Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.732869
Title: Smart distributed processing technologies for hedge fund management
Author: Thayalakumar, Sinnathurai
ISNI:       0000 0004 6494 6110
Awarding Body: University of Hertfordshire
Current Institution: University of Hertfordshire
Date of Award: 2017
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Abstract:
Distributed processing cluster design using commodity hardware and software has proven to be a technological breakthrough in the field of parallel and distributed computing. The research presented herein is the original investigation on distributed processing using hybrid processing clusters to improve the calculation efficiency of the compute-intensive applications. This has opened a new frontier in affordable supercomputing that can be utilised by businesses and industries at various levels. Distributed processing that uses commodity computer clusters has become extremely popular over recent years, particularly among university research groups and research organisations. The research work discussed herein addresses a bespoke-oriented design and implementation of highly specific and different types of distributed processing clusters with applied load balancing techniques that are well suited for particular business requirements. The research was performed in four phases, which are cohesively interconnected, to find a suitable solution using a new type of distributed processing approaches. The first phase is an implementation of a bespoke-type distributed processing cluster using an existing network of workstations as a calculation cluster based on a loosely coupled distributed process system design that has improved calculation efficiency of certain legacy applications. This approach has demonstrated how to design an innovative, cost-effective, and efficient way to utilise a workstation cluster for distributed processing. The second phase is to improve the calculation efficiency of the distributed processing system; a new type of load balancing system is designed to incorporate multiple processing devices. The load balancing system incorporates hardware, software and application related parameters to assigned calculation tasks to each processing devices accordingly. Three types of load balancing methods are tested, static, dynamic and hybrid, which each of them has their own advantages, and all three of them have further improved the calculation efficiency of the distributed processing system. The third phase is to facilitate the company to improve the batch processing application calculation time, and two separate dedicated calculation clusters are built using small form factor (SFF) computers and PCs as separate peer-to-peer (P2P) network based calculation clusters. Multiple batch processing applications were tested on theses clusters, and the results have shown consistent calculation time improvement across all the applications tested. In addition, dedicated clusters are built using SFF computers with reduced power consumption, small cluster size, and comparatively low cost to suit particular business needs. The fourth phase incorporates all the processing devices available in the company as a hybrid calculation cluster utilises various type of servers, workstations, and SFF computers to form a high-throughput distributed processing system that consolidates multiple calculations clusters. These clusters can be utilised as multiple mutually exclusive multiple clusters or combined as a single cluster depending on the applications used. The test results show considerable calculation time improvements by using consolidated calculation cluster in conjunction with rule-based load balancing techniques. The main design concept of the system is based on the original design that uses first principle methods and utilises existing LAN and separate P2P network infrastructures, hardware, and software. Tests and investigations conducted show promising results where the company's legacy applications can be modified and implemented with different types of distributed processing clusters to achieve calculation and processing efficiency for various applications within the company. The test results have confirmed the expected calculation time improvements in controlled environments and show that it is feasible to design and develop a bespoke-type dedicated distributed processing cluster using existing hardware, software, and low-cost SFF computers. Furthermore, a combination of bespoke distributed processing system with appropriate load balancing algorithms has shown considerable calculation time improvements for various legacy and bespoke applications. Hence, the bespoke design is better suited to provide a solution for the calculation of time improvements for critical problems currently faced by the sponsoring company.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Eng.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.732869  DOI: Not available
Keywords: Distributed processing ; Parallel processing ; Cluster computing ; Workstation cluster ; Server cluster ; Smart devices ; Bespoke design ; Load balancing ; Adaptive control ; Hedge fund technology
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