Black Widow Optimization (BWO) Algorithm in Cloud Brokering Systems for Connected Internet of Things
الموضوعات :
Journal of Computer & Robotics
Nasim Jelodari
1
,
Ali AsgharPourhaji Kazem
2
1 - Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
2 - Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
تاريخ الإرسال : 02 الأربعاء , ذو القعدة, 1443
تاريخ التأكيد : 14 الإثنين , ذو القعدة, 1443
تاريخ الإصدار : 02 الجمعة , ذو الحجة, 1443
الکلمات المفتاحية:
Simulation,
Optimization,
Internet of Things,
Cloud Broker,
Black Widow Optimization Algorithm,
ملخص المقالة :
The Internet of Things (IoT) now connects over nine billion devices. This number is predicted to approach 20 billion in the near future, and the number of things is rapidly expanding, implying that a large amount of data will be created. To handle the connected things, an infrastructure must be built. Cloud computing (CC) has become necessary in the analysis and data storage for IoT. A cloud broker, which is an intermediate in the infrastructure that controls connected things in cloud computing, is discussed in this study. An optimization problem is examined for maximizing the broker's profit and system availability while minimizing request response time and energy consumption. For this purpose, an objective function is proposed and solved using the Black Widow Optimization (BWO) algorithm. Subsequently, the obtained results are compared with the particle swarm optimization (PSO) algorithms. The results indicate that the BWO algorithm could outperform the PSO algorithm, and it can provide much better results considering different scenarios.
المصادر:
[1] S. Li, L.D. Xu, S. Zhao, The internet of things: a survey, Information systems frontiers, 17(2) (2015) 243-259.
[2] S.M. Besen, The European telecommunications standards institute: A preliminary analysis, Telecommunications policy, 14(6) (1990) 521-530.
[3] S. Samal, B. Acharya, P.K. Barik, Internet of Things (IoT) in agriculture toward urban greening, in: AI, Edge and IoT-based Smart Agriculture, Elsevier, 2022, pp. 171-182.
[4] A. Rejeb, Z. Suhaiza, K. Rejeb, S. Seuring, H. Treiblmaier, The Internet of Things and the circular economy: A systematic literature review and research agenda, Journal of Cleaner Production, (2022) 131439.
[5] K. Rose, S. Eldridge, L. Chapin, The internet of things: An overview, The internet society (ISOC), 80 (2015) 1-50.
[6] D.K. Sharma, S. Bhargava, K. Singhal, Internet of Things applications in the pharmaceutical industry, in: An Industrial IoT Approach for Pharmaceutical Industry Growth, Elsevier, 2020, pp. 153-190.
[7] I. Boussaïd, J. Lepagnot, P. Siarry, A survey on optimization metaheuristics, Information Sciences, 237 (2013) 82-117. https://doi.org/https://doi.org/10.1016/j.ins.2013.02.041
[8] B. Galván, D. Greiner, J. Periaux, M. Sefrioui, G. Winter, Parallel Evolutionary Computation for solving complex CFD Optimization problems: a review and some nozzle applications, Parallel Computational Fluid Dynamics 2002, (2003) 573-604.
[9] J.H. Holland, Genetic Algorithms and Adaptation, in: O.G. Selfridge, E.L. Rissland, M.A. Arbib (Eds.) Adaptive Control of Ill-Defined Systems, Springer US, Boston, MA, 1984, pp. 317-333.
[10] R. Storn, K. Price, Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces, Journal of Global Optimization, 11(4) (1997) 341-359. https://doi.org/10.1023/A:1008202821328
[11] B.M. Angadi, M.S. Kakkasageri, S.S. Manvi, Computational intelligence techniques for localization and clustering in wireless sensor networks, in: Recent Trends in Computational Intelligence Enabled Research, Elsevier, 2021, pp. 23-40.
[12] R. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, in: MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, pp. 39-43.
[13] M. Dorigo, V. Maniezzo, A. Colorni, Ant system: optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1) (1996) 29-41. https://doi.org/10.1109/3477.484436
[14] Z.E. Ahmed, R.A. Saeed, A. Mukherjee, S.N. Ghorpade, 10 - Energy optimization in low-power wide area networks by using heuristic techniques, in: B.S. Chaudhari, M. Zennaro (Eds.) LPWAN Technologies for IoT and M2M Applications, Academic Press, 2020, pp. 199-223.
