Thesis of Bo Zhang

Self-optimization of Infrastructure and Platform Resources in Cloud Computing

Elasticity is considered as an important solution to handle the performance issues in scalable distributed system. However, most of the researches of elasticity only concern the provisioning and de-provisioning resources in automatic ways, but always ignore the resource utilization of provisioned resources. This might lead to resource leaks while provisioning redundant resources, thereby causing unnecessary expenditure. To avoid the resource leaks and redundant resources, my research therefore focus on how to maximize resource utilization by self resource management. In this thesis, relevant to diverse problems of resource usage and allocation in different layers, I propose two resource management approaches corresponding to infrastructure and platform, respectively. To overcome infrastructure limitation, I propose CloudGC as middleware service which aims to free occupied resources by recycling idle VMs. In platform-layer, a self-balancing approach is introduced to adjust Hadoop configuration at runtime, thereby avoiding memory loss and dynamically optimizing Hadoop performance. Finally, this thesis concerns rapid deployment of service which is also an issue of elasticity. A new tool, named « hadoop-benchmark », applies docker to accelerate the installation of Hadoop cluster and to provide a set of docker images which contain several well-known Hadoop benchmarks. The assessments show that these approaches and tool can well achieve resource management and self-optimization in various layers, and then facilitate the elasticity of infrastructure and platform in scalable platform, such as Cloud computing.


- Directeurs de thèse : Lionel SEINTURIER, Romain ROUVOY - Rapporteurs : Gaël Thomas, Patricia Stolf - Examinateurs : Noël De Palma, Fabrice Huet, Jean-Christophe Routier

Thesis of the team Spirals defended on 12/12/2016