Energy-aware and Reliable Service Placement of IoT applications on Fog Computing Platforms by Utilizing Whale Optimization Algorithm
محورهای موضوعی : Cloud, Cluster, Grid and P2P Computing
Yaser Ramzanpoor
1
(Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran)
Mirsaeid Hosseini Shirvani
2
(Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran)
Mehdi GolSorkhTabar
3
(Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran)
کلید واژه: Internet of Things (IoT), Service Placement Problem (SPP), Fog Computing, Whale Optimization Algorithm (WOA),
چکیده مقاله :
Fog computing is known as a new computing technology where it covers cloud computing shortcomings in term of delay. This is a potential for running IoT applications containing multiple services taking benefit of closeness to fog nodes near to devices where the data are sensed. This article formulates service placement issue into an optimization problem with total power consumption minimization inclination. It considers resource utilization and traffic transmission between different services as two prominent factors of power consumption, once they are placed on different fog nodes. On the other hand, placing all of the services on the single fog node owing to power reduction reduces system reliability because of one point of failure phenomenon. In the proposed optimization model, reliability limitations are considered as constraints of stated problem. To solve this combinatorial problem, an energy-aware reliable service placement algorithm based on whale optimization algorithm (ER-SPA-WOA) is proposed. The suggested algorithm was validated in different circumstances. The results reported from simulations prove the dominance of proposed algorithm in comparison with counterpart state-of-the-arts.
[1] Azimi Sh, Pahl C, Hosseini Shirvani M. Particle Swarm Optimization for Performance Management in Multi-cluster IoT Edge Architectures. International cloud computing conference CLOSER. 2020; 328-337. http://dx.doi.org/10.5220/0009391203280337.
[2] Karimi M. B, Isazadeh A, Rahmani A. M. QoS-aware service composition in cloud computing using data mining techniques and genetic algorithm. The Journal of Supercomputing. 2017; 73(4):1387–1415. https://doi.org/10.1007/s11227-016-1814-8.
[3] Hosseini Shirvani M. Bi-objective web service composition problem in multi-cloud environment: a bi-objective time-varying particle swarm optimisation algorithm. J Exp Theor Artif Intell. 2020; 33(2):179-202. https://doi.org/10.1080/0952813X.2020.1725652.
[4] Hosseini Shirvani M, Babazadeh Gorji A. Optimisation of automatic web services composition using genetic algorithm. Int J Cloud Comput. 2020; 9(4):397–411. https://dx.doi.org/10.1504/IJCC.2020.112313
[5] Ramzanpoor Y, Hosseini Shirvani M. Multi-objective QoS-aware Optimization for Deployment of IoT Applications on Cloud and Fog Computing Infrastructure. Cluster Computing. 2021; Under Review.
[6] Ramzanpoor, Y., Hosseini Shirvani, M. & Golsorkhtabaramiri, M. Multi-objective fault-tolerant optimization algorithm for deployment of IoT applications on fog computing infrastructure. Complex Intell. Syst. (2021). https://doi.org/10.1007/s40747-021-00368-z.
[7] Farzai S, Hosseini Shirvani M, Rabbani M, Multi-Objective Communication-Aware Optimization for Virtual Machine Placement in Cloud Datacenters. Sustainable Computing: Informatics and Systems. 2020; 28. https://doi.org/10.1016/j.suscom.2020.100374.
[8] Foukalas F. Cognitive IoT platform for fog computing industrial applications. Computers and Electrical Engineering. 2020; 87: 1-13. https://doi.org/10.1016/j.compeleceng.2020.106770
[9] OpenFog. An OpenFog Architecture Overview. https://www.iiconsortium.org/pdf/OpenFog_ Reference_Architecture_2_09_17.pdf. Accessed February, 2017.
[10] Brogi A, Forti A. QoS-aware Deployment of IoT Applications Through the Fog. IEEE Internet of Things Journal. 2017; 4:1185-1192. https://doi.org/10.1109/JIOT.2017.2701408.
[11] Taneja M, Davy A. Resource-aware Placement of IoT Application Modules in Fog-Cloud Computing Paradigm. in Proc. of the IFIP/IEEE Symposium on Integrated Network and Service Management. IM ’15. IEEE. 2017; 1222–1228. https://doi.org/10.23919/INM.2017.7987464.