[15] S. Mirjalili, A. Lewis, The Whale Optimization Algorithm, Advances in Engineering Software, 95 (2016) 51-67. https://doi.org/https://doi.org/10.1016/j.advengsoft.2016.01.008
[16] S. Mirjalili, S.M. Mirjalili, A. Lewis, Grey Wolf Optimizer, Advances in Engineering Software, 69 (2014) 46-61. https://doi.org/https://doi.org/10.1016/j.advengsoft.2013.12.007
[17] M. Azizi, S. Talatahari, A. Giaralis, Active Vibration Control of Seismically Excited Building Structures by Upgraded Grey Wolf Optimizer, IEEE Access, 9 (2021) 166658-166673.
[18] L. Xie, J. Zeng, Z. Cui, General framework of artificial physics optimization algorithm, in: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), IEEE, 2009, pp. 1321-1326.
[19] M. Azizi, Atomic orbital search: A novel metaheuristic algorithm, Applied Mathematical Modelling, 93 (2021) 657-683. https://doi.org/https://doi.org/10.1016/j.apm.2020.12.021
[20] M. Azizi, S. Talatahari, A. Giaralis, Optimization of Engineering Design Problems Using Atomic Orbital Search Algorithm, IEEE Access, 9 (2021) 102497-102519. https://doi.org/10.1109/ACCESS.2021.3096726
[21] S. Talatahari, M. Azizi, A.H. Gandomi, Material generation algorithm: a novel metaheuristic algorithm for optimization of engineering problems, Processes, 9(5) (2021) 859.
[1] S. Li, L.D. Xu, S. Zhao, The internet of things: a survey, Information systems frontiers, 17(2) (2015) 243-259.
[2] S.M. Besen, The European telecommunications standards institute: A preliminary analysis, Telecommunications policy, 14(6) (1990) 521-530.
[3] S. Samal, B. Acharya, P.K. Barik, Internet of Things (IoT) in agriculture toward urban greening, in: AI, Edge and IoT-based Smart Agriculture, Elsevier, 2022, pp. 171-182.
[4] A. Rejeb, Z. Suhaiza, K. Rejeb, S. Seuring, H. Treiblmaier, The Internet of Things and the circular economy: A systematic literature review and research agenda, Journal of Cleaner Production, (2022) 131439.
[5] K. Rose, S. Eldridge, L. Chapin, The internet of things: An overview, The internet society (ISOC), 80 (2015) 1-50.
[6] D.K. Sharma, S. Bhargava, K. Singhal, Internet of Things applications in the pharmaceutical industry, in: An Industrial IoT Approach for Pharmaceutical Industry Growth, Elsevier, 2020, pp. 153-190.
[7] I. Boussaïd, J. Lepagnot, P. Siarry, A survey on optimization metaheuristics, Information Sciences, 237 (2013) 82-117. https://doi.org/https://doi.org/10.1016/j.ins.2013.02.041
[8] B. Galván, D. Greiner, J. Periaux, M. Sefrioui, G. Winter, Parallel Evolutionary Computation for solving complex CFD Optimization problems: a review and some nozzle applications, Parallel Computational Fluid Dynamics 2002, (2003) 573-604.
[9] J.H. Holland, Genetic Algorithms and Adaptation, in: O.G. Selfridge, E.L. Rissland, M.A. Arbib (Eds.) Adaptive Control of Ill-Defined Systems, Springer US, Boston, MA, 1984, pp. 317-333.
[10] R. Storn, K. Price, Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces, Journal of Global Optimization, 11(4) (1997) 341-359. https://doi.org/10.1023/A:1008202821328
[11] B.M. Angadi, M.S. Kakkasageri, S.S. Manvi, Computational intelligence techniques for localization and clustering in wireless sensor networks, in: Recent Trends in Computational Intelligence Enabled Research, Elsevier, 2021, pp. 23-40.
[12] R. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, in: MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, pp. 39-43.
