2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 15 , Issue 2 , PP: 138-150, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Syed Mutiullah Hussaini 1 * , T. Abdul Razak 2 , Muhammad Abid Jamil 3
Doi: https://doi.org/10.54216/JISIoT.150210
The (SPEA2) Strength Pareto Evolutionary Algorithm 2 is a capable technique for managing multi-objective optimization problems. In IoT-cloud systems, this is particularly true with regard to task scheduling. Task scheduling and efficient resource allocation are necessary to improve performance and service quality as the Internet of Things (IoT) grows. SPEA2, which is especially helpful for cloud computing frameworks, is excellent at handling competing goals, such minimizing executing duration while increasing the usage of resources. The capacity of SPEA2 to keep a large collection of solutions allows for the exploration of various scheduling approaches in IoT-cloud scenarios, where tasks generated by several devices need to be handled effectively. In dynamic contexts where resource availability varies, this IoT-CS (IoT-Cloud_Scheduling) adaptability is essential. With SPEA2, researchers are able to create algorithms that enhance system responsiveness and dependability overall while also optimizing task scheduling. The management of resource distribution and task prioritizing difficulties is exemplified by the use of SPEA2 to scheduling problems in IoT-cloud infrastructures. Thus, by guaranteeing that computing resources are used efficiently while respecting performance limitations, SPEA2 makes a substantial contribution to the development of intelligent scheduling solutions that satisfy the changing requirements of IoT applications
Cloud Computing , IoT things , IBEA , NSGA-II , SPEA2 , IoT-CS , MOEA , Evolutionary Algorithm , Optimization , Scheduling Solution
[1] S. Ismail, K. Shah, H. Reza, R. Marsh, and E. Grant, "Toward management of uncertainty in self-adaptive software systems: IoT case study," Computers, vol. 10, no. 27, 2021.
[2] D. Arcelli, "Exploiting queuing networks to model and assess the performance of self-adaptive software systems: A survey," Procedia Comput. Sci., vol. 170, pp. 498–505, 2020.
[3] A. J. Ramirez, D. B. Knoester, B. H. Cheng, and P. K. McKinley, "Plato: A genetic algorithm approach to run-time reconfiguration in autonomic computing systems," Clust. Comput., vol. 14, pp. 229–244, 2011.
[4] Z. I. M. Yusoh and M. Tang, "Composite SaaS placement and resource optimization in cloud computing using evolutionary algorithms," in Proc. IEEE 5th Int. Conf. Cloud Computing (CLOUD), Honolulu, HI, USA, 24–29 Jun. 2012, pp. 590–597.
[5] S. Hameed, Y. Elsheikh, and M. Azzeh, "An optimized case-based software project effort estimation using genetic algorithm," Inf. Softw. Technol., vol. 153, p. 107088, 2023.
[6] R. M. Hierons, M. Li, X. Liu, S. Segura, and W. Zheng, "SIP: Optimal product selection from feature models using many-objective evolutionary optimization," ACM Trans. Softw. Eng. Methodol., vol. 25, pp. 1–39, 2016.
[7] T. Chen and R. Bahsoon, "Self-adaptive trade-off decision making for autoscaling cloud-based services," IEEE Trans. Serv. Comput., vol. 10, pp. 618–632, 2015.
[8] D. El Kateb, F. Fouquet, G. Nain, J. A. Meira, M. Ackerman, and Y. Le Traon, "Generic cloud platform multi-objective optimization leveraging models@ runtime," in Proc. 29th Annu. ACM Symp. Appl. Comput., Gyeongju, Republic of Korea, 24–28 Mar. 2014, pp. 343–350.
[9] E. M. Fredericks, "Automatically hardening a self-adaptive system against uncertainty," in Proc. 11th Int. Symp. Software Eng. Adaptive Self-Managing Syst. (SEAMS’16), Austin, TX, USA, 16–17 May 2016, pp. 16–27.
[10] Q. Zhang and H. Li, "MOEA/D: A multiobjective evolutionary algorithm based on decomposition," IEEE Trans. Evol. Comput., vol. 11, pp. 712–731, 2017.
[11] C. G. Fidalgo, M. Sousa, D. Mendes, R. K. Dos Anjos, D. Medeiros, K. Singh, and J. Jorge, "Manipulating avatars and gestures to improve remote collaboration," in Proc. IEEE Conf. Virtual Reality 3D User Interfaces (VR), Shanghai, China, 25–29 Mar. 2023, pp. 438–448.
[12] K. Keeton, C. A. Santos, D. Beyer, J. S. Chase, and J. Wilkes, "Designing for DiIoT_CS ters," FAST, vol. 4, pp. 59–62, 2004.
[13] A. J. Ramirez, D. B. Knoester, B. H. Cheng, and P. K. McKinley, "Applying genetic algorithms to decision making in autonomic computing systems," in Proc. 6th Int. Conf. Autonomic Comput., Barcelona, Spain, 15–19 Jun. 2009, pp. 97–106.
[14] T. Chen, K. Li, R. Bahsoon, and X. Yao, "FEMOSAA: Feature-guided and knee-driven multi-objective optimization for self-adaptive software," ACM Trans. Softw. Eng. Methodol, vol. 27, pp. 1–50, 2018.
