Saad Muhammad, Enam Rabia Noor, Qureshi Rehan
Computer Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan.
Software Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan.
Front Big Data. 2024 Feb 21;7:1358486. doi: 10.3389/fdata.2024.1358486. eCollection 2024.
As the volume and velocity of Big Data continue to grow, traditional cloud computing approaches struggle to meet the demands of real-time processing and low latency. Fog computing, with its distributed network of edge devices, emerges as a compelling solution. However, efficient task scheduling in fog computing remains a challenge due to its inherently multi-objective nature, balancing factors like execution time, response time, and resource utilization. This paper proposes a hybrid Genetic Algorithm (GA)-Particle Swarm Optimization (PSO) algorithm to optimize multi-objective task scheduling in fog computing environments. The hybrid approach combines the strengths of GA and PSO, achieving effective exploration and exploitation of the search space, leading to improved performance compared to traditional single-algorithm approaches. The proposed hybrid algorithm results improved the execution time by 85.68% when compared with GA algorithm, by 84% when compared with Hybrid PWOA and by 51.03% when compared with PSO algorithm as well as it improved the response time by 67.28% when compared with GA algorithm, by 54.24% when compared with Hybrid PWOA and by 75.40% when compared with PSO algorithm as well as it improved the completion time by 68.69% when compared with GA algorithm, by 98.91% when compared with Hybrid PWOA and by 75.90% when compared with PSO algorithm when various tasks inputs are given. The proposed hybrid algorithm results also improved the execution time by 84.87% when compared with GA algorithm, by 88.64% when compared with Hybrid PWOA and by 85.07% when compared with PSO algorithm it improved the response time by 65.92% when compared with GA algorithm, by 80.51% when compared with Hybrid PWOA and by 85.26% when compared with PSO algorithm as well as it improved the completion time by 67.60% when compared with GA algorithm, by 81.34% when compared with Hybrid PWOA and by 85.23% when compared with PSO algorithm when various fog nodes are given.
随着大数据的体量和速度持续增长，传统云计算方法难以满足实时处理和低延迟的需求。雾计算凭借其边缘设备的分布式网络，成为一种颇具吸引力的解决方案。然而，由于雾计算本质上具有多目标特性，要平衡执行时间、响应时间和资源利用率等因素，雾计算中的高效任务调度仍是一项挑战。本文提出一种混合遗传算法（GA）-粒子群优化（PSO）算法，以优化雾计算环境中的多目标任务调度。这种混合方法结合了GA和PSO的优势，实现了对搜索空间的有效探索和利用，与传统单算法方法相比性能得到提升。当给出各种任务输入时，所提出的混合算法与GA算法相比，执行时间缩短了85.68%，与混合鲸鱼优化算法（Hybrid PWOA）相比缩短了84%，与PSO算法相比缩短了51.03%；响应时间与GA算法相比缩短了67.28%，与Hybrid PWOA相比缩短了54.24%，与PSO算法相比缩短了75.40%；完成时间与GA算法相比缩短了68.69%，与Hybrid PWOA相比缩短了98.91%，与PSO算法相比缩短了75.90%。当给出各种雾节点时，所提出的混合算法与GA算法相比，执行时间缩短了84.87%，与Hybrid PWOA相比缩短了88.64%，与PSO算法相比缩短了85.07%；响应时间与GA算法相比缩短了65.92%，与Hybrid PWOA相比缩短了80.51%，与PSO算法相比缩短了85.26%；完成时间与GA算法相比缩短了67.60%，与Hybrid PWOA相比缩短了81.34%，与PSO算法相比缩短了85.23%。
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