Service Adaptive Broking Mechanism Using MROSP Algorithm
Cloud computing is an effort in delivering resources as a service. It represents a shift away from the era where products were purchased, to computing as a service that is delivered to consumers over the internet from large-scale data centers or clouds. As cloud computing is gaining popularity in the IT industry, academia appeared to be working in parallel for the rapid developments in this field. In a cloud computing environment now a days, the role of service provider is divided into two: Cloud Broker who manage cloud platforms and lease resources according to a usage-based pricing model, and service providers, who rent resources from one or many infrastructure providers to serve the end users. The aim of this research work is to deal with the scheduling of the requests on the basis of some parameters that we have identified to achieve the best optimal paths or cloud service provider allotment to the users. We have used rough set theory to generate the mathematical model. The algorithm is implemented in the cloud simulator CLOUDSIM in which cloudlets, datacenters, cloud brokers are created to perform the algorithms. Finally, we created a GUI for the user convenience so that both Cloud Service Provider and users can themselves analyze each other’s performance. We have reused some inbuilt packages of Cloudsim net beans to simulate the process.
KeywordsCloud Computing Cloud Service Providers Rough Set Theory Datacenters Cloudsim
Unable to display preview. Download preview PDF.
- 1.Tiwari, A., Nagaraju, A., Mahrishi, M.: An Optimized Scheduling Algorithm for Cloud Broker Using Cost Adaptive Modeling. In: 3rd IEEE International Advanced Computing Conference (2013)Google Scholar
- 3.Komorowski, J.: Rough Sets: A Tutorial. Department of Computer and Information Science Norwegian University of Science and Technology (NTNU) 7034 Trondheim, NorwayGoogle Scholar
- 4.Calheiros, R.N.: CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services. In: Grid Computing and Distributed Systems (GRIDS) Laboratory Department of Computer Science and Software Engineering. The University of Melbourne, AustraliaGoogle Scholar
- 5.Pawlak, Z.: Rough set theory and its applications. Journal of Telecommunication and Information Technology 3 (2002)Google Scholar
- 6.Sharma, A.K.: The Role and Use of Data Mining Techniques for Intrusion Detection Systems. International Journal of Research in IT and Management 2(2), 425–430 (2012)Google Scholar
- 7.Mahrishi, M., Sharma, D.K., Shrotriya, A.: Globally Recorded binary encoded Domain Compression algorithm in Column Oriented Databases. Global Journals of Computer Science and Technology 11(23), 27–30 (2011)Google Scholar
- 8.Tiwari, A., Tiwari, A.K., Saini, H.C., Sharma, A.K., Yadav, A.K.: A Cloud Computing using Rough set Theory for Cloud Service Parameters through Ontology in Cloud Simulator. In: ACITY (2013)Google Scholar
- 9.Nair, T.R.G., Sharma, V.: A Pragmatic Scheduling Approach for Creating Optimal Periority of jobs with Business Values in Cloud Computing. In: ACC (2012)Google Scholar
- 10.Tiwari, A.: Adaptive Cost Model for Cloud Broker application Rough Set and Fuzzy argumentation Technique. In: ACITE Sponsored National Seminar on Recent Trends in Embedded Systems (2014)Google Scholar
- 11.Mahrishi, M., Nagaraju, A.: Optimizing Cloud Service Provider Scheduling by Rough Set model. In: International Conference on Cloud Computing Techno and Management (2012)Google Scholar
- 12.Mahrishi, M., Nagaraju, A.: Rating Based Formulation for Scheduling of Cloud Service Providers. In: National Conference on Emerging Trends in ITGoogle Scholar