Multi-Objective Energy Efficient Virtual Machines Allocation at the Cloud Data Center

2019 ◽  
Vol 12 (1) ◽  
pp. 158-171 ◽  
Author(s):  
Neeraj Kumar Sharma ◽  
G. Ram Mohana Reddy
Keyword(s):  
Data Center ◽  
Energy Efficient ◽  
Virtual Machines ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Multi Objective

LSRU: A Novel Deep Learning based Hybrid Method to Predict the Workload of Virtual Machines in Cloud Data Center

2020 ◽  
Author(s):  
Md. Nahid Hasan Shuvo ◽  
Md. Nahid Hasan Shuvo ◽  
Mirza Mohd Shahriar Maswood ◽  
Mirza Mohd Shahriar Maswood ◽  
Abdullah G. Alharbi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hybrid Method ◽  
Data Center ◽  
Virtual Machines ◽  
Cloud Data Center ◽  
Cloud Data

scholarly journals ENERGY EFFICIENT LOAD BALANCING FOR CLOUD DATA CENTER

2017 ◽  
Vol 10 (13) ◽  
pp. 162
Author(s):  
Amey Rivankar ◽  
Anusooya G
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Energy Efficient ◽  
Large Scale ◽  
Virtual Machines ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Efficient Resource ◽  
Cloud Data Centers ◽  
Near Future

Cloud computing is the latest trend in large-scale distributed computing. It provides diverse services on demand to distributive resources such asservers, software, and databases. One of the challenging problems in cloud data centers is to manage the load of different reconfigurable virtual machines over one another. Thus, in the near future of cloud computing field, providing a mechanism for efficient resource management will be very significant. Many load balancing algorithms have been already implemented and executed to manage the resources efficiently and adequately. The objective of this paper is to analyze shortcomings of existing algorithms and implement a new algorithm which will give optimized load balancingresult.

scholarly journals Task Classification Based Energy-Aware Consolidation in Clouds

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
HeeSeok Choi ◽  
JongBeom Lim ◽  
Heonchang Yu ◽  
EunYoung Lee
Keyword(s):  
Resource Utilization ◽  
Data Center ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Energy Aware ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Balance Task ◽  
Task Classification ◽  
Vm Consolidation

We consider a cloud data center, in which the service provider supplies virtual machines (VMs) on hosts or physical machines (PMs) to its subscribers for computation in an on-demand fashion. For the cloud data center, we propose a task consolidation algorithm based on task classification (i.e., computation-intensive and data-intensive) and resource utilization (e.g., CPU and RAM). Furthermore, we design a VM consolidation algorithm to balance task execution time and energy consumption without violating a predefined service level agreement (SLA). Unlike the existing research on VM consolidation or scheduling that applies none or single threshold schemes, we focus on a double threshold (upper and lower) scheme, which is used for VM consolidation. More specifically, when a host operates with resource utilization below the lower threshold, all the VMs on the host will be scheduled to be migrated to other hosts and then the host will be powered down, while when a host operates with resource utilization above the upper threshold, a VM will be migrated to avoid using 100% of resource utilization. Based on experimental performance evaluations with real-world traces, we prove that our task classification based energy-aware consolidation algorithm (TCEA) achieves a significant energy reduction without incurring predefined SLA violations.

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