EXTENSION OF AN OPEN SOURCE RESOURCE MANAGEMENT TOOL FOR HETEROGENEOUS CLOUD DATA CENTERS: IMPLEMENTATION AND EVALUATION

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Taha_Dogan_Thesis_Submitted.pdf

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2022-2-11

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Doğan, Taha

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Cloud Computing is enabled by the virtualization of computing resources to realize users' requests of virtual machines (VMs) and data processing in the scope of Infrastructure as a Service (IaaS) and Software as a Service (SaaS) respectively. The current heterogeneous cloud data centers incorporate hardware accelerators in addition to the conventional servers to offer these services more efficiently. It is an important research problem to allocate heterogeneous physical computing resources to a mixture of IaaS and SaaS requests. On the one hand, the requirements of the requests should be satisfied. On the other hand, resource use and power consumption should be efficient. OpenStack is a popular cloud management platform that focuses on IaaS. It collects the user requests and accordingly instantiates VMs in the cloud data center. The default resource allocation of OpenStack only aims for fulfilling the user request without any further optimizations regarding efficient resource use. This thesis develops, implements and evaluates an extension of OpenStack to address the resource management and allocation of heterogeneous cloud data centers to address the requirements that we list above. The default filter-based resource allocation method of OpenStack only targets fulfilling the request requirements without any optimization concern. To this end, we integrate an ILP-based cloud resource allocation method with power minimization objectives in the most recent version of OpenStack that was available at the time of writing this thesis. We develop a software architecture that interfaces the ILP solver which provides all the required messaging according to the defined APIs of OpenStack. The entire extended implementation is realized in a laboratory set-up that features cloud servers, a 40 Gbps network and FPGA cards with hardware accelerators that represent a heterogeneous cloud data center.

Subject Keywords

Cloud computing, Hardware accelerator, Resource management, OpenStack, Nova

URI

https://hdl.handle.net/11511/96353

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

T. Doğan, “EXTENSION OF AN OPEN SOURCE RESOURCE MANAGEMENT TOOL FOR HETEROGENEOUS CLOUD DATA CENTERS: IMPLEMENTATION AND EVALUATION,” M.S. - Master of Science, Middle East Technical University, 2022.