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Scheduling and deployment of large-scale applications on Cloud platforms

Adrian Muresan 1, 2
Abstract : Infrastructure as a service (IaaS) Cloud platforms are increasingly used in the IT industry. IaaS platforms are providers of virtual resources from a catalogue of predefined types. Improvements in virtualization technology make it possible to create and destroy virtual machines on the fly, with a low overhead. As a result, the great benefit of IaaS platforms is the ability to scale a virtual platform on the fly, while only paying for the used resources. From a research point of view, IaaS platforms raise new questions in terms of making efficient virtual platform scaling decisions and then efficiently scheduling applications on dynamic platforms. The current thesis is a step forward towards exploring and answering these questions. The first contribution of the current work is focused on resource management. We have worked on the topic of automatically scaling cloud client applications to meet changing platform usage. There have been various studies showing self-similarities in web platform traffic which implies the existence of usage patterns that may or may not be periodical. We have developed an automatic platform scaling strategy that predicted platform usage by identifying non-periodic usage patterns and extrapolating future platform usage based on them. Next we have focused on extending an existing grid platform with on-demand resources from an IaaS platform. We have developed an extension to the DIET (Distributed Interactive Engineering Toolkit) middleware, that uses a virtual market based approach to perform resource allocation. Each user is given a sum of virtual currency that he will use for running his tasks. This mechanism help in ensuring fair platform sharing between users. The third and final contribution targets application management for IaaS platforms. We have studied and developed an allocation strategy for budget-constrained workflow applications that target IaaS Cloud platforms. The workflow abstraction is very common amongst scientific applications. It is easy to find examples in any field from bioinformatics to geology. In this work we have considered a general model of workflow applications that comprise parallel tasks and permit non-deterministic transitions. We have elaborated two budget-constrained allocation strategies for this type of workflow. The problem is a bi-criteria optimization problem as we are optimizing both budget and workflow makespan. This work has been practically validated by implementing it on top of the Nimbus open source cloud platform and the DIET MADAG workflow engine. This is being tested with a cosmological simulation workflow application called RAMSES. This is a parallel MPI application that, as part of this work, has been ported for execution on dynamic virtual platforms. Both theoretical simulations and practical experiments have shown encouraging results and improvements.
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Submitted on : Thursday, February 21, 2013 - 3:43:43 PM
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  • HAL Id : tel-00786475, version 2


Adrian Muresan. Scheduling and deployment of large-scale applications on Cloud platforms. Other [cs.OH]. Ecole normale supérieure de lyon - ENS LYON, 2012. English. ⟨NNT : 2012ENSL0780⟩. ⟨tel-00786475v2⟩



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