[13] M. Dorigo, V. Maniezzo, A. Colorni, Ant system: optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1) (1996) 29-41. https://doi.org/10.1109/3477.484436
[14] Z.E. Ahmed, R.A. Saeed, A. Mukherjee, S.N. Ghorpade, 10 - Energy optimization in low-power wide area networks by using heuristic techniques, in: B.S. Chaudhari, M. Zennaro (Eds.) LPWAN Technologies for IoT and M2M Applications, Academic Press, 2020, pp. 199-223.
[15] S. Mirjalili, A. Lewis, The Whale Optimization Algorithm, Advances in Engineering Software, 95 (2016) 51-67. https://doi.org/https://doi.org/10.1016/j.advengsoft.2016.01.008
[16] S. Mirjalili, S.M. Mirjalili, A. Lewis, Grey Wolf Optimizer, Advances in Engineering Software, 69 (2014) 46-61. https://doi.org/https://doi.org/10.1016/j.advengsoft.2013.12.007
[17] M. Azizi, S. Talatahari, A. Giaralis, Active Vibration Control of Seismically Excited Building Structures by Upgraded Grey Wolf Optimizer, IEEE Access, 9 (2021) 166658-166673.
[18] L. Xie, J. Zeng, Z. Cui, General framework of artificial physics optimization algorithm, in: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), IEEE, 2009, pp. 1321-1326.
[19] M. Azizi, Atomic orbital search: A novel metaheuristic algorithm, Applied Mathematical Modelling, 93 (2021) 657-683. https://doi.org/https://doi.org/10.1016/j.apm.2020.12.021
[20] M. Azizi, S. Talatahari, A. Giaralis, Optimization of Engineering Design Problems Using Atomic Orbital Search Algorithm, IEEE Access, 9 (2021) 102497-102519. https://doi.org/10.1109/ACCESS.2021.3096726
[21] S. Talatahari, M. Azizi, A.H. Gandomi, Material generation algorithm: a novel metaheuristic algorithm for optimization of engineering problems, Processes, 9(5) (2021) 859.
[22] M. Azizi, M.B. Shishehgarkhaneh, M. Basiri, Optimum design of truss structures by Material Generation Algorithm with discrete variables, Decision Analytics Journal, (2022) 100043. https://doi.org/https://doi.org/10.1016/j.dajour.2022.100043
[23] O.K. Erol, I. Eksin, A new optimization method: big bang–big crunch, Advances in Engineering Software, 37(2) (2006) 106-111.
[24] Ş.İ. Birbil, S.-C. Fang, An electromagnetism-like mechanism for global optimization, Journal of global optimization, 25(3) (2003) 263-282.
[25] N. Khodadadi, M. Azizi, S. Talatahari, P. Sareh, Multi-Objective Crystal Structure Algorithm (MOCryStAl): Introduction and Performance Evaluation, IEEE Access, 9 (2021) 117795-117812. https://doi.org/10.1109/ACCESS.2021.3106487
[26] M. Azizi, S. Talatahari, P. Sareh, Design optimization of fuzzy controllers in building structures using the crystal structure algorithm (CryStAl), Advanced Engineering Informatics, 52 (2022) 101616. https://doi.org/https://doi.org/10.1016/j.aei.2022.101616
[27] B. Talatahari, M. Azizi, S. Talatahari, M. Tolouei, P. Sareh, Crystal structure optimization approach to problem solving in mechanical engineering design, Multidiscipline Modeling in Materials and Structures, (2022).
[28] S. Talatahari, M. Azizi, M. Tolouei, B. Talatahari, P. Sareh, Crystal Structure Algorithm (CryStAl): A Metaheuristic Optimization Method, IEEE Access, 9 (2021) 71244-71261. https://doi.org/10.1109/ACCESS.2021.3079161
[29] S. Talatahari, M. Azizi, Chaos Game Optimization: a novel metaheuristic algorithm, Artificial Intelligence Review, 54(2) (2021) 917-1004. https://doi.org/10.1007/s10462-020-09867-w
[30] M. Azizi, U. Aickelin, H.A. Khorshidi, M.B. Shishehgarkhaneh, Shape and size optimization of truss structures by Chaos game optimization considering frequency constraints, Journal of Advanced Research, (2022). https://doi.org/https://doi.org/10.1016/j.jare.2022.01.002
[31] R.V. Rao, V.J. Savsani, D. Vakharia, Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems, Computer-aided design, 43(3) (2011) 303-315.
