GAP: Game Theory-Based Approach for Reliability and Power Management in Emerging Fog Computing

TL;DR

This paper introduces a game theory-based model for balancing power consumption and reliability in fog computing, achieving significant energy savings and performance improvements through strategic task management.

Contribution

It presents a novel non-cooperative game theory approach combined with CPB and DVFS strategies to optimize power and reliability in fog computing environments.

Findings

Up to 35% reduction in energy consumption

41% decrease in wait time

31% shorter task completion time

Abstract

Fog computing brings about a transformative shift in data management, presenting unprecedented opportunities for enhanced performance and reduced latency. However, one of the key aspects of fog computing revolves around ensuring efficient power and reliability management. To address this challenge, we have introduced a novel model that proposes a non-cooperative game theory-based strategy to strike a balance between power consumption and reliability in decision-making processes. Our proposed model capitalizes on the Cold Primary/Backup strategy (CPB) to guarantee reliability target by re-executing tasks to different nodes when a fault occurs, while also leveraging Dynamic Voltage and Frequency Scaling (DVFS) to reduce power consumption during task execution and maximizing overall efficiency. Non-cooperative game theory plays a pivotal role in our model, as it facilitates the development of strategies and solutions that uphold reliability while reducing power consumption. By treating the trade-off between power and reliability as a non-cooperative game, our proposed method yields significant energy savings, with up to a 35% reduction in energy consumption, 41% decrease in wait time, and 31% shorter completion time compared to state-of-the-art approaches. Our findings underscore the value of game theory in optimizing power and reliability within fog computing environments, demonstrating its potential for driving substantial improvements