Paper Info
Title
TARGO: Transition and Reallocation Based Green Optimization for Cloud VMs.
Abstract
Much research has been conducted focusing on improving resource utilization efficiency in data centers in the context of Green Cloud Computing (GCC). While virtualization enables better resource provision and utilization for various computational resources, different approaches are proposed based on virtual machine (VM) optimizations using either server or workload consolidation techniques. Nonetheless, these solutions can only be applied inside the Cloud. In fact, Infrastructure-as-a-Service (IaaS) users can hardly proactively achieve better VM resource utilization efficiency, as they typically have no control over any hyper visor or hardware in any Clouds. The issue gets more critical when workloads on VMs alter dramatically from time to time. This paper presents a novel approach namely Transition and Reallocation based Green Optimization (TARGO) for such users. Through fully automated and intelligent VM optimization according to customizable optimization rules, TARGO guarantees that VMs or their successors being optimized will always run at their customizable green optimal states regardless how workloads vary. Experiments conducted on Amazon EC2 instances in the EU region show that, even under extreme random workloads, TARGO is still capable of selecting and retaining VM successors which run at an average CPU utilization of 50%-60%.
Year
DOI
Venue
2013
10.1109/GreenCom-iThings-CPSCom.2013.56
GreenCom), 2013 IEEE and Internet of Things
Keywords
Field
DocType
average cpu utilization,targo guarantee,resource utilization efficiency,intelligent vm optimization,extreme random workloads,vm successor,green optimization,green cloud computing,better resource provision,various computational resource,vm resource utilization efficiency,cloud vms,cloud computing,virtual machines
Virtualization,Virtual machine,Computer science,CPU time,Hypervisor,Workload consolidation,Operating system,Cloud computing
Conference
Citations 
PageRank 
References 
2
0.36
14
Authors
4
Name
Order
Citations
PageRank
Daren Fang1252.83
Xiaodong Liu23611.83
Lin Liu31128115.75
Hongji Yang41039137.37