[12] Li F, V ̈ogler M, Claeßens M, Dustdar S. Towards automated iot application deployment by a cloud-based approach. in 6th International Conference on Service-Oriented Computing and Applications. IEEE. 2013; 61–68. https://doi.org/10.1109/SOCA.2013.12.
[13] Mahmud R, Ramamohanarao K, Buyya R. Latency-aware application module Management for fog Computing Environments. ACM Transactions on Internet Technology. 2018; 1–21. https://doi.org/10.1145/3186592.
[14] Vögler M, Schleicher J. M, Inzinger C, Dustdar S. DIANE - Dynamic IoT Application Deployment. IEEE International Conference on Mobile Services. 2015; 298-305. https://doi.org/10.1109/MobServ.2015.49.
[15] Yousefpour A, Patil A, Ishigaki G, Kim I, Wang X, Cankaya H. C, Zhang Q, Xie W, Jue J. P. Fogplan: A lightweight qos-aware dynamic fog service provisioning framework. IEEE Internet of Things Journal. 2019; 6(3): 5080 – 5096. https://doi.org/10.1109/JIOT.2019.2896311.
[16] Canali C, Lancellotti R. Gasp: Genetic algorithms for service placement in fog computing systems. Algorithms. 2019; 12(10): 201. https://doi.org/10.3390/a12100201.
[17] Azizi S, Khosroabadi F, Shojafar M. A priority-based service placement policy for fog-cloud computing systems. Computational Methods for Differential Equations. 2019; 7(4):521–534.
[18] Guerrero C, Lera I, Juiz C. Evaluation and efficiency comparison of evolutionary algorithms for service placement optimization in fog architectures. Future Generation Computer Systems. 2019; 97: 131–144. https://doi.org/10.1016/j.future.2019.02.056.
[19] Hosseini Shirvani M. Web service composition in multi-cloud environment: a bi-objective genetic optimization algorithm. In 2018 innovations in intelligent systems and applications (INISTA). IEEE. 2018; pp 1–6. https://doi.org/10.1109/INISTA.2018.8466267.
[20] Arcangeli J. P, Boujbel R, Leriche S. Automatic deployment of distributed software systems: Definitions and state of the art. The Journal of Systems and Software. January 2015; 3:198-218. https://doi.org/10.1016/j.jss.2015.01.040.
[21] Dorigo M. Optimization, Learning and Natural Algorithms. PhD thesis, Politecnico di Milano. Italy. 1992.
[22] Teodorović D. Bee Colony Optimization (BCO). In: Lim C.P., Jain L.C., Dehuri S. (eds) Innovations in Swarm Intelligence. Studies in Computational Intelligence. Springer. 2009; 248, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04225-6_3.
[23] Yang X. S. A New Metaheuristic Bat-Inspired Algorithm, in: Nature Inspired Cooperative Strategies for Optimization. Studies in Computational Intelligence. 2010; 284: 65–74. https://doi.org/10.1007/978-3-642-12538-6_6.
[24] Mirjalili S, Mirjalili S. M, Lewis A. Grey wolf optimizer. Advances in Engineering Software. 2014; 69: 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007.
[25] Mirjalili S, Lewis A. The whale optimization algorithm. Advances in Engineering Software. 2016; 95: 51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008.
[26] Saeedi, P, Hosseini Shirvani M. An improved thermodynamic simulated annealing-based approach for resource-skewness-aware and power-efficient virtual machine consolidation in cloud datacenters. Soft Comput. 2021. https://doi.org/10.1007/s00500-020-05523-1.
[27] Hosseini Shirvani M. A hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous distributed computing systems. Engineering Applications of Artificial Intelligence. 2020; 90:1–20. https://doi.org/10.1016/j.engappai.2020.103501.
[28] Javadian Kootanaee, A, Poor Aghajan A, Hosseini Shirvani M. A hybrid model based on machine learning and genetic algorithm for detecting fraud in financial statements. Journal of Optimization in Industrial Engineering. 2021; 14(2):183-201. doi: 10.22094/joie.2020.1877455.1685.
[29] Azimi, S., Pahl, C., Hosseijni Shirvani, M.: Performance Management in Clustered Edge Architectures Using Particle swarm optimization. In: Cloud Computing and Services Science. 2021; 1399: 233–257.
