Paper Info
Title
iPlace: An Intelligent and Tunable Power- and Performance-Aware Virtual Machine Placement Technique for Cloud-Based Real-Time Applications
Abstract
Power and performance tradeoffs are critical and challenging issues faced by cloud service providers (CSPs) while managing their data centers. On the one hand, CSPs strive to reduce power consumption of their data centers to not only decrease their energy costs but to also reduce adverse impact on the environment. On the other hand, CSPs must deliver performance expected by the applications hosted in their cloud in accordance with predefined Service Level Agreements (SLAs). Not doing so will lead to loss of customers and thereby major revenue losses for the CSPs. Addressing these dual set of challenges is hard for the CSPs because power management and performance assurance are conflicting objectives, particularly in the context of multi-tenant cloud systems where multiple virtual machines (VMs) may be hosted on a single physical server. The problem becomes even harder when real-time applications are hosted in these VMs. To address these challenges and make appropriate tradeoffs, we present iPlace, which is an intelligent and tunable power- and performance-aware VM placement middleware. The placement strategy is based on a two-level artificial neural network which predicts (1) CPU usage at the first level, and (2) power consumption and performance of a host machine at the second level that uses the predicted CPU usage. The efficacy of iPlace is evaluated in the context of a VM consolidation algorithm that is applied to running virtual machines and host machines in a private cloud.
Year
DOI
Venue
2014
10.1109/ISORC.2014.35
Object/Component/Service-Oriented Real-Time Distributed Computing
Keywords
Field
DocType
cloud computing,contracts,power aware computing,virtual machines,CSP,SLA,VM consolidation algorithm,cloud service providers,cloud-based real-time applications,host machines,iPlace,multitenant cloud systems,performance assurance,performance tradeoffs,performance-aware virtual machine placement technique,power consumption,power management,private cloud,service level agreements,tunable power-aware virtual machine placement technique,cloud computing,deployment algorithm,power and performance tradeoffs,virtual machine placement
Revenue,Middleware,Power management,Service level,Virtual machine,Computer science,CPU time,Real-time computing,Artificial neural network,Cloud computing,Distributed computing
Conference
ISSN
Citations 
PageRank 
1555-0885
3
0.41
References 
Authors
13
3
Name
Order
Citations
PageRank
Faruk Caglar1676.32
Shashi Shekhar243521098.43
Gokhale Aniruddha31645172.16