Abstract
Fog Computing continues to extend its usage by solving cloud computing challenges about Internet of Things (IoT). Fog nodes as a processing resource, can perform tasks generated by IoT devices. IoT as a client are concerned with the timely execution of their tasks and also lower cost services, and on the other hand, they are looking for a secured task execution. In this paper, we propose a multi-objective simulated annealing (MOSA) algorithm to allocate tasks securely on the fog and cloud nodes based on deadline constraints. The Goal Programming Approach (GPA) is applied to find a compromised solution which will satisfy multiple goals. Also, regarding the distribution of IoT tasks between fog and cloud nodes, a new goal is created called access level and scheduling based on client demand. Simulation results in four low, normal, medium, and high load scenarios showing that the proposed algorithm is on average 9.5% more efficient in terms of service delay time, 87% in terms of access level control and 49.8% in terms of deadline compared to multi-objective Particle Swarm Optimization (MOPSO), multi-objective Tabu Search (MOTS), and multi-objective Moth-Flame optimization (MOMF). Also, in terms of service cost, it has obtained acceptable results close to the average of other algorithms.
Similar content being viewed by others
References
Bonomi, F., Milito, R., Natarajan, P., Zhu, J.: Fog Computing: A Platform for Internet of Things and Analytics. In Studies in Computational Intelligence, pp. 169–186. Springer, Cham (2014)
Perera, C., Qin, Y., Estrella, J.C., Reiff-Marganiec, S., Vasilakos, A.V.: Fog computing for sustainable smart cities: a survey. ACM Comput Surv 50, 1–43 (2017)
Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. MCC’12—Proc 1st ACM Mob Cloud Comput Work (2012). https://doi.org/10.1145/2342509.2342513
Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R.H., Morrow, M.J., Polakos, P.A.: A comprehensive survey on fog computing: state-of-the-art and research challenges. IEEE Commun Surv Tutorials 20, 416–464 (2018)
Najafizadeh, A., Salajegheh, A., Rahmani, A.M., Sahafi, A.: Task scheduling in fog computing: a survey. J Adv Comput Res 11, 1–10 (2020)
Li, C., Bai, J., Tang, J.: Joint optimization of data placement and scheduling for improving user experience in edge computing. J Parallel Distrib Comput 125, 93–105 (2019)
Hosseinioun, P., Kheirabadi, M., Kamel Tabbakh, S.R., Ghaemi, R.: A new energy-aware tasks scheduling approach in fog computing using hybrid meta-heuristic algorithm. J Parallel Distrib Comput 143, 88–96 (2020)
Elashri, S., Azim, A.: Energy-efficient offloading of real-time tasks using cloud computing. Cluster Comput 23, 3273–3288 (2020)
Huang, X., Li, C., Chen, H., An, D.: Task scheduling in cloud computing using particle swarm optimization with time varying inertia weight strategies. Cluster Comput 23, 1137–1147 (2020)
Masadeh, R., Alsharman, N., Sharieh, A., Mahafzah, B.A., Abdulrahman, A.: Task scheduling on cloud computing based on sea lion optimization algorithm. Int J Web Inf Syst 17, 99–116 (2021)
Iranmanesh, A., Naji, H.R.: DCHG-TS: a deadline-constrained and cost-effective hybrid genetic algorithm for scientific workflow scheduling in cloud computing. Clust Comput 24, 667–681 (2021)
Mohammad Taisir Masadeh, R., Abdel-Aziz Sharieh, A., Mahafzah, B.A., Masadeh, R., Sharieh, A.: Humpback whale optimization algorithm based on vocal behavior for task scheduling in cloud computing. Int J Adv Sci Technol 13 (2019)
Bitam, S., Zeadally, S., Mellouk, A.: Fog computing job scheduling optimization based on bees swarm. Enterp Inf Syst 12, 373–397 (2018)
Zhu, C., Tao, J., Pastor, G., Xiao, Y., Ji, Y., Zhou, Q., Li, Y., Yla-Jaaski, A.: Folo: latency and quality optimized task allocation in vehicular fog computing. IEEE Internet Things J (2018). https://doi.org/10.1109/JIOT.2018.2875520
Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Trans Comput 65, 3702–3712 (2016)
Xu. R., Wang. Y., Cheng. Y., Zhu. Y., Xie. Y., Sani. A.S., Yuan. D.: Improved Particle Swarm Optimization Based Workflow Scheduling in Cloud-Fog Environment. In: Lect. Notes Bus. Inf. Process. Springer, Berlin, pp. 337–347 (2019)
Wang, X., Veeravalli, B., Rana, O.F.: An optimal task-scheduling strategy for large-scale astronomical workloads using in-transit computation model. Int J Comput Intell Syst 11, 600 (2018)
