Cloudlet scheduling in cloud computing is one of the most issues that face the cloud computing environment. This paper presents a new efficient approach, called Traveling Salesman Approach for Cloudlet Scheduling (TSACS), to solve the cloudlet-scheduling problem. The main idea is to convert the cloudlet-scheduling problem into an instance of the Traveling Salesman Problem (TSP) and then apply one of the TSP solution strategies to solve the problem. The proposed approach consists of three phases: clustering phase, converting phase, and assignment phase. In the clustering phase, the proposed approach converts the large size cloudlet-scheduling problem into a small size cluster-scheduling problem to minimize computation time complexity of the proposed approach. In the converting phase, the approach forms the cluster-scheduling problem as an instance of the TSP. In the assignment phase, the approach schedules the clusters into the available virtual machines by using the nearest neighbor algorithm. The proposed approach is evaluated by using the CloudSim and the results are compared with that obtained by the most recent algorithms. The results show that the proposed approach enhances the overall system performance in terms of schedule length, balancing degree, and time complexity. In addition, the proposed TSACS overcomes the oscillation problem of the existing cloudlet-scheduling algorithms.
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Marinescu, D.C.: Cloud Computing: Theory and Practice. Morgan Kaufmann, Burlington (2017)
Kotas, C., Naughton, T., Imam, N.: A comparison of Amazon Web Services and Microsoft Azure cloud platforms for high performance computing. In: Proceedings of the IEEE International Conference on Consumer Electronics (ICCE), pp. 1–4 (2018)
Mei, L., Chan, W.K., Tse, T.H.: A tale of clouds: paradigm comparisons and some thoughts on research issues. In: Proceedings of the IEEE Asia-Pacific Services Computing Conference (APSCC’08), pp. 464–469 (2008)
Mathew, T., Sekaran, K.C., Jose, J.: Study and analysis of various task scheduling algorithms in the cloud computing environment. In: Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 658–664 (2014)
Kar, I., Parida, R.R., Das, H.: Energy aware scheduling using genetic algorithm in cloud data centers. In: Proceedings of the International Conference in Electrical, Electronics, and Optimization Techniques (ICEEOT), pp. 3545–3550 (2016)
Panda, S.K., Gupta, I., Jana, P.K.: Allocation-aware task scheduling for heterogeneous multi-cloud systems. Procedia Comput. Sci. 50, 176–184 (2015)
Yuan, H., Bi, J., Tan, W., Li, B.H.: Temporal task scheduling with constrained service delay for profit maximization in hybrid clouds. IEEE Trans. Autom. Sci. Eng. 14(1), 337–348 (2017)
Nasr, A.A., El-Bahnasawy, N.A., El-Sayed, A.: Task Scheduling optimization in heterogeneous distributed systems. Int. J. Comput. Appl. 107(4), 5–12 (2014)
Zhong, Z., Chen, K., Zhai, X., Zhou, S.: Virtual machine-based task scheduling algorithm in a cloud computing environment. Tsinghua Sci. Technol. 21(6), 660–667 (2016)
Kimpan, W., Kruekaew, B.: Heuristic task scheduling with artificial bee colony algorithm for virtual machines. In: Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems, pp. 281–286 (2016)
Nasr, A.A., El-Bahnasawy, N.A., El-Sayed, A.: Performance enhancement of scheduling algorithm in heterogeneous distributed computing systems. Int. J. Adv. Comput. Sci. Appl. 6(5), 88–96 (2015)
Nasr, A., El-Bahnasawy, N.A., El-Sayed, A.: A new duplication task scheduling algorithm in heterogeneous distributed computing systems. Bull. Electr. Eng. Inform. 5(3), 373–382 (2016)
Pavithra, B., Ranjana, R.: A comparative study on performance of energy efficient load balancing techniques in cloud. In: International Conference in Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 1192–1196 (2016)
Chatterjee, T., Ojha, V.K., Adhikari, M., Banerjee, S., Biswas, U., Snášel, V.: Design and implementation of an improved datacenter broker policy to improve the QoS of a cloud. In: Proceedings of the Fifth International Conference on Innovations in Bio-inspired Computing and Applications (IBICA), pp. 281–290 (2014)
Tsai, C.W., Rodrigues, J.J.P.C.: Metaheuristic scheduling for cloud: a survey. IEEE Syst. J. 8(1), 279–291 (2014)
Singh, S., Kalra, M.: Scheduling of independent tasks in cloud computing using modified genetic algorithm. In: International Conference in Computational Intelligence and Communication Networks (CICN), pp. 565–569 (2014)
Gu, J., Hu, J., Zhao, T., Sun, G.: A new resource scheduling strategy based on genetic algorithm in cloud computing environment. J. Comput. 7(1), 42–52 (2012)
Wu, Z., Xing, S., Cai, S., Xiao, Z., Ming, Z.: A genetic-ant-colony hybrid algorithm for task scheduling in cloud system. In: International Conference on Smart Computing and Communication, pp. 183–193 (2016)
Keshanchi, B., Souri, A., Navimipour, N.J.: An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. J. Syst. Softw. 124, 1–21 (2017)
Dam, S., Mandal, G., Dasgupta, K., Dutta, P.: An ant-colony-based meta-heuristic approach for load balancing in cloud computing. In: Khalid, S. (ed.) Applied Computational Intelligence and Soft Computing in Engineering, pp. 204–232. IGI Global, Hershey (2018)
Al-maamari, A., Omara, F.A.: Task scheduling using PSO algorithm in cloud computing environments. Int. J. Grid Distrib. Comput. 8(5), 245–256 (2015)
Xu, Y., Li, K., He, L., Zhang, L., Li, K.: A hybrid chemical reaction optimization scheme for task scheduling on heterogeneous computing systems. IEEE Trans. Parallel Distrib. Syst. 26(12), 3208–3222 (2015)
Zhou, Y., Luo, Q., Chen, H., He, A., Wu, J.: A discrete invasive weed optimization algorithm for solving traveling salesman problem. Neurocomputing 15, 1227–1236 (2015)
Ma, Z., Liu, L., Sukhatme, G.S.: An adaptive k-opt method for solving traveling salesman problem. In: IEEE 55th Conference in Decision and Control (CDC), pp. 6537–6543 (2016)
Sahana, S.K., Jain, A.: An improved modular hybrid ant colony approach for solving traveling salesman problem. GSTF J. Comput. (JoC) 1(2), 123–127 (2018)
Borker, S.B., Markeshan, S., Suvarna, S., Nayak, M.V.: A hybrid approach to solve travelling salesman problem in map reduce framework using parallel genetic algorithm. Imp. J. Interdiscip. Res. 2(5), 1264–1269 (2016)
Calheiros, R.N., Ranjan, R., Beloglazov, A., Rose, C.A.F.D., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)
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Nasr, A.A., El-Bahnasawy, N.A., Attiya, G. et al. Using the TSP Solution Strategy for Cloudlet Scheduling in Cloud Computing. J Netw Syst Manage 27, 366–387 (2019). https://doi.org/10.1007/s10922-018-9469-9
- Nearest neighbor algorithm
- Load balancing
- Oscillation problem