Energy E cient Virtual Machine Placement Algorithms in OpenStack Neat

dc.contributor.advisorSurafel, Lemma (PhD)
dc.contributor.authorFikru, Feleke
dc.date.accessioned2018-12-21T09:01:54Z
dc.date.accessioned2023-11-04T15:13:17Z
dc.date.available2018-12-21T09:01:54Z
dc.date.available2023-11-04T15:13:17Z
dc.date.issued2018-11-13
dc.description.abstractCloud computing provides a computing capability through the Internet. It enables organizations or individuals to have a computing power without deploying and maintaining their own Information Technology (IT) infrastructure. As a cloud is realized on a vast scale data-center, it consumes an enormous amount of energy. Several research have been conducted on consolidating Virtual Machines (VMs), logical computing machines that are hosted in servers, to minimize energy consumption. Among the proposed solutions OpenStack Neat is notable for its practicality. OpenStack Neat is an open-source VM consolidation framework that can seamlessly integrate to OpenStack, one of the most common and widely used open-source cloud management tool. The framework has components for deciding when to migrate VMs and selecting suitable hosts for the VMs (VM placement). The VM placement algorithm of OpenStack Neat is called Modi ed BestFit Decreasing (MBFD). MBFD is based on a heuristic that handles only minimizing the number of servers. The heuristic is not only less energy e cient but also increases Service Level Agreement (SLA) violation and consequently cause more VM migrations. To improve the energy e ciency, we propose VM placement algorithms based on both bin-packing heuristics and servers' power e ciency. In addition, we introduce a new binpacking heuristic called a Medium-Fit (MF) to reduce SLA violation and VM migrations. To evaluate the performance of the proposed algorithms we have conducted experiments using CloudSim on three cloud data-center scenarios: homogeneous, heterogeneous and default. The workloads that run in the data-center scenarios are generated from traces of PlanetLab and Bitbrains clouds. The results of the experiment show up-to 67% improvement in energy consumption and up-to 78% and 46% reduction in SLA violation and amount of VM migrations, respectively. Moreover, all improvements are statistically signi cant with signi cance level of 0.01.en_US
dc.identifier.urihttp://etd.aau.edu.et/handle/123456789/15233
dc.language.isoen_USen_US
dc.publisherAAUen_US
dc.subjectvirtual machine consolidationen_US
dc.subjectvirtual machine placementen_US
dc.subjectbin packingen_US
dc.subjectOpenStacken_US
dc.subjectOpenStack Neaten_US
dc.titleEnergy E cient Virtual Machine Placement Algorithms in OpenStack Neaten_US
dc.typeThesisen_US

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