[15] E. M. Fredericks and B. H. Cheng, "Automated generation of adaptive test plans for self-adaptive systems," in Proc. IEEE/ACM 10th Int. Symp. Software Eng. Adaptive Self-Managing Syst., Florence, Italy, 18–19 May 2015, pp. 157–167.
[16] D. Han, Y. Cai, W. Chen, Z. Cui, and A. Li, "Timed-IOT_CS: Modeling and analyzing the time behaviors of self-adaptive software under uncertainty," Appl. Sci., vol. 13, p. 2018, 2023.
[17] R. A. Sulaiman, D. N. Jawawi, and S. A. Halim, "Cost-effective test case generation with the hyper-heuristic for software product line testing," Adv. Eng. Softw., vol. 175, p. 103335, 2023.
[18] D. Benavides, S. Segura, and A. Ruiz-Cortés, "Automated analysis of feature models 20 years later: A literature review," Inf. Syst., vol. 35, pp. 615–636, 2010.
[19] E. Zitzler, M. Laumanns, and L. Thiele, "SPEA2: Improving the strength Pareto evolutionary algorithm," TIK Report, vol. 103, ETH Zurich, 2001.
[20] S. Gueorguiev, M. Harman, and G. Antoniol, "Software project planning for robustness and completion time in the presence of uncertainty using multi-objective search-based software engineering," in Proc. 11th Annu. Conf. Genetic Evol. Comput., Montreal, QC, Canada, 8–12 Jul. 2009, pp. 1673–1680.
[21] L. Zhang, W. Li, and L. Zhang, "Adaptive QoS-aware resource allocation in cloud computing systems using multi-agent reinforcement learning," IEEE Trans. Cloud Comput., vol. 11, no. 5, pp. 1556–1569, Sept.-Oct. 2023.
[22] A. S. Tan, J. Sun, and X. Xu, "QoS-aware adaptive scheduling for cloud services based on evolutionary algorithms," IEEE Trans. Serv. Comput., vol. 14, no. 3, pp. 856–867, May-Jun. 2021.
[23] N. Roy, A. Dubey, A. Gokhale, and L. Dowdy, "A capacity planning process for performance assurance of component-based distributed systems," in Proc. 2nd ACM/SPEC Int. Conf. Perform. Eng., Delft, The Netherlands, 12–16 Mar. 2016, pp. 259–270.
[24] F. Fittkau, S. Frey, and W. Hasselbring, "CDOSim: Simulating cloud deployment options for software migration support," in Proc. IEEE 6th Int. Workshop Maint. Evol. Service-Oriented Cloud-Based Syst. (MESOCA), Trento, Italy, 24 Sep. 2012, pp. 37–46.
[25] E. Zitzler and L. Thiele, "Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach," IEEE Trans. Evol. Comput., vol. 3, pp. 257–271, 1999.
[26] J. Cao, Z. Yan, Z. Chen, and J. Zhang, "A Pareto front estimation-based constrained multi-objective evolutionary algorithm," Appl. Sci., vol. 53, pp. 10380–10416, 2023.
[27] G. G. Yen and Z. He, "Performance metrics ensemble for multiobjective evolutionary algorithms," IEEE Trans. Evol. Comput., vol. 17, pp. 736–749, 2013.
[28] S. M. Hussaini and T. A. Razzak, "Optimizing scheduling problem IoT based cloud computing environment using EAs (IBEA algorithm)," J. Data Acquis. Process, vol. 38, no. 3, 2023, ISSN 1004-9037.
[29] Z. Movahedi, B. Defude, and A. M. Hosseininia, "An efficient population-based multi-objective task scheduling approach in fog computing systems," J. Cloud Comput., vol. 10, no. 53, pp. 1–17, Oct. 2021.
[30] H. Gao, H. Nie, and K. Li, "Visualization of Pareto front approximation: A short survey and empirical comparisons," arXiv: 1903.01768v1, 5 Mar. 2019.
[31] D. Hadka, "MOEA Framework—A free and open source Java framework for multiobjective optimization," Version 2, 2015. Available online: http://moeaframework.org/ (accessed on 8 October 2022).
[32] M. A. Jamil, M. K. Nour, S. S. Alotaibi, M. J. Hussain, S. M. Hussaini, and A. Naseer, "Adaptive test suits generation for self-adaptive systems using SPEA2 algorithm," Appl. Sci., vol. 13, no. 20, p. 11324, 2023.
[33] R. Wazirali, W. Alasmary, M. M. Mahmoud, and A. Alhindi, "An optimized steganography hiding capacity and imperceptibility using genetic algorithms," IEEE Access, vol. 7, pp. 133496–133508, 2019.
[34] M. A. Jamil and M. K. Nour, "Managing software testing technical debt using evolutionary algorithms," Comput. Mater. Contin., vol. 73, pp. 735–747, 2022.
[35] A. S. Saini, V. Mishra, and S. Gupta, "A hybrid evolutionary approach for feature selection in software product line engineering," J. Softw. Evol. Process, vol. 35, no. 4, pp. 1–16, Apr. 2023.
[36] M. A. Jamil, M. K. Nour, S. S. Alotaibi, M. J. Hussain, S. M. Hussaini, and A. Naseer, "Adaptive test suits generation for self-adaptive systems using SPEA2 algorithm," Appl. Sci., vol. 13, no. 20, p. 11324, Oct. 2023.
