Energy Efficient Double Critic Deep Deterministic Policy Gradient Framework for Fog Computing
Abstract
Nowadays the data is growing at a faster pace and the big data applications are required to be more agile and flexible. There is a need for a decentralized model to carry out the required substantial amount of computation across edge devices as they has led to the innovation of fog computing. Energy consumption among the edge devices is one of the potential threatening issues in fog computing. Their high energy demand also contributes to higher computation cost. In this paper Double Critic (DC) approach is enforced over the Deep Deterministic Policy Gradient (DDPG) technique to design the DC-DDPG framework which formulates high quality energy efficiency policies for fog computing. The performance of the proposed framework is outstanding compared to existing works based on the metrics like energy consumption, response time, total cost, and throughput. They are measured under two different fog computing scenarios i.e., fog layer with multiple entities in a region and fog layer with multiple entities in multiple regions. Mathematical modeling reveals that the energy efficiency policies formulated are of high quality as they satisfy the quality assurance metrics, such as empirical correctness, robustness, model relevance, and data privacy. copy; 2022 IEEE.
Publication Title
2022 IEEE World AI IoT Congress, AIIoT 2022
Recommended Citation
Krishnamurthy, B., & Shiva, S. (2022). Energy Efficient Double Critic Deep Deterministic Policy Gradient Framework for Fog Computing. 2022 IEEE World AI IoT Congress, AIIoT 2022, 521-526. https://doi.org/10.1109/AIIoT54504.2022.9817157
