Scheduling and deployment of large-scale applications on Cloud platforms
type: Dataset
description: 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 applicati…
identifier: NNT: 2012ENSL0780
issued: 2026-05-14T06:01:36.117852
language: en
modified: 2026-05-14T06:01:36.117858
contact point: Organization
  fn: CCSD
keyword: auto-scaling
keyword: cloud-computing
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-ohcomputer-science-csother-csoh
keyword: ordonnancement-dapplications-de-type-workflow
keyword: redimensionnement-automatique
keyword: theses
keyword: workflow-scheduling
landing page: https://theses.hal.science/tel-00786475

Scheduling and deployment of large-scale applications on Cloud platforms
type: Distribution
format: HTML
issued: 2026-05-14T06:01:36.129314
modified: 2026-05-14T06:01:36.100623
access URL: https://theses.hal.science/tel-00786475

test_moissonnage_selune
type: Agent