• Vykuntarao Simma
• Kasa Madhavi

Cloud computing provides large pool of shared computing resources. This new computing paradigm enables large concurrent applications that need access to computing resources on demand. The partitions of the workloads of these concurrent applications are executed by the compute nodes in the cloud. Historically, the application designers target their applications for specific hardware and environments. However, such applications need to have different design with cloud computing which enables different platforms. Self-tuning is essential for concurrent applications in cloud platforms. It is very challenging problem to design such self-tuning applications that can split workload and achieve high cost efficiency in cloud. Many researchers contributed towards self-tuning cloud applications. Of late Rajan and Thain proposed a methodology that enables adaptive self-tuning split-map-merge applications to have cost-effective processing. the optimization will reduce system overhead. However, it could be improved further with the division of workload with the intention of using multiple cores for execution. The proposed system focuses on building application that is capable of self-modelling and self-tuning besides having ability to divide workload in order to utilize multiple cores for execution. A new algorithm is proposed in order to estimate the need for workload divisions and the number of cores for the execution. The proposed application will be more cost-efficient and a prototype is built to demonstrate the cost effective system.

Cloud computing, scientific applications, Map Reduce, resource provisioning, data partitioning

"Cost Effective Self-Tuning Cloud Application With Multiple Cores", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 6, page no.780-783, June-2018, Available :http://www.jetir.org/papers/JETIR1806358.pdf

"Cost Effective Self-Tuning Cloud Application With Multiple Cores", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 6, page no. pp780-783, June-2018, Available at : http://www.jetir.org/papers/JETIR1806358.pdf