Skip to Main Content
Skip Nav Destination
ASME Press Select Proceedings
International Conference on Advanced Computer Theory and Engineering, 5th (ICACTE 2012)
Editor
Xie Yi
Xie Yi
Search for other works by this author on:
ISBN:
9780791860045
No. of Pages:
938
Publisher:
ASME Press
Publication date:
2012

In this paper a classification of MapReduce workloads(CPU bound or I/O bound) based on the characteristics of MapReduce jobs is proposed. Also the packing of mixes of CPU and I/O jobs on to nodes is formulated as an optimization problem based on total cost of migration, down time and operational cost in any dynamic MapReduce base cloud computing environment. A genetic algorithm is used to solve this optimization problem since the packing problem is NP-hard. The load balancer framework is implemented by using the proposed genetic algorithm. Experimental results show that proposed model outperforms existing models in terms of minimizing the number of active nodes, reduction in energy consumption and improving pool utilization.

1. Introduction
2. Optimization Problem
3. Design of a Genetic Algorithm
4. Implementation
5. Conclusions and Future Scope
References
This content is only available via PDF.
You do not currently have access to this chapter.
Close Modal

or Create an Account

Close Modal
Close Modal