A Hybrid Swarm Particle Optimization Algorithm for Task Scheduling in Cloud Computing
Abstract
Today, cloud computing experts seek internet-based service providing to share resources using service providing techniques. This environment provides users with an image of abundant resources. The present paper recommends a combination of particle swarm optimization algorithm and simulated annealing algorithm to obtain an improvement in the performance of task scheduling to resources considering the available bandwidth allocated to each virtual machine. The performance of the proposed algorithm is investigated by the use of the Cloudsim Simulator. Research results show that the proposed algorithm outperforms the Swarm Particle Optimization (SPO), bat, and raven roosting optimism algorithms in terms of task execution time, response time, and performance efficiency.
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References
Ahani, A., Nilashi, M., Ibrahim, O., Sanzogni, L., & Weaven, S. (2019). Market segmentation and travel choice prediction in Spa hotels through TripAdvisor’s online reviews. International Journal of Hospitality Management, 80, 52-77.
Ahani, A., Nilashi, M., Yadegaridehkordi, E., Sanzogni, L., Tarik, A. R., Knox, K., . . . Ibrahim, O. (2019). Revealing customers’ satisfaction and preferences through online review analysis: The case of Canary Islands hotels. Journal of Retailing and Consumer Services, 51, 331-343.
Akbari, E., Buntat, Z., Nilashi, M., Afroozeh, A., Farhang, Y., & Zeinalinezhad, A. (2016). ISVR modeling of an interferon gamma (IFN-γ) biosensor based on graphene. Analytical methods, 8(39), 7217-7224.
Asadi, S., Nilashi, M., Husin, A. R. C., & Yadegaridehkordi, E. (2017). Customers perspectives on adoption of cloud computing in banking sector. Information Technology and Management, 18(4), 305-330.
Awad, N., Ali, M., Liang, J., Qu, B., & Suganthan, P. (2016). Problem definitions and evaluation criteria for the cec 2017 special session and competition on single objective real-parameter. Nanyang Technological University, Singapore, Tech. Rep.
Beloglazov, A., Buyya, R., Lee, Y. C., & Zomaya, A. (2011). A taxonomy and survey of energy-efficient data centers and cloud computing systems Advances in computers (Vol. 82, pp. 47-111): Elsevier.
Buttazzo, G. C. (2011). Hard real-time computing systems: predictable scheduling algorithms and applications (Vol. 24): Springer Science & Business Media.
Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., & Buyya, R. (2011). CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience, 41(1), 23-50.
Chapin, S. J., Katramatos, D., Karpovich, J., & Grimshaw, A. S. (1999). The legion resource management system. Paper presented at the Workshop on Job Scheduling Strategies for Parallel Processing.
Choudhary, M., & Peddoju, S. K. (2012). A dynamic optimization algorithm for task scheduling in cloud environment. International Journal of Engineering Research and Applications (IJERA), 2(3), 2564-2568.
Kalra, M., & Singh, S. (2015). A review of metaheuristic scheduling techniques in cloud computing. Egyptian informatics journal, 16(3), 275-295.
Karlin, S. (1955). The structure of dynamic programing models. Naval Research Logistics Quarterly, 2(4), 285-294.
Masdari, M., Salehi, F., Jalali, M., & Bidaki, M. (2017). A survey of PSO-based scheduling algorithms in cloud computing. Journal of Network and Systems Management, 25(1), 122-158.
Mohammed, F., Ibrahim, O., Nilashi, M., & Alzurqa, E. (2017). Cloud computing adoption model for e-government implementation. Information Development, 33(3), 303-323.
Nilashi, M., Ahani, A., Esfahani, M. D., Yadegaridehkordi, E., Samad, S., Ibrahim, O., . . . Akbari, E. (2019). Preference learning for eco-friendly hotels recommendation: A multi-criteria collaborative filtering approach. Journal of Cleaner Production, 215, 767-783.
Nilashi, M., Ahmadi, H., Manaf, A. A., Rashid, T. A., Samad, S., Shahmoradi, L., . . . Akbari, E. (2020). Coronary Heart Disease Diagnosis Through Self-Organizing Map and Fuzzy Support Vector Machine with Incremental Updates. International Journal of Fuzzy Systems, 1-13.
Nilashi, M., Ahmadi, H., Shahmoradi, L., Ibrahim, O., & Akbari, E. (2019). A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique. Journal of Infection and Public Health, 12(1), 13-20.
Nilashi, M., Ahmadi, H., Sheikhtaheri, A., Naemi, R., Alotaibi, R., Alarood, A. A., . . . Zhao, J. (2020). Remote Tracking of Parkinson's Disease Progression Using Ensembles of Deep Belief Network and Self-Organizing Map. Expert Systems with Applications, 113562.
Nilashi, M., Bagherifard, K., Rahmani, M., & Rafe, V. (2017). A recommender system for tourism industry using cluster ensemble and prediction machine learning techniques. Computers & industrial engineering, 109, 357-368.
Nilashi, M., Bin Ibrahim, O., Mardani, A., Ahani, A., & Jusoh, A. (2018). A soft computing approach for diabetes disease classification. Health Informatics Journal, 24(4), 379-393.
Nilashi, M., Cavallaro, F., Mardani, A., Zavadskas, E. K., Samad, S., & Ibrahim, O. (2018). Measuring country sustainability performance using ensembles of neuro-fuzzy technique. Sustainability, 10(8), 2707.
Nilashi, M., Ibrahim, O., Samad, S., Ahmadi, H., Shahmoradi, L., & Akbari, E. (2019). An analytical method for measuring the Parkinson’s disease progression: A case on a Parkinson’s telemonitoring dataset. Measurement, 136, 545-557.
Nilashi, M., Jannach, D., bin Ibrahim, O., & Ithnin, N. (2015). Clustering-and regression-based multi-criteria collaborative filtering with incremental updates. Information Sciences, 293, 235-250.
Nilashi, M., Rupani, P. F., Rupani, M. M., Kamyab, H., Shao, W., Ahmadi, H., . . . Aljojo, N. (2019). Measuring sustainability through ecological sustainability and human sustainability: A machine learning approach. Journal of Cleaner Production, 240, 118162.
Nilashi, M., Samad, S., Yadegaridehkordi, E., Alizadeh, A., Akbari, E., & Ibrahim, O. (2019). Early Detection of Diabetic Retinopathy Using Ensemble Learning Approach. Journal of Soft Computing and Decision Support Systems, 6(2), 12-17.
Rimal, B. P., Choi, E., & Lumb, I. (2009). A taxonomy and survey of cloud computing systems. Paper presented at the 2009 Fifth International Joint Conference on INC, IMS and IDC.
Wang, L., Von Laszewski, G., Younge, A., He, X., Kunze, M., Tao, J., & Fu, C. (2010). Cloud computing: a perspective study. New Generation Computing, 28(2), 137-146.
Yadegaridehkordi, E., Nilashi, M., Shuib, L., Nasir, M. H. N. B. M., Asadi, S., Samad, S., & Awang, N. F. (2020). The impact of big data on firm performance in hotel industry. Electronic Commerce Research and Applications, 40, 100921.
Yadegaridehkordi, E., Shuib, L., Nilashi, M., & Asadi, S. (2019). Decision to adopt online collaborative learning tools in higher education: A case of top Malaysian universities. Education and Information Technologies, 24(1), 79-102.
Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications, 1(1), 7-18.
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