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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  • 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