1Azhar Ameer Hamza , 2Seyed Ebrahim Dashti (corresponding author), 3 Hala Hussein Issa Allobawi


Ubiquitous computing refers to the distribution of data over a large amount of data sources. Such a computing system gathers millions of resources to enable parallel computing applications Its goal is to harness the Internet’s vast computational capacity for large parallel applications. Since many users deal with these systems day and night, reliability and throughput are very important to them, therefore, this paper focuses on improving them in ubiquitous computing systems. This article suggests a spider optimization algorithm to improve parameters like reliability and throughput. It compares with other evolutionary algorithms; the result of the simulation shows significant improvement in comparison to other algorithms.

Keyword : Ubiquitous Computing, Network Computing, Social Spider Optimization Algorithm, reliability, throughput.

Published in Issue
August 23, 2023
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


[1] Yilmaz, E., Ecer, A.,Parallel CFD Applications Under DLB Environment, in Parallel Computational Fluid Dynamics 2001, 2002 [2] Matthew, N. O.,Sadiku, Kelechi, G. Eze, and Sarhan, M. Musa, GLOBAL COMPUTING, International Journal of Advances in Scientific Research and Engineering (ijasre), Volume 5, Issue 5 May - 2019 [3] Aida, K., et al., Performance evaluation model for scheduling in global computing systems, The International Journal of High Performance Computing Applications , vol. 14, no. 3, Fall 2000, pp. 268-279. [4] Pfoser D.,Pitoura, E., andTryfona, N., Metadata modeling in a global computing environment, Proceedings of the 10th ACM International Symposium on Advances in Geographic Information Systems, 2002, pp. 68-73. [5] Takefus, A., et al., Multi-client LAN/WAN performance analysis of Ninf: a high-performance global computing system, Proceedings of the 1997 ACM/IEEE Conference on Supercomputing, San Jose, CA., Nov. 1997, pp. 1-23. [6] Aleti, A., Moser, I., and Meedeniya, I., Choosing the appropriate forecasting model for predictive parameter control, Evolutionary Computation, vol. 22, no. 2, pp. 319–349, 2014. [7] Smit, S., andEiben, A., Comparing parameter tuning methods for evolutionary algorithms, in Proc. IEEE Congress on Evolutionary Computation (CEC), Trondheim, Norway, May 2009, pp. 399–406. [8] Grefenstette, J., Optimization of control parameters for genetic algorithms,IEEE Transactions on Systems, Man and Cybernetics, vol. 16, no. 1, pp. 122–128, 1986. [9] Shi, Y., and Eberhart, R. C., Parameter selection in particle swarm optimization, in Proceedings of the 7th International Conference on Evolutionary Programming VII, 1998, pp. 591–600. [10] Qin, A. K., and Li, X., Differential evolution on the CEC-2013 singleobjective continuous optimization testbed, in Proc. IEEE Congress on Evolutionary Computation (CEC), Cancun, Mexico, Jun. 2013, pp. 1099–1106. [11] Yu, J. J. Q., and Li, V. O. K., A social spider algorithm for global optimization, Appl. Soft Comput., vol. 30, pp. 614–627, 2015. [12] Uetz, G., Foraging strategies of spiders, Trends in Ecology and Evolution, vol. 7, no. 5, pp. 155–159, 1992. [13] Yu, J. J. Q., and Li, V. O. K., Base station switching problem for green cellular networks with social spider algorithm, in Proc. IEEE Congress on Evolutionary Computation (CEC), Beijing, China, Jul. 2014, pp. 1–7. [14] ——, “Power-controlled base station switching and cell zooming in green cellular networks, 2015, submitted for publication. [15] Cuevas, E., Cienguegos, M., Zaldvar, D., Perez Cisneros, M.: A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Systems with Applications 40(16), 6374–6384 (2013) [16] James J.Q, Victor O.K.: A Social Spider Algorithm for Global Algorithm. Technical Report No. TR 2013-004, The University of Hong Kong, October 2013 [17] Cuevas, E., Cienfuegos, M.: A new algorithm inspired in the behavior of the socialspider for constrained optimization. Expert Systems with Applications 41(2), 412–425 (2014) [18] Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization 