Heterogeneous Resource Allocation in Datacenters

Date
2020-04-24
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Abstract

Virtualized 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.

Description
Degree
Master of Science
Type
Thesis
Keywords
Data Center, Resource Allocation, Heterogeneous Resources, QoS, Fairness, Envy Freedom, Sharing Incentive, Bandwidth Allocation, Scalability, Placement, Virtual Machines, Virtual Clusters, Connectivity Constraints
Citation

Parvez Khan, Mohammad Shahriar. "Heterogeneous Resource Allocation in Datacenters." (2020) Master’s Thesis, Rice University. https://hdl.handle.net/1911/108393.

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