International Journal of Computer Networks and Applications

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A Hybrid Secure and Optimized Execution Pattern Analysis Based O-HMACSHA 3 Resource Allocation in Cloud Environment


Affiliations
1 Department of Computer Science and Engineering, MMEC, Maharishi Markandeshwar (Deemed to be University),Mullana, Ambala, Haryana., India
 

According to the analysis, several task scheduling methods have been implemented, such as the Particle Swarm Optimization (PSO) method, which has enhanced the performance of cloud data centers (DCs) in terms of various scheduling metrics. The scheduling issue in cloud computing (CC) is well-known to be NP-hard, with the main challenge arising from the exponential increase in the no. of possible outcomes or groupings as the problem size grows. Therefore, the key aim is to determine secure and optimal solutions for scheduling consumer tasks. In this study, a proposed method called Optimized-Hybrid Medium Access Control Secure Hash Algorithm 3 (O-HMACSHA3) is introduced for CC. The investigation aims to address the issue of reliable resource scheduling access for different tasks in the cloud environment, with a focus on reducing turnaround time (TAT) and energy consumption (EC). The proposed method utilizes optimization with PSO to achieve soft security in resource scheduling. To evaluate its effectiveness, the research method is compared with other task scheduling methods, including PSO and Fruit Fly-Based Simulated Annealing Optimization (FSAO) method, in terms of EC and time. The findings indicate significant improvements in performance metrics, with energy consumption reduced to 47.7 joules and TAT decreased to 316 milliseconds compared to existing methods. This is in contrast to the proposed method, which resulted in 57.3 joules and 479 milliseconds, respectively, for 20 tasks.

Keywords

Task Scheduling (TS), O-HMACSHA3 (Optimized-Hybrid Medium Access Control Secure Hash Algorithm), PSO (Particle Swarm Optimization), EC (Energy Consumption),TAT (Turnaround Time).
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  • Gupta, A., Garg, R. “Load balancing based task scheduling with ACO in cloud computing”, In 2017 International Conference on Computer and Applications (ICCA), 2017, (pp. 174-179).IEEE.

  • Rathore, N., Chana, I. “ Load balancing and job migration techniques in grid: a survey of recent trends”, Wireless personal communications, 2014, 79(3), 2089-2125.

  • Grewal, S. K., Mangla, N. “Deadline based Energy Efficient Scheduling Algorithm in Cloud Computing Environment”, In 2021 Fourth International Conference on Computational Intelligence and Communication Technologies (CCICT),2021, (pp. 383-388). IEEE.

  • Ms.Saranya G, Mr.Srinivasan J “Load Balancing Algorithm In Task Scheduling Process Using Cloud Computing”, International Research Journal of Engineering and Technology (IRJET), 2016, vol 3(5), pp. 1-8.

  • Singh S, Chana I “Cloud resource provisioning: survey, status and future research directions”. KnowlInfSyst, 2016, 49(3):1005–1069

  • Panda, SK., and Prasanta K. J. "Efficient task scheduling algorithms for heterogeneous multi-cloud environment." The Journal of Supercomputing 71 (2015): 1505-1533.

  • Liu L, Mei H, Xie B “Towards a multi-QoS human-centric cloud computing load balance resource allocation method”, J Supercomput 2016, 72(7):2488–2501.

  • Panda, S. K., Jana, P. K. “Load balanced task scheduling for cloud computing: A probabilistic approach”, Knowledge and Information Systems, 2019, 61(3), 1607-1631.

  • Nabi, S., Ibrahim, M., Jimenez, J. M. “DRALBA: dynamic and resource aware load balanced scheduling approach for cloud computing”, IEEE Access,2021, 9, 61283-61297.

  • Panda, S. K., Jana, P. K. “ A multi-objective task scheduling algorithm for heterogeneous multi-cloud environment”,In 2015 International Conference on Electronic Design, Computer Networks & Automated Verification (EDCAV) 2015 (pp. 82-87).IEEE.

  • Zuo, L., Shu, L., Dong, S., Zhu, C., Hara, T. “A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing”, Ieee Access, 2015, 3, 2687-2699.

  • Shishido, H. Y., Estrella, J. C., Toledo, C. F. M., Arantes, M. S. “Genetic-based algorithms applied to a workflow scheduling algorithm with security and deadline constraints in clouds”. Computers & Electrical Engineering, 2018, 69, 378-394.

  • Mubeen, A., Ibrahim, M., Bibi, N., Baz, M., Hamam, H., Cheikhrouhou, O. “Alts: An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing”. Processes, 2021,9(9), 1514.

  • Potluri, S., Rao, K. S. “Optimization model for QoS based task scheduling in cloud computing environment”. Indonesian Journal of Electrical Engineering and Computer Science, 2020, 18(2), 1081-1088.

  • Singh, H., Bhasin, A., Kaveri, P. R. “QRAS: efficient resource allocation for task scheduling in cloud computing”, SN Applied Sciences, 2021, 3(4), 1-7.

