ABSTRACT
The Cloud computing is a new paradigm for offering computing services via the Internet. Customers can lease infrastructure resources from cloud providers, such as CPU core, memory and disk storage, based on a "pay as you require" model. The approach in this paper is about distributing the resources (storage, processor, memory) of cloud providers to the customers by efficient manner, satisfying parties in terms of providing requirements and guarantee efficient and fair distribution of the resources. The approach system consists of two phases. In the first phase, we will create an interface in order to allow both customers and providers to insert their inputs. The system will allocate customers' demands based on the availability of the provider resources. In the second phase, the system will start to monitor the customers' usage of the resources to determine whether the customers using all the resources that have been allocated to them or did not. Then the system will reallocate the VMs resources that have not been used for a while to other customers. This will lead to reduce the cost and increase the provider profits.
- Alshwaier, A., Youssef, A., & Emam, A. (2012). A new trend for e-learning in KSA using educational clouds. Advanced Computing, 3(1), 81.Google ScholarCross Ref
- Al-Ruithe, M., Benkhelifa, E., & Hameed, K. (2017). Current State of Cloud Computing Adoption---An Empirical Study in Major Public Sector Organizations of Saudi Arabia (KSA). Procedia Comput. Sci, 110, 378--385.Google ScholarCross Ref
- Gade, A. H. (2013). A Survey paper on Cloud Computing and its effective utilization with Virtualization. International Journal of Scientific & Engineering Research, 4(12), 357--363.Google Scholar
- Peter Mell and Tim Grance. The NIST definition of cloud computing. National Institute of Standards and Technology, 53(6):50, 2009. Google ScholarDigital Library
- Angeles, S. (2014). Virtualization vs Cloud Computing: What's the Difference. Business News Daily, January 20.Google Scholar
- Saraswathi, A. T., Kalaashri, Y. R. A., & Padmavathi, S. (2015). Dynamic resource allocation scheme in cloud computing. Procedia Computer Science, 47, 30--36.Google ScholarCross Ref
- Amazon elastic compute cloud. http://amazon.com/ec2.Google Scholar
- Ghobaei-Arani, M., Jabbehdari, S., & Pourmina, M. A. (2018). An autonomic resource provisioning approach for service-based cloud applications: A hybrid approach. Future Generation Computer Systems, 78, 191--210.Google ScholarCross Ref
- M. H. Mohamaddiah, A. Abdullah, S. Subramaniam, and M. Hussin, "A Survey on Resource Allocation and Monitoring in Cloud Computing," Int. J. Mach. Learn. Compute., vol. 4, no. 1, pp. 31--38, Feb. 2014.Google Scholar
- S. S. Manvi and G. Krishna Shyam, "Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey," J. Netw. Compute. Appl., vol. 41, no. 1, pp. 424--440, 2014.Google Scholar
- Xiao, Z., Song, W., & Chen, Q. (2013). Dynamic resource allocation using virtual machines for cloud computing environment. IEEE transactions on parallel and distributed systems, 24(6), 1107--1117. Google ScholarDigital Library
- Saraswathi, AT Kalaashri, YRA Padmavathi, S Procedia (2015) Dynamic resource allocation scheme in cloud computing Computer Science 47 30-36 1877-0509 ElsevierGoogle Scholar
- Shanmuganathan, G., Gulati, A., & Varman, P. (2013, June). Defragmenting the cloud using demand-based resource allocation. In ACM SIGMETRICS Performance Evaluation Review (Vol. 41, No. 1, pp. 67--80). ACM. Google ScholarDigital Library
- Mashayekhy, L., Nejad, M. M., Grosu, D., & Vasilakos, A. V. (2016). An online mechanism for resource allocation and pricing in clouds. IEEE transactions on computers, 65(4), 1172--1184. Google ScholarDigital Library
- Aslanpour, M. S., Ghobaei-Arani, M., & Toosi, A. N. (2017). Auto-scaling web applications in clouds: A cost-aware approach. Journal of Network and Computer Applications, 95, 26--41. Google ScholarDigital Library
- Ghobaei-Arani, M., Jabbehdari, S., & Pourmina, M. A. (2016). An autonomic approach for resource provisioning of cloud services. Cluster Computing, 19(3), 1017--1036. Google ScholarDigital Library
- Asgari, B., Arani, M. G., & Jabbehdari, S. (2016). An effiecient approach for resource auto-scaling in cloud environments. International Journal of Electrical and Computer Engineering (IJECE), 6(5), 2415--2424.Google ScholarCross Ref
- Khorsand, R., Ghobaei-Arani, M., & Ramezanpour, M. (2018). FAHP approach for autonomic resource provisioning of multitier applications in cloud computing environments. Software: Practice and Experience, 48(12), 2147--2173.Google ScholarCross Ref
Index Terms
- An Efficient Allocation of Cloud Computing Resources
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