Varman, Peter J2020-04-272020-04-272020-052020-04-24May 2020Parvez Khan, Mohammad Shahriar. "Heterogeneous Resource Allocation in Datacenters." (2020) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/108393">https://hdl.handle.net/1911/108393</a>.https://hdl.handle.net/1911/108393Virtualized datacenters are considerably cost-effective and convenient to their clients who routinely deploy clusters of communicating Virtual Machines (VMs) on physical infrastructures to create a distributed ensemble of servers for Web applications. These applications require multiple resources such as compute, memory, storage and network bandwidth for execution, motivating the need for fair allocation policies. Here, we present a new model for allocating multiple resources among clients, and our results show that we can obtain significantly better utilization than existing approaches, while provably maintaining good fairness properties like Envy Freedom and Sharing Incentive. We also look at datacenter optimization from an opposite standpoint, i.e., VM placement. It is necessary to place a high number of virtual servers per physical host to maximize the benefits of a shared infrastructure. We present a framework to automate the placement of VMs in several scenarios alongside the corresponding ILP optimization models and numerical results.application/pdfengCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.Data CenterResource AllocationHeterogeneous ResourcesQoSFairnessEnvy FreedomSharing IncentiveBandwidth AllocationScalabilityPlacementVirtual MachinesVirtual ClustersConnectivity ConstraintsThesis2020-04-27