Collaborative Fog Computing for Computation Reuse Based on Popularity of Services in IoT

Fog computing has emerged as a critical paradigm for processing data closer to the edge. Managing fog nodes effectively is essential for optimizing performance. This study explores a new strategy for reducing response time in IoT, focusing on computation reuse. Computation reuse is a technique used in edge computing to prevent redundant processing. This approach helps in eliminating repetitive calculations, enhancing the capacity of fog computing resources. Although computation reuse saves a significant number of computational resources when the result is cached, it also incurs extra computational costs due to operations like hashing and similarity calculation. It may be beneficial to directly process tasks without searching for the cached results, especially when the probability of successfully finding the target result is low. A novel computation reuse approach among interconnected fog nodes is proposed, leveraging parallel processing and popularity-aware caching to minimize response times and optimize storage. Our method utilizes Locality Sensitive Hashing (LSH) to detect similar computation requests and executes parallel processes for cache lookup and service computation to avoid latency penalties from cache misses. Unlike prior Computation Reuse methods that serialize lookup and execution, the proposed parallel design mitigates cache-miss latency by ensuring that unsuccessful reuse attempts do not increase response time beyond that of direct execution. Evaluations demonstrate significant improvements in response time, reuse rates, energy efficiency, and scalability compared to baseline methods. This work highlights the potential of cooperative computation reuse in fog networks and lays the groundwork for future research in dynamic network topologies.