blue banner

Graduate Projects - Details

Computer Science Program

Project ID: 525
Author: Srinivas Thota
Project Title: Fusion-based Load-aware Resource Allocation on Cloud Infrastructure
Semester: 3 2017
Committe Chair: Dr. Dulal Kar
Committee Member 1: Dr. Ajay Katangur
Committee Member 2: -
Project Description: One of the main challenges posed to cloud computing is limited availability of resources for extensive amount of computation required. In the literature, numerous attempts have been made to optimize resource utilization with an objective to im- prove eciency of cloud. As the number of requests for cloud services increases, it becomes dicult for the system to balance the load and serve user requests in a stip- ulated time. Load Balancing is a well known NP-Complete Problem. This project proposes a fusion-based Load aware resource allocation algorithm (FLA) approach that exploits three existing load balancing algorithms: traditional Round-robin al- gorithm (RR), basic genetic algorithm (BGA) and priority-based genetic algorithm (PGA), with an objective to improve the performance of the system. The idea of fusion lies in considering the variable amount of user requests to the cloud system. The proposed algorithm is intended to use Round-robin algorithm (RR) when there is relatively light load, basic genetic algorithm (BGA) when there is an intermediate load, and priority-based genetic algorithm (PGA) when the system encounters heavy load. Moreover, the goal of this project is also to determine the optimum threshold values for the load to be considered as light, intermediate and heavy. The optimum threshold values will be computed using several parameters such as the number of incoming user requests, CPU, RAM and bandwidth of VMs on the cloud. Utiliza- tion of CPU plays a vital role in the VM load. Hence, CPU used by the VM can be considered as a VM load. The simulations will be carried out using CloudSim 3.0.
Project URL:   525.pdf
© Texas A&M University-Corpus Christi • 6300 Ocean Drive, Corpus Christi, Texas 78412 • 361-825-5700