[32] Z.W. Geem, J.H. Kim, G.V. Loganathan, A new heuristic optimization algorithm: harmony search, simulation, 76(2) (2001) 60-68.
[33] M. Azizi, S. Talatahari, M. Basiri, M.B. Shishehgarkhaneh, Optimal design of low-and high-rise building structures by Tribe-Harmony Search algorithm, Decision Analytics Journal, (2022) 100067.
[34] S.-A. Ahmadi, Human behavior-based optimization: a novel metaheuristic approach to solve complex optimization problems, Neural Computing and Applications, 28(1) (2017) 233-244. https://doi.org/10.1007/s00521-016-2334-4
[35] V. Hayyolalam, A.A. Pourhaji Kazem, Black Widow Optimization Algorithm: A novel meta-heuristic approach for solving engineering optimization problems, Engineering Applications of Artificial Intelligence, 87 (2020) 103249. https://doi.org/https://doi.org/10.1016/j.engappai.2019.103249
[36] Y. Kessaci, N. Melab, E.-G. Talbi, A Pareto-based metaheuristic for scheduling HPC applications on a geographically distributed cloud federation, Cluster Computing, 16(3) (2013) 451-468.
[37] H.K. Mehta, P. Pawar, P. Kanungo, A two level broker system for infrastructure as a service cloud, Wireless Personal Communications, 90(3) (2016) 1135-1147.
[38] K.S. Yildirim, T.E. Kalayci, A. Ugur, Optimizing coverage in a k-covered and connected sensor network using genetic algorithms, in: Proceedings of the 9th WSEAS international conference on evolutionary computing, Citeseer, 2008, pp. 21-26.
[39] M. Elhoseny, A. Abdelaziz, A. Salama, A.e.-d. Riad, K. Muhammad, A. Kumar, A hybrid model of Internet of Things and cloud computing to manage big data in health services applications, Future Generation Computer Systems, 86 (2018). https://doi.org/10.1016/j.future.2018.03.005
[40] J. Mei, K. Li, Z. Tong, Q. Li, K. Li, Profit maximization for cloud brokers in cloud computing, IEEE Transactions on Parallel and Distributed Systems, 30(1) (2018) 190-203.
[41] P. Asghari, A. Rahmani, H. Haj Seyyed Javadi, Privacy-aware cloud service composition based on QoS optimization in Internet of Things, Journal of Ambient Intelligence and Humanized Computing, (2020). https://doi.org/10.1007/s12652-020-01723-7
[42] X. Ye, Y. Yin, L. Lan, Energy-efficient many-objective virtual machine placement optimization in a cloud computing environment, IEEE Access, 5 (2017) 16006-16020.
[43] M. Le Berre, F. Hnaien, H. Snoussi, Multi-objective optimization in wireless sensors networks, in: ICM 2011 Proceeding, IEEE, 2011, pp. 1-4.
[44] Z. Sun, Y. Zhang, Y. Nie, W. Wei, J. Lloret, H. Song, CASMOC: a novel complex alliance strategy with multi-objective optimization of coverage in wireless sensor networks, wireless Networks, 23(4) (2017) 1201-1222.
[45] W. Wei, X. Fan, H. Song, X. Fan, J. Yang, Imperfect information dynamic stackelberg game based resource allocation using hidden Markov for cloud computing, IEEE transactions on services computing, 11(1) (2016) 78-89.
[46] S. Dörterler, M. Dörterler, S. Ozdemir, Multi-objective virtual machine placement optimization for cloud computing, in: 2017 International Symposium on Networks, Computers and Communications (ISNCC), IEEE, 2017, pp. 1-6.
[47] H. Li, M. Dong, K. Ota, M. Guo, Pricing and repurchasing for big data processing in multi-clouds, IEEE Transactions on Emerging Topics in Computing, 4(2) (2016) 266-277.
