Optimizing Cloud-Fog-Edge Job Scheduling Using Catastrophic Genetic Algorithm and Block Chain-Based Trust: A Collaborative Approach
DOI:
https://doi.org/10.37385/jaets.v5i1.3125Keywords:
Edge Computing Task Scheduling, CGA, Ca Catastrophic Genetic Algorithm, BlockchainAbstract
Collaborative edge-cloud features improve job scheduling. Cloud job scheduling is crucial. Pending delay completion. A cloud-edge mixed system replaced centralized cloud computing. Combining resource levels reduces terminal user service call latency. Decentralization, regionalization, and node dispersal autonomy increase ambiguity, unreliability, and instability. This paper will plan cloud-migrating tasks on edge devices or the cloud to achieve a global optimum. The objective of this research is to enhance the efficiency of job scheduling in cloud-fog edge environments through the integration of the Catastrophic Genetic Algorithm (CGA), a genetic algorithm inspired by natural evolution. Additionally, Berger's theory will be employed to develop a trust-enabled interaction framework based on blockchain technology. The CGA fitness function incorporates load balancing and reasonability in the coordination of services and scheduling of tasks, with the goal of maximizing performance. This article presents proposed improvements to the CGA, which involve the incorporation of mutation and crossover operators, roulette selection, and cataclysm. These changes aim to expand the search area and potentially discover schedules that are more optimal. The approach also effectively deals with the problem of premature convergence, guaranteeing ample time for the algorithm to comprehensively explore the solution space prior to reaching a final solution. The experimental findings indicate that the strategy put forward in this study yields a substantial reduction in task completion time, surpassing 97%. Furthermore, it effectively addresses the best local problem, hence showcasing competing options.
Downloads
References
Abd Elaziz, M., Xiong, S., Jayasena, K. P. N., & Li, L. (2019). Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution. Knowledge-Based Systems, 169, 39-52. https://doi.org/10.1016/j.knosys.2019.01.023
Abdul Kareem, E. I ., & Hussein, S. A. (2022). Optimal CPU Jobs Scheduling Method Based on Simulated Annealing Algorithm. Iraqi Journal of Science, 63(8), 3640–3651. https://doi.org/10.24996/ijs.2022.63.8.38
Abdullah, S. K., & Jabir, A. J. (2022). A Multi-Objective Task Offloading Optimization for Vehicular Fog Computing. Iraqi Journal of Science, 36(2), 785-800. https://doi.org/10.24996/ijs.2022.63.2.33
Acampora, G., Chiatto, A., & Vitiello, A. (2023). Genetic algorithms as classical optimizer for the Quantum Approximate Optimization Algorithm. Applied Soft Computing, 142, 110296. https://doi.org/10.1016/j.asoc.2023.110296
Ahn, C. W., Kim, K. P., & Ramakrishna, R. S. (2004). A memory-efficient elitist genetic algorithm. In Parallel Processing and Applied Mathematics: 5th International Conference, PPAM 2003, Czestochowa, Poland, September 7-10, 2003. Revised Papers 5 (pp. 552-559). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-24669-5_72
Al-Tamimi, M. S., & Mohammed Ali, F. A. (2023). Face mask detection based on algorithm YOLOv5s. International Journal of Nonlinear Analysis and Applications, 14(1), 679-697. https://doi.org/10.22075/ijnaa.2022.28178.3824
Baritompa, W. P., Bulger, D. W., & Wood, G. R. (2005). Grover's quantum algorithm applied to global optimization. SIAM Journal on Optimization, 15(4), 1170-1184. https://doi.org/10.1137/040605072
Brakerski, Z., Christiano, P., Mahadev, U., Vazirani, U., & Vidick, T. (2021). A cryptographic test of quantumness and certifiable randomness from a single quantum device. Journal of the ACM (JACM), 68(5), 1-47. https://doi.org/10.1145/3441309