Ghobaei-Arani, M., Souri, A., Safara, F., Norouzi, M.: An efficient task scheduling approach using moth-flame optimization algorithm for cyber-physical system applications in fog computing. Trans Emerg Telecommun Technol 31, 1–17 (2020)
Ni, L., Zhang, J., Jiang, C., Yan, C., Yu, K.: Resource allocation strategy in fog computing based on priced timed petri nets. IEEE Internet Things J 4, 1216–1228 (2017)
Pham, X.-Q., Man, N.D., Tri, N.D.T., Thai, N.Q., Huh, E.-N.: A cost- and performance-effective approach for task scheduling based on collaboration between cloud and fog computing. Int J Distrib Sens Networks 13, 155014771774207 (2017)
Yang, Y., Zhao, S., Zhang, W., Chen, Y., Luo, X., Wang, J.: DEBTS: delay energy balanced task scheduling in homogeneous fog networks. IEEE Internet Things J 5, 2094–2106 (2018)
Liu, Z., Zhang, J., Li, Y., Bai, L., Ji, Y.: Joint jobs scheduling and lightpath provisioning in fog computing micro datacenter networks. J Opt Commun Netw 10, B152 (2018)
Wang, X., Ning, Z., Wang, L.: Offloading in internet of vehicles: a fog-enabled real-time traffic management system. IEEE Trans Ind Informatics 14, 4568–4578 (2018)
Deng, R., Lu, R., Lai, C., Luan, T.H., Liang, H.: Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet Things J 3, 1171–1181 (2016)
Liu, L., Qi, D., Zhou, N., Wu, Y.: A task scheduling algorithm based on classification mining in fog computing environment. Wirel Commun Mob Comput 2018, 1–11 (2018)
Wang, J., Li, D.: Task scheduling based on a hybrid heuristic algorithm for smart production line with fog computing. Sensors 19, 1023 (2019)
Wan, J., Chen, B., Wang, S., Xia, M., Li, D., Liu, C.: Fog computing for energy-aware load balancing and scheduling in smart factory. IEEE Trans Ind Informatics 14, 4548–4556 (2018)
Yin, L., Luo, J., Luo, H.: Tasks scheduling and resource allocation in fog computing based on containers for smart manufacturing. IEEE Trans Ind Informatics 14, 4712–4721 (2018)
Barbosa, H.J., Lemonge, A.C.: An adaptive penalty method for genetic algorithms in constrained optimization problems. Front Evol Robot (2008). https://doi.org/10.5772/5446
Charnes, A., Cooper, W.W., Ferguson, R.O.: Optimal estimation of executive compensation by linear programming. Manage Sci 1, 138–151 (1955)
Tamiz, M., Jones, D., Romero, C.: Goal programming for decision making: An overview of the current state-of-the-art. Eur J Oper Res (1998). https://doi.org/10.1016/S0377-2217(97)00317-2
Clayton, E.R., Weber, W.E., Taylor, B.W.: A goal programming approach to the optimization of multi response simulation models. A I I E Trans 14, 282–287 (1982)
Masud, A.S., Hwang, C.L.: Interactive sequential goal programming. J Oper Res Soc 32, 391–400 (1981)
Williams, K.B., Charnes, A., Cooper, W.W.: Management models and industrial applications of linear programming. OR 13, 274 (1962)
Ignizio, J.P.: A review of goal programming: a tool for multiobjective analysis. J Oper Res Soc 29, 1109–1119 (1978)
Ho, R.M.Y., Ignizio, J.P.: Goal programming and extensions. Oper Res Q 28, 478 (1977)
Sayyouh, M.H.: Goal programming: A new tool for optimization in petroleum reservoir history matching. Appl Math Model 5, 223–226 (1981)
Deb, K.: Nonlinear goal programming using multi-objective genetic algorithms. J Oper Res Soc 52, 291–302 (2001)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Manage Sci (1983). https://doi.org/10.1126/science.220.4598.671
Coello Coello, C.A., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8, 256–279 (2004)
Corne, D., Jerram, N., Knowles, J., Oates, M., Martin, J.: PESA-II: Region-based Selection in Evolutionary Multiobjective Optimization. Proc Genet Evol Comput Conf, pp. 283–290 (2001)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: MHS’95. Proc. Sixth Int Symp Micro Mach Hum Sci IEEE, pp. 39–43 (1995)
Téllez, N., Jimeno, M., Salazar, A., Nino-Ruiz, E.D.: A Tabu search method for load balancing in fog computing. Int J Artif Intell 16, 18 (2018)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Najafizadeh, A., Salajegheh, A., Rahmani, A.M. et al. Multi-objective Task Scheduling in cloud-fog computing using goal programming approach. Cluster Comput 25, 141–165 (2022). https://doi.org/10.1007/s10586-021-03371-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-021-03371-8