (ILP) models, to identify the leverage of green energy sources with data centres and the energy consumption of migrating VMs.

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