Weighted Service Broker Algorithm in Cloud Environment
Abstract
Cloud Computing refers to on-demand delivery of IT resources and applications via the Internet with pay-as-you-go pricing. The cloud service provider provides these services through datacenters with high configured servers. Therefore, many factors are studied such as response time, cost, the number of requests, and choosing the best datacenter (DC) to serve the customer request. Cloud service broker acts as a mediator between customer and service provider and it is responsible for choosing the datacenter. There are many policies to determine this selection such as Service Proximity-Based Routing, which selects the earliest region, which has minimum communication delay and lowest network latency. However, this policy adopts the random selection of datacenter when the closest region includes more than one datacenter; it does not consider any factor such as the cost of the datacenter, and its processing time. Therefore, a weighted based approach aware service brokering policy is proposed to avoid the random selection of datacenters by grading datacenters according to their virtual machine (VM) cost, data transfer cost, and the number of VMs included. The proposed algorithm has been tested and evaluated using Cloud Analyst software, and the results show a significant reduction in the total cost, processing time, and response time.
Full Text:
PDFReferences
Aazam, M., Huh, E.N., 2014. Fog computing and smart gateway based communication for cloud of things, in: 2014 International Conference on Future Internet of Things and Cloud, pp. 464–470.
Abed-Alguni, B.H. and Alawad, N.A., 2021. Distributed Grey Wolf Optimizer for scheduling of workflow applications in cloud environments. Applied Soft Computing, 102, p.107113.
Ahmed, A.S., 2012. Proximity-Based Routing Policy for Service Brokering in Cloud Computing.”. International Journal of Engineering Research and Applications.2(2) 453, 12.
Aljuhani, A., Alhubaishy, A., Rahmani, M.K.I. and Alzahrani, A.A., 2023. Light-Weighted Decision Support Framework for Selecting Cloud Service Providers. Computers, Materials & Continua, 74(2).
Alwada’n, T., Al-Tamimi, A.K., Mohammad, A.H., Salem, M. and Muhammad, Y., 2023. Dynamic congestion management system for cloud service broker. International Journal of Electrical and Computer Engineering.
Aruna, M., Bhanu, D., Karthik, S., 2017. Allocating resources in cloud using CloudAnalyst, in: 2017 International Conference on Intelligent Computing and Control (I2C2), pp. 1–5.
Beloglazov, A., et al., 2016. CloudSim: A Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services. Cloud Computing and Distributed Systems (CLOUDS) Laboratory, Department of Computer Science and Software Engineering, the University of Melbourne, Australia.
Chauhan, S.S., Pilli, E.S. and Joshi, R.C., 2021. BSS: a brokering model for service selection using integrated weighting approach in cloud environment. Journal of Cloud Computing, 10(1), pp.1-14.
Hafiz, J.Y., 2015. Efficient Load Balancing Algorithm in Cloud Computing. Efficient Load Balancing Algorithm in Cloud Computing.
Kapgate, D., 2014. Improved round robin algorithm for data center selection in cloud computing. International Journal of Engineering Sciences & Research Technology 3, 686–691.
Kaur, A., Kaur, B., Singh, P., Devgan, M.S. and Toor, H.K., 2020. Load balancing optimization based on deep learning approach in cloud environment. International Journal of Information Technology and Computer Science, 12(3), pp.8-18.
Kishor, K., Thapar, V., 2014. An efficient service broker policy for Cloud computing environment. International Journal of Computer Science Trends and Technology (IJCST) 2.
Limbani, D., Oza, B., 2012a. A proposed service broker policy for data center selection in cloud environment with implementation. International Journal of Computer Technology & Applications 3, 1082–1087.
Limbani, D., Oza, B., 2012b. A proposed service broker strategy in cloudanalyst for cost-effective data center selection. International Journal of Engineering Research and Applications, India 2, 793–797.
Mahalle, H.S., Tayde, S., Kaveri, P.R., 2015. Implementing service broker policies in cloud computing environment, in: 2015 International Conference on Communication Networks (ICCN), pp. 186–190.
Manasrah, A.M., Smadi, T., Almomani, A., 2017. A variable service broker routing policy for data center selection in cloud analyst. Journal of King Saud University-Computer and Information Sciences 29, 365–377.
Mehraj, S. and Banday, M.T., 2022. A Dynamic Weighted Averaging Technique for Trust Assessment in Cloud Computing. International Journal of Cloud Applications and Computing (IJCAC), 12(1), pp.1-21.
Mishra, R.K., Bhukya, S.N., 2014. Service broker algorithm for cloud-analyst. International Journal of Computer Science and Information Technologies 5, 3957–3962.
Mishra, R.K., Kumar, S., Naik, B.S., 2014b. Priority based Round-Robin service broker algorithm for Cloud-Analyst, in: 2014 IEEE International Advance Computing Conference (IACC), pp. 878–881.
Nishad, L.S., Kumar, S., Bola, S.K., Beniwal, S., Pareek, A., 2016. Round robin selection of datacenter simulation technique cloudsim and cloud analsyt architecture and making it efficient by using load balanc- ing technique, in: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 2901–2905.
Patel, H., Patel, R., 2015. Cloud analyst: an insight of service broker policy. International Journal of Advanced Research in Computer and Communication Engineering 4, 122–127.
Radi, M., 2015. Efficient service broker policy for large-scale cloud environments. arXiv preprint arXiv:1503.03460.
Rekha, P.M., Dakshayini, M., 2018. Dynamic Cost-Load Aware Service Broker Load Balancing in Virtual- ization Environment. Procedia Computer Science 132, 744–751.
Sheikhani, L., Chang, Y., Gu, C., Luo, F., 2017. Modifying broker policy for better response time in datacenters. IEEE.
Shen, H., 2017. RIAL: resource intensity aware load balancing in clouds. IEEE Transactions. Cloud Computing.
Wickremasinghe, B., Buyya, R., 2009. CloudAnalyst: A CloudSim-based tool for modelling and analysis of largescale cloud computing environments. MEDC project report 22, 433–659.
DOI: https://doi.org/10.31449/inf.v48i7.5617
This work is licensed under a Creative Commons Attribution 3.0 License.