Show simple item record

dc.contributor.authorDuggan, Jim
dc.contributor.authorHowley, Enda
dc.contributor.authorBarrett, Enda
dc.date.accessioned2014-01-21T16:40:14Z
dc.date.available2014-01-21T16:40:14Z
dc.date.issued2012-05-30
dc.identifier.citationBarrett, E; Howley, E; Duggan, J (2012) 'Applying reinforcement learning towards automating resource allocation and application scalability in the cloud'. Concurrency And Computation: Practice And Experience (To Appear), .en_US
dc.identifier.issn1532-0634
dc.identifier.urihttp://hdl.handle.net/10379/3988
dc.descriptionJournal articleen_US
dc.description.abstractPublic Infrastructure as a Service (IaaS) clouds such as Amazon, GoGrid and Rackspace deliver computational resources by means of virtualisation technologies. These technologies allow multiple independent virtual machines to reside in apparent isolation on the same physical host. Dynamically scaling applications running on IaaS clouds can lead to varied and unpredictable results because of the performance interference effects associated with co-located virtual machines. Determining appropriate scaling policies in a dynamic non-stationary environment is non-trivial. One principle advantage exhibited by IaaS clouds over their traditional hosting counterparts is the ability to scale resources on-demand. However, a problem arises concerning resource allocation as to which resources should be added and removed when the underlying performance of the resource is in a constant state of flux. Decision theoretic frameworks such as Markov Decision Processes are particularly suited to decision making under uncertainty. By applying a temporal difference, reinforcement learning algorithm known as Q-learning, optimal scaling policies can be determined. Additionally, reinforcement learning techniques typically suffer from curse of dimensionality problems, where the state space grows exponentially with each additional state variable. To address this challenge, we also present a novel parallel Q-learning approach aimed at reducing the time taken to determine optimal policies whilst learning online.en_US
dc.description.sponsorshipScience Foundation Irelanden_US
dc.formatapplication/pdfen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofConcurrency And Computation: Practice And Experience (To Appear)en
dc.subjectReinforcement learningen_US
dc.subjectCloud computingen_US
dc.subjectResource scalingen_US
dc.titleApplying reinforcement learning towards automating resource allocation and application scalability in the clouden_US
dc.typeArticleen_US
dc.date.updated2013-09-18T22:07:53Z
dc.identifier.doi10.1002/cpe.2864
dc.local.publishedsourcehttp://dx.doi.org/10.1002/cpe.2864en_US
dc.description.peer-reviewedpeer-reviewed
dc.internal.rssid3186358
dc.local.contactJames Duggan, Dept. Of Information Technology, I.T. Building, Nui Galway. 3336 Email: james.duggan@nuigalway.ie
dc.local.copyrightcheckedYes
dc.local.versionSUBMITTED
nui.item.downloads2509


Files in this item

Attribution-NonCommercial-NoDerivs 3.0 Ireland
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. Please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.

The following license files are associated with this item:

Thumbnail

This item appears in the following Collection(s)

Show simple item record