Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application.

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.

Strengthening the Bridge Between Academic and the Industry Through the Academia-Industry Collaboration Plan Design Model.

The study has been undertaken to integrate two different aspects of the triple helix model: universities and the industry. Special attention has been paid to the prevailing difference between the two, hampering their working as a coherent unit. Integrating the existing knowledge in the study, we proposed the Academia-Industry Collaboration Plan (AICP) design model. The model comprises processes, methods or approaches, and tools. Processes serve as a road map to third parties for establishing collaboration between academia and the industry. It has all the essential process models and a series of steps that help minimize the organizational complexity of the collaboration process between academia and the industry. Methods or approaches serve the purpose of implementing those processes effectively. Finally, appropriate tools are selected to integrate possible collaboration improvements that lead to innovation.