[48] I. Butun, M. Erol-Kantarci, B. Kantarci, H. Song, Cloud-centric multi-level authentication as a service for secure public safety device networks, IEEE Communications Magazine, 54(4) (2016) 47-53.
[49] S. Rana, D. Mishra, R. Arora, Privacy-Preserving Key Agreement Protocol for Fog Computing Supported Internet of Things Environment, Wireless Personal Communications, 119(1) (2021) 727-747. https://doi.org/10.1007/s11277-021-08234-4
[50] S. Pandey, L. Wu, S.M. Guru, R. Buyya, A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments, in: 2010 24th IEEE international conference on advanced information networking and applications, IEEE, 2010, pp. 400-407.
[51] A.M. Yadav, K.N. Tripathi, S.C. Sharma, An enhanced multi-objective fireworks algorithm for task scheduling in fog computing environment, Cluster Computing, 25(2) (2022) 983-998. https://doi.org/10.1007/s10586-021-03481-3
[52] M.A. Rakrouki, N. Alharbe, QoS-Aware Algorithm Based on Task Flow Scheduling in Cloud Computing Environment, Sensors, 22(7) (2022) 2632.
[53] N. Arora, R.K. Banyal, A Particle Grey Wolf Hybrid Algorithm for Workflow Scheduling in Cloud Computing, Wireless Personal Communications, 122(4) (2022) 3313-3345. https://doi.org/10.1007/s11277-021-09065-z
[54] H. Singh, S. Tyagi, P. Kumar, High availability and accessibility of services in cloud environment, in: 2018 4th International Conference on Computing Sciences (ICCS), IEEE, 2018, pp. 67-71.
[55] M. Prakash, R. Budihal, IMPROVING HIGH AVAILABILITY IN CLOUD INFRASTRUCTURE AT INSTANCE AND STORAGE LEVEL, Australian Journal of Wireless Technologies, Mobility and Security, 1(1) (2017) 1-7.
[56] D.-W. Sun, G.-R. Chang, S. Gao, L.-Z. Jin, X.-W. Wang, Modeling a dynamic data replication strategy to increase system availability in cloud computing environments, Journal of computer science and technology, 27(2) (2012) 256-272.
[57] B. Apduhan, M. Younas, T. Uchibayashi, Improving reliability and availability of Iaas services in hybrid clouds, in: International Conference on Computational Science and Its Applications, Springer, 2015, pp. 557-568.
[58] S. S, R. J, H.S. Guruprasad, Enhanced Load Balancing Algorithm in Three-Tier Cloud Computing, International Journal of Engineering Sciences & Emerging Technologies, 2 (2014) 296-301.
[59] M.R. Mesbahi, A.M. Rahmani, M. Hosseinzadeh, Reliability and high availability in cloud computing environments: a reference roadmap, Human-centric Computing and Information Sciences, 8(1) (2018) 1-31.
[60] H. Ma, A.S.d. Silva, W. Kuang, NSGA-II with Local Search for Multi-objective Application Deployment in Multi-Cloud, in: 2019 IEEE Congress on Evolutionary Computation (CEC), 2019, pp. 2800-2807.
[61] C. Guerrero, I. Lera, C. Juiz, Genetic-based optimization in fog computing: Current trends and research opportunities, Swarm and Evolutionary Computation, 72 (2022) 101094. https://doi.org/https://doi.org/10.1016/j.swevo.2022.101094
[62] V. Jafari, M.H. Rezvani, Joint optimization of energy consumption and time delay in IoT-fog-cloud computing environments using NSGA-II metaheuristic algorithm, Journal of Ambient Intelligence and Humanized Computing, (2021). https://doi.org/10.1007/s12652-021-03388-2
[63] M. Azizi, A. Mousavi, R. Ejlali, S. Talatahari, Optimum design of fuzzy controller using hybrid ant lion optimizer and Jaya algorithm, Artificial Intelligence Review, 53 (2020) 1-32. https://doi.org/10.1007/s10462-019-09713-8
[64] T. Kumrai, K. Ota, M. Dong, J. Kishigami, D.K. Sung, Multiobjective optimization in cloud brokering systems for connected Internet of Things, IEEE Internet of Things Journal, 4(2) (2016) 404-413.