Heuristic based Energy-aware Resource Allocation by Dynamic Consolidation of Virtual Machines in Cloud Data Center

2013 ◽  
Vol 7 (8) ◽  
pp. 1825-1842 ◽  
Keyword(s):  
Resource Allocation ◽  
Data Center ◽  
Virtual Machines ◽  
Energy Aware ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Dynamic Consolidation

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.

Preference Based Resource Allocation In Cloud Data Center

2018 ◽  
Vol 6 (2) ◽  
pp. 287-292
Author(s):  
M.R. Dave ◽  
◽  
H.B. Patel ◽  
B. Shrimali ◽  
◽  
...  
Keyword(s):  
Resource Allocation ◽  
Data Center ◽  
Cloud Data Center ◽  
Cloud Data

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

Minimization of VM Placement Change in Energy-Aware Resource Provisioning for Cloud Data Center

2013 ◽  
Vol 325-326 ◽  
pp. 1730-1733 ◽  
Author(s):  
Si Yuan Jing ◽  
Shahzad Ali ◽  
Kun She
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Network Flow ◽  
Resource Provisioning ◽  
Approximate Algorithm ◽  
Vm Placement ◽  
Energy Aware ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Placement Change

Numerous part of the energy-aware resource provision research for cloud data center just considers how to maximize the resource utilization, i.e. minimize the required servers, without considering the overhead of a virtual machine (abbreviated as a VM) placement change. In this work, we propose a new method to minimize the energy consumption and VM placement change at the same time, moreover we also design a network-flow-theory based approximate algorithm to solve it. The simulation results show that, compared to existing work, the proposed method can slightly decrease the energy consumption but greatly decrease the number of VM placement change

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