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Adaptive provisioning of heterogeneous resources for processing chains

Kollenstart, M. (2017) Adaptive provisioning of heterogeneous resources for processing chains. Master's Thesis / Essay, Computing Science.

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Efficient utilisation of resources plays an important role in the performance of batch-based task processing. In the cases where different types of resources are used within the same application, it is hard to achieve good utilisation of all the different types of resources. By adaptively altering the size of the available resources for all the different resource types the overall utilisation of resources can be improved. Eliminating the necessity of doing trial runs to determine the desired ratio between resources or having knowledge on the different steps on beforehand. With the current developments in cloud infrastructure, enabling dynamic clusters of resources for applications, this can improve throughput and decrease lead times in the field of computing science. In this thesis a solution is proposed that tries to come up with the right calculations necessary to create an adaptive system that provisions the right resources at run-time. The solution aims to provide a generic algorithm to estimate the desired ratios of instances processing tasks as well as ratios of the resources that are used by these instances. To verify the proposed solution a reference framework is provided that tries to eliminate underutilisation of virtual machines in the cloud, where functionally different virtual machines are used in a CPU intensive calculation job. Experiments are conducted based on use-case in which the probability of pipeline failures is determined based on the settlement of soils. These experiments show that the solution is well capable of eliminating large amounts of underutilisation. Resulting in increased throughput and lower lead times.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Computing Science
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 08:33
Last Modified: 15 Feb 2018 08:33

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