Loading

Load Balancing and Parallelism in Cloud Computing
Pragati Priyadarshinee1, Pragya Jain2
1Pragati Priyadarshinee, Assistant Professor, Chaitanya Bharathi Institute of Technology, Hyderabad, India.
2Dr. Pragya Jain, incharge of the “grid computing” and “cloud computing” at IIT Delhi, India.
Manuscript received on May 17, 2012. | Revised Manuscript received on June 22, 2012. | Manuscript published on June 30, 2012. | PP: 486-489 | Volume-1 Issue-5, June 2012. | Retrieval Number: E0540061512/2012©BEIESP

Open Access | Ethics and Policies | Cite
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Large-scale heterogeneous distributed computing environments (such as Computational Grids and Clouds) offer the promise of access to a vast amount of computing resources at a relatively low cost. In order to ease the application development and deployment on such complex environments, high-level parallel programming languages exist that need to be supported by sophisticated runtime systems. The anticipated uptake of Cloud computing, built on well-established research in Web Services, networks, utility computing, distributed computing and virtualization, will bring many advantages in cost, flexibility and availability for service users. These benefits are expected to further drive the demand for Cloud services, increasing both the Cloud’s customer base and the scale of Cloud installations. This has implications for many technical issues in Service Oriented Architectures and Internet of Services (IoS)-type applications; including fault tolerance, high availability and scalability. Central to these issues is the establishment of effective load balancing techniques. It is clear the scale and complexity of these systems makes centralized assignment of jobs to specific servers infeasible; requiring an effective distributed solution. 
Keywords: Load Balancing, Parallelism, SOA, Virtualization.