  • Ibrahim, M., Nabi, S., Hussain, R., Raza, M. S., Imran, M., Kazmi, S. A., Hussain, F. “A comparative analysis of task scheduling approaches in cloud computing”, In 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), 2020, (pp. 681-684).IEEE.

  • Afzal, S., Kavitha, G. “Load balancing in cloud computing–A hierarchical taxonomical classification”, Journal of Cloud Computing, 2019, 8(1), 1-24.

  • Kumar, M., Dubey, K., Sharma, S. C. “Elastic and flexible deadline constraint load balancing algorithm for cloud computing”. Procedia Computer Science, 2018, 125, 717-724.

  • Gabi, D., Dankolo, N. M., Muslim, A. A., Abraham, A., Joda, M. U., Zainal, A., Zakaria, Z, ”Dynamic scheduling of heterogeneous resources across mobile edge-cloud continuum using fruit fly-based simulated annealing optimization scheme”, Neural Computing and Applications, . 2022, pp. 1-21.

  • Saleh, H., Nashaat, H., Saber, W., Harb, H. M. “IPSO task scheduling algorithm for large scale data in cloud computing environment”, IEEE Access, 2018, 7, pp. 5412-5420.

  • Farhadian, F., Kashani, M. M. R., Rezazadeh, J., Farahbakhsh, R., & Sandrasegaran, K. “WITHDRAWN: An efficient IoT cloud energy consumption based on genetic algorithm”, Digital Communications and Networks, 2019.

  • Dordaie, N., Navimipour, N. J. “A hybrid particle swarm optimization and hill climbing algorithm for task scheduling in the cloud environments”, ICT Express, 2018, 4(4), 199-202.

  • Selvakumar, A., Gunasekaran, G. “A novel approach of load balancing and task scheduling using ant colony optimization algorithm”, International Journal of Software Innovation (IJSI), 2019, 7(2), 9-20.

  • Torabi, S., Safi-Esfahani, F. “A dynamic task scheduling framework based on chicken swarm and improved raven roosting optimization methods in cloud computing”, The Journal of Supercomputing, 2018, 74(6), 2581-2626.

  • Basu, S., Kannayaram, G., Ramasubbareddy, S., Venkatasubbaiah, C. “Improved genetic algorithm for monitoring of virtual machines in cloud environment”. In Smart Intelligent Computing and Applications 2019, (pp. 319-326). Springer, Singapore.

  • Basu, S., Karuppiah, M., Selvakumar, K., Li, K. C., Islam, S. H., Hassan, M. M., & Bhuiyan, M. Z. A. “ An intelligent/cognitive model of task scheduling for IoT applications in cloud computing environment”, Future Generation Computer Systems, 2018, 88, 254-261.

  • Shukri, S. E., Al-Sayyed, R., Hudaib, A., Mirjalili, S. “Enhanced multi-verse optimizer for task scheduling in cloud computing environments”, Expert Systems with Applications, 2021, 168, 114230.

  • Velliangiri, S., Karthikeyan, P., Xavier, V. A., Baswaraj, D. “Hybrid electro search with genetic algorithm for task scheduling in cloud computing”, Ain Shams Engineering Journal, 2021, 12(1), 631-639.

  • Negi, S., Rauthan, M. M. S., Vaisla, K. S., Panwar, N. “ CMODLB: an efficient load balancing approach in cloud computing environment”. The Journal of Supercomputing, 2021, pp. 1-53.

  • Krishna, A. V., Ramasubbareddy, S., Govinda, K. “ Task scheduling based on hybrid algorithm for cloud computing”, In International Conference on Intelligent Computing and Smart Communication 2019 (pp. 415-421).Springer, Singapore.

  • Topcuoglu, H., Hariri, S., Wu, M. Y. “Performance-effective and low-complexity task scheduling for heterogeneous computing”, IEEE transactions on parallel and distributed systems, 2002, 13(3), 260-274.

  • Mao, Y., Chen, X., & Li, X. (2014). Max–min task scheduling algorithm for load balance in cloud computing. In Proceedings of International Conference on Computer Science and Information Technology (pp. 457-465).Springer, New Delhi.

  • Li, C., Jiang, W., & Zou, X. (2009, December). Botnet: Survey and case study. In 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC) (pp. 1184-1187). IEEE.

  • R. K. Mondal, E. Nandi and D. Sarddar, “Load balancing scheduling with shortest load first,” International Journal of Grid Distribution Computing, vol. 8, no. 4, pp. 171-178, Oct. 2015

  • Mohapatra, S., Mohanty, S., &Rekha, K. S. (2013). Analysis of different variants in round robin algorithms for load balancing in cloud computing. International Journal of Computer Applications, 69(22), 17-21.

  • Devi, D. C., & Uthariaraj, V. R. (2016). Load balancing in cloud computing environment using improved weighted round robin algorithm for nonpreemptive dependent tasks. The scientific world journal, 2016.

  • Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). IEEE.

  • Mahato, D. P., Singh, R. S., Tripathi, A. K., &Maurya, A. K. (2017). On scheduling transactions in a grid processing system considering load through ant colony optimization. Applied Soft Computing, 61, 875-891.

