Enhancing Computational Offloading for Sustainable Smart Cities: A Deep Belief Network Approach
Subject Areas : Cloud computing
Kaebeh Yaeghoobi
1
,
Mahsa Bakhshandeh N
2
1 -
2 -
Keywords: Computational Offloading, Cloud Computing, Deep Belief Network, Response Time, Resource Management, Sustainable Smart Cities, Real-time Management,
Abstract :
The use of mobile devices with limited processing power has surged in recent years, alongside the expansion of cloud and fog computing across various sectors. These devices can handle small to medium computing tasks, but they fall short when it comes to large-scale processes, making computational offloading a crucial solution. Cloud computing and fog computing provide an effective platform for offloading tasks from mobile devices. However, critical real-time applications necessitate a near-edge approach to managing the computational load. Significant challenges exist in optimizing response times for effective offloading in cloud computing. This research introduces a framework for predicting response times using Deep Belief Network (DBN) learning to enhance offloading performance. Implementing a DBN aims to minimize response times and resource consumption, thereby improving the overall efficiency of offloading processes. The framework is designed to predict response times accurately, ensuring timely completion of tasks and efficient use of resources. Simulation results using multiple models show that the use of DBN significantly reduces processing, response, and offloading times compared to other algorithms. Consequently, the DBN algorithm proves to be more efficient in predicting response times and enhancing offloading performance. By leveraging the capabilities of DBN, this framework provides a promising solution for optimizing computational offloading in cloud computing environments. This enhances the performance of mobile devices and ensures the reliability and efficiency of real-time applications, direct the way for more advanced and responsive computing technologies.
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