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Performance Evaluation of Weighted Greedy Algorithm in Resource Management

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  • Fri, 01/12/2018 - 9:00am - 12:00pm

Performance Evaluation of Weighted Greedy Algorithm in Resource Management

MSc Thesis Defense by:

Harjeet Singh

Date:  Friday, January 12, 2018
Time:  9:00 am – 12:00 pm
Location: Essex Hall, 122

Abstract: Set covering is a well-studied classical problem with many applications across different fields. More recent work on this problem has taken into account the parallel computing architecture, the datasets at scale, the properties of the datasets, etc. Within the context of web crawling where the data follow the lognormal distribution, a weighted greedy algorithm has been proposed in the literature and demonstrated to outperform the traditional one. In the present work, we evaluate the performance of the weighted greedy algorithm using an open-source dataset in the context of resource management. The data is sampled from a given roadmap with 1.9 million of nodes. Our research includes three different cost definitions, representing major types of the cost considered in this setting e.g. cost of the building, cost of the location, etc. We also consider the different coverage radius to model possible parameters in the application. Our experiment results show that weighted greedy algorithm outperforms the greedy algorithm by 8% in average for all three different cost definitions.

Thesis Committee:
Internal Reader:  Dr. Jianguo Lu      
External Reader:  Dr. Huapeng Wu  
Advisor:  Dr. Jessica Chen
Chair: Dr. Imran Ahmad

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