Cao, Z., Zhou, H., Yuan, X., & Ma, X. (2016). Source-independent quantum random number generation. Physical Review X, 6(1), 011020. https://doi.org/10.1103/PhysRevX.6.011020
Chavda, A. D. (2021). Multi-stage CNN architecture for face mask detection. International Conference for Convergence in Technology, (pp. 1-8). IEEE. https://doi.org/10.1109/I2CT51068.2021.9418207
Chmiel, W., & Kwiecien, J. (2018). Quantum-inspired evolutionary approach for the quadratic assignment problem. Entropy, 20(10), 781. https://doi.org/10.3390/e20100781
da S. Medeiros, D. R., Torquato, M. F., & Fernandes, M. A. (2020). Embedded genetic algorithm for low?power, low?cost, and low?size?memory devices. Engineering Reports, 2(9), e12231. https://doi.org/10.1002/eng2.12231
DAR, A. R., Ravindran, D., & Islam, S. (2020). Fog-based spider web algorithm to overcome latency in cloud computing. Iraqi Journal of Science, 61(7), 1781-1790. https://doi.org/10.24996/ijs.2020.61.7.27
Dubuque, I. A. Adomavicius, G., A. Tuzhilin. (2005). Toward the next generation of recommender systems: a survey of the state?of?the?art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749. https://doi.org/10.1109/TKDE.2005.99
Fürer, M. (2008). Solving NP-complete problems with quantum search. In Latin American Symposium on Theoretical Informatics (pp. 784-792). Berlin, Heidelberg: Springer Berlin Heidelberg.? https://doi.org/10.1007/978-3-540-78773-0_67
Grover, L. K. (1996). A fast quantum mechanical algorithm for database search. In Proceedings of the twenty-eighth annual ACM symposium on Theory of computing (pp. 212-219). https://doi.org/10.1145/237814.237866
Harik, G. R., Lobo, F. G., & Goldberg, D. E. (1999). The compact genetic algorithm. IEEE transactions on evolutionary computation, 3(4), 287-297. https://doi.org/10.1109/4235.797971
Harik, G., Cantú-Paz, E., Goldberg, D. E., & Miller, B. L. (1999). The gambler's ruin problem, genetic algorithms, and the sizing of populations. Evolutionary computation, 7(3), 231-253. https://doi.org/10.1162/evco.1999.7.3.231
Jiang, J., Han, G., Shu, L., Chan, S., & Wang, K. (2015). A trust model based on cloud theory in underwater acoustic sensor networks. IEEE Transactions on Industrial Informatics, 13(1), 342-350. https://doi.org/10.1109/TII.2015.2510226
Joseph, C. T., Chandrasekaran, K., & Cyriac, R. (2014). Improving the efficiency of genetic algorithm approach to virtual machine allocation. In 2014 International Conference on Computer and Communication Technology (ICCCT) (pp. 111-116). IEEE.? https://doi.org/10.1109/ICCCT.2014.7001477
Jyoti, A., & Chauhan, R. K. (2022). A blockchain and smart contract-based data provenance collection and storing in cloud environment. Wireless Networks, 28(4), 1541-1562. https://doi.org/10.1007/s11276-022-02924-y
Kamalinia, A., & Ghaffari, A. (2017). Hybrid task scheduling method for cloud computing by genetic and DE algorithms. Wireless personal communications, 97, 6301-6323. https://doi.org/10.1007/s11277-017-4839-2
Kumar, A. K. (2021). A hybrid tiny YOLO v4-SPP module based improved face mask detection vision system. J. Ambient Intell. Humaniz. Comput, p1–14. https://doi.org/10.1007/s12652-021-03541-x
Li, C. C. (2020). Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture. Association for Computing Machinery, pp. 74–77. https://doi.org/10.1145/3421766.3421768.
Li, Y., Wang, X., Gan, X., Jin, H., Fu, L., & Wang, X. (2019). Learning-aided computation offloading for trusted collaborative mobile edge computing. IEEE Transactions on Mobile Computing, 19(12), 2833-2849. https://doi.org/10.1109/TMC.2019.2934103
Lucas, A. (2014). Ising formulations of many NP problems. Frontiers in physics, PP2, 5. https://doi.org/10.3389/fphy.2014.00005
Mohamed, N. A., & Al-Tamimi, M. S. H. (2020). Image fusion using a convolutional neural network. Solid State Technol, 63(6), 13149-13162.