39(3), 459–471 (2007) [19] Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Applied soft computing 8(1), 687–697 (2008) [20] Baker, M., Buyya, R., and Laforenza, D., The Grid: International efforts in global computing, [21] Alexandrov, A. D. , et al., SuperWeb: Research issues in Java-based global computing, Concurrency: Practice and Experience, vol.9, no. 6, June 1997, pp. 535-553. [22] Carde, L., Global computation, ACM Computing Surveys, vol. 28, no. 4, Dec, 1996, pp. 66-68. [23] Cuevas, E., Cienfuegos, M., Zaldívar, D., Pérez-Cisneros, M. A swarm optimization algorithm inspired in the behavior of the social-spider, Expert Systems with Applications, 40 (16), (2013), pp. 6374-6384 [24] [25] Socha, K., Dorigo, M., Ant colony optimization for continuous domains, European Journal of Operational Research 185 (3) (2008) 1155{1173. [26] Cuevas, E., M. Cienfuegos, D. Zaldvar, M. Prez-Cisneros, A swarm optimization algorithm inspired in the behavior of the social-spider, Expert Systems with Applications 40 (16) (2013) 6374C{6384. [27] MetwalliAnter, A., Social-Spider Optimization Algorithm, Faculty of Computers and informatics, 6 June 2015, BeniSuef University, Member of the Scientific Research Group in Egypt, [28] Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A. A., Jul. 2009. A detailed analysis of the KDD CUP 99 data set. In: Proc. 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1–6, [29] Liu, L., Yu, W., Liang, F., Griffith, D., Golmie, N., On deep reinforcement learning security for Industrial Internet of Things, Computer Communications, Available online 8 January 2021 [30] Kumar A., Jacold L., and Ananda A. L. (2004) “SCTP Vs TCP: Performance comparison in MANETs.” Proceedings of 29th Annual IEEE International Conference on Local Computer Networks 1: 431 – 432. [31] Kim D., Bae H., and Toh C.K. (2007) “Improving TCP-vegas performance over MANET Routing protocols.” IEEE Transactions on Vehicular Technology 56(1): 372 – 377. [32] Abouzeid A. A., and Azizoglu M. (2003) “Comprehensive performance analysis of a TCP session over a Wireless Fading Link with Queueing.” IEEE Transactions on Wireless Communication 2(2):344 - 356. [33] Manshahia M. S. (2016) “Wireless sensor networks: A survey.”, International Journal of Scientific & Engineering Research 7(4): 710-716. [34] Manshahia M. S. (2015) “Water wave optimization algorithm based congestion control and quality of service improvement in wireless sensor networks.” Transactions on Networks and Communications 5(4): 31-39 [35] Manshahia M. S., Dave M., and Singh S.B. (2016) “Firefly algorithm based clustering technique for wireless sensor networks.” Proc. International Conference on Wireless Communications, Signal Processing and Networking, Chennai, India. [36] Nguyen, D.; Ding, M.; Pathirana, P.; Seneviratne, A.; Li, J.; Poor, V. Cooperative Task Offloading and Block Mining in Blockchain-based Edge Computing with Multi-agent Deep Reinforcement Learning. IEEE Trans. Mob. Comput. 2021, 22, 2021–2037.[CrossRef] [37] C. You and K. Huang, “Multiuser resource allocation formobile-edge computation offloading,” in 2016 IEEE GlobalCommunications Conference (GLOBECOM), Washington,DC, USA, 2016. [38] T.Tran and D. Pompili, “Joint task offloading and resourceallocation for multi-server mobile-edge computing networks,”IEEE Transactions on Vehicular Technology, vol. 68, no. 1,pp. 856–868, 2019. [39] J. Wang, J. Pan, F. Esposito, P. Calyam, Z. Yang, andP. Mohapatra, “Edge cloud offloading algorithms: issues,methods, and perspectives,” ACM Computing Surveys,vol. 52, no. 1, 2020. [40] D. Han, W. Chen, and Y. Fang, “Joint channel and queue [41] aware scheduling for latency sensitive mobile edge computingwith power constraints,” IEEETransactions on Wireless Communications, vol. 19, no. 6, pp. 3938–3951, 2020 [42] X. Chen, Y. Cai, L. Li, M. Zhao, B. Champagne, and L. Hanzo,“Energy-efficient resource allocation for latency-sensitivemobile edge computing,” IEEE Transactions on VehicularTechnology, vol. 69, no. 2, pp. 2246–2262, 2020.