Main Page
Deanship
The Dean
Dean's Word
Curriculum Vitae
Contact the Dean
Vision and Mission
Organizational Structure
Vice- Deanship
Vice- Dean
KAU Graduate Studies
Research Services & Courses
Research Services Unit
Important Research for Society
Deanship's Services
FAQs
Research
Staff Directory
Files
Favorite Websites
Deanship Access Map
Graduate Studies Awards
Deanship's Staff
Staff Directory
Files
Researches
Contact us
عربي
English
About
Admission
Academic
Research and Innovations
University Life
E-Services
Search
Deanship of Graduate Studies
Document Details
Document Type
:
Thesis
Document Title
:
Load Balancing for Big Data Computing with Data Locality Awareness
موازنة العبء لحوسبة البيانات الكبيرة مع الوعي بمحلية البيانات
Subject
:
Faculty of Computing and Information Technology
Document Language
:
Arabic
Abstract
:
Load balancing of computational tasks and data plays a critical role in big data systems. Load balancing attempts to optimize the overall computation or system performance, such as throughput, solution (or response) time, and resource usage. This is achieved by an optimum allocation of data and workloads to the available resources. Data locality techniques aim to map tasks to the nodes where the relevant data resides. The two performance objectives (i.e. load balancing and data locality) are usually at odds. There is a need to find optimal strategies to maximize the performance. The aim of this thesis is to develop data locality aware load balancing techniques for big data computing and apply these to practical problems of high significance. A detailed review of the literature is presented to identify major challenges in big data research. These challenges include, among others, load balancing and data locality. A load balancing technique that is also aware of data locality has been developed and is applied to a graph-based road transportation problem. We have modelled the entire US road network data that contains approximately 24 million vertices and 58 million arcs; the specific aim of this research is to identify various points of interests (PoIs) in the regions including living places and healthcare centres. These PoIs are subsequently used to find the shortest paths among them for planning and operations purposes. The relevant algorithms that we have developed are presented in the thesis. The algorithms have been implemented using the Spark big data platform tools on the Aziz supercomputer, a Top500 supercomputer in the world according to the June and November 2015 rankings. The results are collected in terms of the shortest paths and the road networks are visualised as graphs. The performance of the load balancing and data locality awareness techniques is analysed against a varying number of nodes on the Aziz supercomputer and a good speedup has been reported. Conclusions are drawn with directions for future work.
Supervisor
:
Prof. Rashid Mehmood
Thesis Type
:
Master Thesis
Publishing Year
:
1438 AH
2017 AD
Co-Supervisor
:
Dr. Aiiad Albeshri
Added Date
:
Monday, June 5, 2017
Researchers
Researcher Name (Arabic)
Researcher Name (English)
Researcher Type
Dr Grade
Email
ياسر عرفات -
-, Yasir Arfat
Researcher
Master
Files
File Name
Type
Description
40855.pdf
pdf
Back To Researches Page