Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.799386
Title: Virtual machine consolidation in cloud data centres using a parameter-based placement strategy
Author: Mosa, Abdelkhalik
ISNI:       0000 0004 8504 5784
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2019
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Abstract:
Cloud computing enables cloud providers to offer computing infrastructure as a service in the form of virtual machines (VMs). VM placement is a vital component of any cloud management platform (e.g. OpenStack). VM placement is the process of mapping VMs to physical machines (PMs) efficiently according to the cloud provider's objectives and placement constraints. So far, any VM placement solution adopts either a reservation-based or demand-based VM placement strategies. Reservation-based VM placement allocates VMs to PMs according to the reserved VM size regardless of the actual workload. If a VM is making use of only a fraction of its reservation, then this leads to PM underutilization, which wastes energy and results in more costs. In contrast, demand-based VM placement consolidates VMs based on the actual workloads demand which may lead to better utilization. However, it may incur more service level agreement violations (SLAVs) resulting from overloaded PMs and/or VM migrations among PMs due to workload fluctuations. This thesis aims to introduce a novel VM placement strategy to control the tradeoff between PM utilization and SLAVs that will allow cloud providers to explore the whole space of VM placement options that range from demand-based to reservation-based, with the help of a single parameter. The thesis first presents our strategy called parameter-based VM placement using a static parameter. Then it introduces various algorithms that adjust this parameter continuously at run-time in a way that a provider can maintain the number of SLAVs below a certain (predetermined) threshold while using the smallest possible number of PMs. These algorithms fine-tune the parameter both at the cloud data center level and at the VM level using reactive and hybrid (reactive and proactive) approaches. An empirical evaluation using CloudSim confirms that the proposed parameter-based VM placement solution offers more flexibility in choosing between different tradeoffs.
Supervisor: Gurd, John ; Sakellariou, Rizos Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.799386  DOI: Not available
Keywords: Predicting resource utilization ; Reactive Virtual Machine Reallocation ; Dynamic Virtual Machine Placement ; SLA-aware Virtual Machine Placement ; Energy Efficient Cloud Data Centres ; Demand-based Virtual Machine Placement ; Parameter-based Virtual Machine Placement ; Virtual Machine Consolidation ; Virtual Machine Placement ; Reservation-based Virtual Machine Placement
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