Mohammed Ali, F. A., & Al-Tamimi, M. S. (2022). Face mask detection methods and techniques: A review. International Journal of Nonlinear Analysis and Applications, 13(1), 3811-3823. https://doi.org/10.22075/ijnaa.2022.6166
Mora-Melià, D., Martínez-Solano, F. J., Iglesias-Rey, P. L., & Gutiérrez-Bahamondes, J. H. (2017). Population size influence on the efficiency of evolutionary algorithms to design water networks. Procedia Engineering, 186, 341-348.? https://doi.org/10.1016/j.proeng.2017.03.209
Murthy, C. V. B., Shri, M. L., Kadry, S., & Lim, S. (2020). Blockchain based cloud computing: Architecture and research challenges. IEEE access, 8, 205190-205205. https://doi.org/10.1109/ACCESS.2020.3036812
Narayanan, A., & Moore, M. (1996). Quantum-inspired genetic algorithms. In Proceedings of IEEE international conference on evolutionary computation (pp. 61-66). IEEE.? https://doi.org/10.1109/ICEC.1996.542334
Patil, S. (2022). Block Chain-Based System: Applications and Challenges. International Journal of Research and Analysis in Science and Engineering, pp.2(2), 9-9.
Perumalla, S. C. (2021). Block chain-based access control and intrusion detection system in iod. International Conference on Communication and Electronics Systems (ICCES) IEEE, pp. 511-518. https://doi.org/10.1109/ICCES51350.2021.9488948
Rodionova, A., Antonov, K., Buzdalova, A., & Doerr, C. (2019). Offspring population size matters when comparing evolutionary algorithms with self-adjusting mutation rates. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 855-863).? https://doi.org/10.1145/3321707.3321827
Rylander, B., Soule, T., Foster, J. A., & Alves-Foss, J. (2000). Quantum Genetic Algorithms. In GECCO (p. 373).?
Sharma, V. (2020). Face mask detection using yolov5 for COVID-19. Doctoral dissertation, California State University San Marcos.
Shor, P. W. (1999). Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM review, 41(2), 303-332.? https://doi.org/10.1137/S0036144598347011.
Siddiqi, U. F., Shiraishi, Y., & Sait, S. M. (2013). Memory-efficient genetic algorithm for path optimization in embedded systems. IPSJ Online Transactions, 6, 28-36.? https://doi.org/10.2197/ipsjtrans.6.28
Sohail, A. (2023). Genetic algorithms in the fields of artificial intelligence and data sciences. Annals of Data Science, 10(4), 1007-1018. https://doi.org/10.1007/s40745-021-00354-9
Sun, G., Su, S., & Xu, M. (2014). Quantum algorithm for polynomial root finding problem. In 2014 Tenth International Conference on Computational Intelligence and Security (pp. 469-473). IEEE. https://doi.org/10.1109/CIS.2014.40
Supasil, J., Pathumsoot, P., & Suwanna, S. (2021). Simulation of implementable quantum-assisted genetic algorithm. In Journal of Physics: Conference Series (Vol. 1719, No. 1, p. 012102). IOP Publishing. https://doi.org/10.1088/1742-6596/1719/1/012102.
Tian, Z., Chu, X., Wang, X., Wei, X., & Shen, C. (2022). Fully convolutional one-stage 3d object detection on lidar range images. Advances in Neural Information Processing Systems, 35, 34899-34911.? https://doi.org/10.1109/ICCV.2019.00972.
Wang, T., Zhang, G. X., Cai, S. B., Jia, W. J., & Wang, G. J. (2018). Survey on trust evaluation mechanism in sensor-cloud. Journal on Communications, 39(6), 37-51.? http://dx.doi.org/10.11959/j.issn.1000-436x.2018098
Xu, Y., Li, K., Hu, J., & Li, K. (2014). A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Information Sciences, 270, 255-287.? https://doi.org/10.1016/j.ins.2014.02.122
Yang, H., Cho, J. H., Son, H., & Lee, D. (2020). Context-aware trust estimation for realtime crowdsensing services in vehicular edge networks. In 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC) (pp. 1-6). IEEE.? https://doi.org/10.1109/CCNC46108.2020.9045221
Yu, J., & Zhang, W. (2021). Face mask wearing detection algorithm based on improved YOLO-v4. Sensors, 21(9), 3263.https://doi.org/10.3390/s21093263