Problem Statement Dynamic hosting platforms allow companies to support simple, yet efficient communications between applications. SQL databases can be massive in terms of the amount of data that is stored on them. SQL queries are resource intensive and consume CPU cycles (Linthicum, 2010). This can make system response times too slow to be useful. This research will explore the problem of how to create more efficient cloud computing resource allocation so that it can support the requirements of large SQL databases.

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Literature Review
The literature found that there are several issues that need to be resolved with cloud computing. The first is that large dynamic databases are big energy consumers (Beloglazov,, Abawajy, and Buyya, 2012). Energy consumption increases their cost of operation. Finding ways to make them consume less energy will help to improve their efficiency (Beloglazov,, Abawajy, and Buyya, 2012). Finding resource allocation algorithms that are more effective will help to increase speed so that dynamic hosting platforms can support larger databases and make them more environmentally friendly and cost effective. CloudSim is tool that allows the simulation of cloud environments and the evaluation of resource allocation (Calheiros, Ranjan, eloglazov, De Rose, and Buyya, 2011). The goal of resource allocation is to maximize the number of requests that can be processed, thus allowing greater amounts of data to flow through the system (Sudeep and Guruprasad, 2014). SQl environment can easily create a scarcity of resources in the system, creating a need to find more efficient methods of resource allocation Vinothina, Dean, and Ganapathi, 2012). These the most important findings of the literature review.

This research study will compare different resource allocation strategies that are currently being used to evaluate which ones are the best choices for using cloud computing to manage SQL databases. The study will examine the effect of adding or removing a caching server, set up time on power and response time, and to explore the effect adding removing a cashing server on response time These questions will be studies using simulations in a quantitative approach.


  • Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future generation computer systems. 28(5), 755-768.
  • Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., & Buyya, R. (2011). ClouldSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 41(1), 23-50.
  • Linthicum, D. (2010, May 17). SQL and relational databases: They’re not right for the cloud. InfoWorld. Retrieved from–they-re-not-right-for-the-cloud.html

  • Sudeepa, R. & Guruprasad, H. (2014). Resource Allocation in Cloud Computing. International Journal of Modern Communication Technology. 2 (4): 19-21.

  • Vinothina, V., Dean, S. & Ganapathi, P. (2012). A Survey on Resource Allocation Strategies in Cloud Computing. International Journal of Advanced Computer Science and Applications. 3(6): 97-104.