Reducing Energy Consumption by Load Aggregation with an Optimized Dynamic Live Migration of Virtual Machines

2010 ◽  
Author(s):  
Daniel Versick ◽  
Djamshid Tavangarian
Keyword(s):  
Energy Consumption ◽  
Virtual Machines ◽  
Live Migration ◽  
Reducing Energy

scholarly journals Sharing with Live Migration Energy Optimization Scheduler for Cloud Computing Data Centers

2018 ◽  
Vol 10 (9) ◽  
pp. 86 ◽  
Author(s):  
Samah Alshathri ◽  
Bogdan Ghita ◽  
Nathan Clarke
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Data Storage ◽  
Resource Utilization ◽  
Data Centers ◽  
Virtual Machines ◽  
Energy Optimization ◽  
Optimization Method ◽  
Live Migration ◽  
Novel Algorithm

The cloud-computing concept has emerged as a powerful mechanism for data storage by providing a suitable platform for data centers. Recent studies show that the energy consumption of cloud computing systems is a key issue. Therefore, we should reduce the energy consumption to satisfy performance requirements, minimize power consumption, and maximize resource utilization. This paper introduces a novel algorithm that could allocate resources in a cloud-computing environment based on an energy optimization method called Sharing with Live Migration (SLM). In this scheduler, we used the Cloud-Sim toolkit to manage the usage of virtual machines (VMs) based on a novel algorithm that learns and predicts the similarity between the tasks, and then allocates each of them to a suitable VM. On the other hand, SLM satisfies the Quality of Services (QoS) constraints of the hosted applications by adopting a migration process. The experimental results show that the algorithm exhibits better performance, while saving power and minimizing the processing time. Therefore, the SLM algorithm demonstrates improved virtual machine efficiency and resource utilization compared to an adapted state-of-the-art algorithm for a similar problem.

A Systematic Literature Review on Virtual Machine Consolidation

2022 ◽  
Vol 54 (8) ◽  
pp. 1-38
Author(s):  
Alexandre H. T. Dias ◽  
Luiz. H. A. Correia ◽  
Neumar Malheiros
Keyword(s):  
Energy Consumption ◽  
Literature Review ◽  
Virtual Machine ◽  
Systematic Literature Review ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Resource Provisioning ◽  
Cloud Data ◽  
Virtual Machine Consolidation ◽  
Reducing Energy

Virtual machine consolidation has been a widely explored topic in recent years due to Cloud Data Centers’ effect on global energy consumption. Thus, academia and companies made efforts to achieve green computing, reducing energy consumption to minimize environmental impact. By consolidating Virtual Machines into a fewer number of Physical Machines, resource provisioning mechanisms can shutdown idle Physical Machines to reduce energy consumption and improve resource utilization. However, there is a tradeoff between reducing energy consumption while assuring the Quality of Service established on the Service Level Agreement. This work introduces a Systematic Literature Review of one year of advances in virtual machine consolidation. It provides a discussion on methods used in each step of the virtual machine consolidation, a classification of papers according to their contribution, and a quantitative and qualitative analysis of datasets, scenarios, and metrics.

scholarly journals A SURVEY FOR ENERGY EFFICIENCY IN CLOUD DATA CENTERS

2017 ◽  
Vol 5 (4RACSIT) ◽  
pp. 63-68
Author(s):  
Dinesh Raj Paneru ◽  
Madhu B. R. ◽  
Santosh Naik
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machines ◽  
Live Migration ◽  
Vm Migration ◽  
Cloud Data ◽  
Platform As A Service ◽  
Cloud Data Centers ◽  
And Storage ◽  
Migration Technique

Services such as Platform as a Service (PaaS), Infrastructure as a Service (IaaS) and Software as a Service (SaaS) are provided by Cloud Computing. Subscription based computing resources and storage is offered in cloud. Cloud Computing is boosted by Virtualization technology. To move running applications or VMs starting with one physical machine then onto the next, while the customer is associated is named as Live VM migration. VM migration is empowered by means of Virtualization innovation to adjust stack in the server farms. Movement is done fundamentally to deal with the assets progressively. Server Consolidation’s main goal is to expel the issue of server sprawl. It tries to pack VMs from daintily stacked host on to fewer machines to satisfy assets needs. On other hand Load balancing helps in distributing workloads across multiple computing resources. Also in the presence of low loaded machines it avoids machines from getting overloaded and maintains efficiency. To balance the load across the systems in various cases, live migration technique is used with the application of various algorithms. The movement of virtual machines from completely stacked physical machines to low stacked physical machines is the instrument to adjust the entire framework stack. When we are worried about the energy consumption in Cloud Computing, VM consolidation & Server Consolidation comes into scenario in Virtual Machine movement method which itself implies that there is low energy consumption.

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

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
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