Performance Management for Cloud Services: Implementation and Evaluation of Schedulers for OpenStack
Abstract: To achieve the best performance out of an IaaS cloud, the resource management layer must be able to distribute the workloads it is tasked with optimally on the underlying infrastructure. A utilization-based scheduler can take advantage of the fact that allocated resources and actual resource usage often differ to make better-informed decisions of where to place future requests. This thesis presents the design, implementation and evaluation of an initial placement controller that uses host utilization data as one of its inputs to help place virtual machines according to one of a number of supported management objectives. The implementation, which builds on top of the OpenStack cloud platform, deals with two different objectives, namely, balanced load and energy efficiency. The thesis also discusses additional objectives and how they can be supported. A testbed and demonstration platform consisting of the aforementioned controller, a synthetic load generator and a monitoring system are built and used during evaluation of the system. Results indicate that the scheduler performs equally well for both objectives using synthetically generated request patterns of both interactive and batch type workloads. A discussion of current limitations of the scheduler and ways to overcome those conclude the thesis. Among the things discussed are how the rate at which host utilization data is collected limits the performance of the scheduler and under which circumstances dynamic placement of virtual machines must be used to complement utilization-based scheduling to avoid the risk of overloading the cloud.
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