Intelligent Load Balancing in Cloud Computer Systems

Sliwko, L. 2019. Intelligent Load Balancing in Cloud Computer Systems. PhD thesis University of Westminster Department of Computer Science and Engineering, College of Design, Creative and Digital Industries

TitleIntelligent Load Balancing in Cloud Computer Systems
TypePhD thesis
AuthorsSliwko, L.
Abstract

Cloud computing is an established technology allowing users to share resources on a large scale, never before seen in IT history. A cloud system connects multiple individual servers in order to process related tasks in several environments at the same time. Clouds are typically more cost-effective than single computers of comparable computing performance. The sheer physical size of the system itself means that thousands of machines may be involved. The focus of this research was to design a strategy to dynamically allocate tasks without overloading Cloud nodes which would result in system stability being maintained at minimum cost. This research has added the following new contributions to the state of knowledge: (i) a novel taxonomy and categorisation of three classes of schedulers, namely OS-level, Cluster and Big Data, which highlight their unique evolution and underline their different objectives; (ii) an abstract model of cloud resources utilisation is specified, including multiple types of resources and consideration of task migration costs; (iii) a virtual machine live migration was experimented with in order to create a formula which estimates the network traffic generated by this process; (iv) a high-fidelity Cloud workload simulator, based on a month-long workload traces from Google's computing cells, was created; (v) two possible approaches to resource management were proposed and examined in the practical part of the manuscript: the centralised metaheuristic load balancer and the decentralised agent-based system. The project involved extensive experiments run on the University of Westminster HPC cluster, and the promising results are presented together with detailed discussions and a conclusion.

Year2019
Filephd_thesis_final.pdf
Publication dates
PublishedJan 2019

Related outputs

Decentralised Workload Scheduler for Resource Allocation in Computational Clusters
Sliwko, L. and Getov, Vladimir 2018. Decentralised Workload Scheduler for Resource Allocation in Computational Clusters. London University of Westminster.

Transfer Cost of Virtual Machine Live Migration in Cloud Systems
Sliwko, L. and Getov, Vladimir 2017. Transfer Cost of Virtual Machine Live Migration in Cloud Systems. London

AGOCS – Accurate Google Cloud Simulator Framework
Sliwko, L. and Getov, Vladimir 2016. AGOCS – Accurate Google Cloud Simulator Framework. 16th IEEE International Conference on Scalable Computing and Communications. Toulouse, France 18 - 21 Jul 2016 IEEE . doi:10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0095

A Meta-Heuristic Load Balancer for Cloud Computing Systems
Sliwko, L. and Getov, Vladimir 2015. A Meta-Heuristic Load Balancer for Cloud Computing Systems. 2015 IEEE 39th Annual Computer Software and Applications Conference (COMPSAC). Taichung, Taiwan 01 Jul 2015 IEEE . doi:10.1109/COMPSAC.2015.223

Workload Schedulers - Genesis, Algorithms and Comparisons
Sliwko, L. and Getov, Vladimir 2015. Workload Schedulers - Genesis, Algorithms and Comparisons. International Journal of Computer Science and Software Engineering. 4 (6), pp. 141-155.

Distributed Agent-Based Load Balancer for Cloud Computing
Sliwko, L., Getov, Vladimir and Bolotov, A. 2015. Distributed Agent-Based Load Balancer for Cloud Computing. The 22nd Workshop on Automated Reasoning: Bridging the Gap between Theory and Practice. University of Birmingham 09 Apr 2015 Automated Reasoning Workshop.

Permalink - https://westminsterresearch.westminster.ac.uk/item/qq4w7/intelligent-load-balancing-in-cloud-computer-systems


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