  • Dasgupta, K., Mandal, B., Dutta, P., Mandal, J. K., & Dam, S. (2013). A genetic algorithm (ga) based load balancing strategy for cloud computing. Procedia Technology, 10, 340-347.

  • LD, D. B., & Krishna, P. V. (2013). Honey bee behavior inspired load balancing of tasks in cloud computing environments. Applied soft computing, 13(5), 2292-2303.

  • Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65-74). Springer, Berlin, Heidelberg.

  • Navimipour, N. J., &Milani, F. S. (2015). Task scheduling in the cloud computing based on the cuckoo search algorithm. International Journal of Modeling and Optimization, 5(1), 44.

  • Gupta, G., & Mangla, N. (2021). Energy Aware Metaheuristic Approaches to Virtual Machine Migration in Cloud Computing. In IOP Conference Series: Materials Science and Engineering (Vol. 1022, No. 1, p. 012050). IOP Publishing.

  • Almezeini, N., & Hafez, A. (2017). Task scheduling in cloud computing using lion optimization algorithm. International Journal of Advanced Computer Science and Applications, 8(11).

  • Kumar, M., Sharma, S. C., Goel, A., & Singh, S. P. (2019). A comprehensive survey for scheduling techniques in cloud computing. Journal of Network and Computer Applications, 143, 1-33. How to cite this article:

  • Dubey, K., Kumar, M., & Sharma, S. C. (2018). Modified HEFT algorithm for task scheduling in cloud environment. Procedia Computer Science, 125, 725-732.

  • Chen, H., Wang, F., Helian, N., & Akanmu, G. (2013, February). User-priority guided Min-Min scheduling algorithm for load balancing in cloud computing. In 2013 National Conference on Parallel computing technologies (PARCOMPTECH) (pp. 1-8).IEEE.

  • Mao, Y., Chen, X., & Li, X. (2014). Max–min task scheduling algorithm for load balance in cloud computing. In Proceedings of International Conference on Computer Science and Information Technology (pp. 457-465).Springer, New Delhi.

  • Mondal, R. K., Nandi, E., &Sarddar, D. (2015). Load balancing scheduling with shortest load first. International Journal of Grid and Distributed Computing, 8(4), 171-178.

  • Ramezani, F., Lu, J., & Hussain, F. K. (2014). Task-based system load balancing in cloud computing using particle swarm optimization. International journal of parallel programming, 42(5), 739-754.

  • Sun, W., Zhang, N., Wang, H., Yin, W., &Qiu, T. (2013, December). PACO: A period ACO based scheduling algorithm in cloud computing. In 2013 International Conference on Cloud Computing and Big Data (pp. 482-486).IEEE.

  • Chhabra, A., Singh, G., & Kahlon, K. S. (2020). QoS-aware energy-efficient task scheduling on HPC cloud infrastructures using swarm-intelligence meta-heuristics. Comp. Mater. Cont, 64, 813-834.

  • Mangla, N. (2021, October). Soft Security Resource Scheduling Issues in Cloud Computing: A Review. In 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC) (pp. 678-684). IEEE.


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  • A Hybrid Secure and Optimized Execution Pattern Analysis Based O-HMACSHA 3 Resource Allocation in Cloud Environment

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Authors

Himanshu
Department of Computer Science and Engineering, MMEC, Maharishi Markandeshwar (Deemed to be University),Mullana, Ambala, Haryana., India
Neeraj Mangla
Department of Computer Science and Engineering, MMEC, Maharishi Markandeshwar (Deemed to be University),Mullana, Ambala, Haryana., India

Abstract


According to the analysis, several task scheduling methods have been implemented, such as the Particle Swarm Optimization (PSO) method, which has enhanced the performance of cloud data centers (DCs) in terms of various scheduling metrics. The scheduling issue in cloud computing (CC) is well-known to be NP-hard, with the main challenge arising from the exponential increase in the no. of possible outcomes or groupings as the problem size grows. Therefore, the key aim is to determine secure and optimal solutions for scheduling consumer tasks. In this study, a proposed method called Optimized-Hybrid Medium Access Control Secure Hash Algorithm 3 (O-HMACSHA3) is introduced for CC. The investigation aims to address the issue of reliable resource scheduling access for different tasks in the cloud environment, with a focus on reducing turnaround time (TAT) and energy consumption (EC). The proposed method utilizes optimization with PSO to achieve soft security in resource scheduling. To evaluate its effectiveness, the research method is compared with other task scheduling methods, including PSO and Fruit Fly-Based Simulated Annealing Optimization (FSAO) method, in terms of EC and time. The findings indicate significant improvements in performance metrics, with energy consumption reduced to 47.7 joules and TAT decreased to 316 milliseconds compared to existing methods. This is in contrast to the proposed method, which resulted in 57.3 joules and 479 milliseconds, respectively, for 20 tasks.

Keywords


Task Scheduling (TS), O-HMACSHA3 (Optimized-Hybrid Medium Access Control Secure Hash Algorithm), PSO (Particle Swarm Optimization), EC (Energy Consumption),TAT (Turnaround Time).

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DOI: https://doi.org/10.22247/ijcna%2F2023%2F221890