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Squeezing out the cloud via profit-maximizing resource allocation policies
Abstract We study the problem of maximizing the aveWe study the problem of maximizing the average hourly profit earned by a Software-as-a-Service (SaaS) provider who runs a software service on behalf of a customer using servers rented from an Infrastructure-as-a-Service (IaaS) provider. The SaaS provider earns a fee per successful transaction and incurs costs pro-portional to the number of server-hours it uses. A number of resource allocation policies for this or similar problems have been proposed in previous work. However, to the best of our knowledge, these policies have not been comparatively evaluated in a cloud environment. This paper reports on an empirical evaluation of three policies using a replica of Wikipedia deployed on the Amazon EC2 cloud. Experimental results show that a policy based on a solution to an optimization problem derived from the SaaS provider's utility function outperforms well-known heuristics that have been proposed for similar problems. It is also shown that all three policies outperform a "reactive" allocation approach based on Amazon's auto-scaling feature.ch based on Amazon's auto-scaling feature.
Abstractsub We study the problem of maximizing the aveWe study the problem of maximizing the average hourly profit earned by a Software-as-a-Service (SaaS) provider who runs a software service on behalf of a customer using servers rented from an Infrastructure-as-a-Service (IaaS) provider. The SaaS provider earns a fee per successful transaction and incurs costs pro-portional to the number of server-hours it uses. A number of resource allocation policies for this or similar problems have been proposed in previous work. However, to the best of our knowledge, these policies have not been comparatively evaluated in a cloud environment. This paper reports on an empirical evaluation of three policies using a replica of Wikipedia deployed on the Amazon EC2 cloud. Experimental results show that a policy based on a solution to an optimization problem derived from the SaaS provider's utility function outperforms well-known heuristics that have been proposed for similar problems. It is also shown that all three policies outperform a "reactive" allocation approach based on Amazon's auto-scaling feature.ch based on Amazon's auto-scaling feature.
Bibtextype inproceedings  +
Doi 10.1109/MASCOTS.2012.13  +
Has author Mazzucco M. + , Vasar M. + , Dumas M. +
Has extra keyword Allocation approach + , Amazon ec2 + , Empirical evaluations + , Optimization problems + , Performance + , Profit maximization + , Resource allocation policy + , Software services + , Software-as-a-Service + , Utility functions + , Wikipedia + , Cloud computing + , Computer simulation + , Profitability + , Resource allocation + , Telecommunication systems + , Computer resource management +
Has keyword Cloud computing + , Performance + , Profit maximization + , Resource allocation +
Isbn 9780769547930  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 19–28  +
Published in Proceedings of the 2012 IEEE 20th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2012 +
Title Squeezing out the cloud via profit-maximizing resource allocation policies +
Type conference paper  +
Year 2012 +
Creation dateThis property is a special property in this wiki. 8 November 2014 05:04:44  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 8 November 2014 05:04:44  +
DateThis property is a special property in this wiki. 2012  +
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