Comprehensive Review of Collaborative Data Caching in Edge Computing

Edge computing has gained significant attention due to the swift development of wireless communication technology. As more smart, data-intensive applications and Internet of Things (IoT) devices are used, the amount of data traffic grows at an exponential rate. This means that we need to manage data effectively so that services can be scalable, bandwidth-efficient, and latency-sensitive. Collaborative data caching has emerged as an essential technique in this context to meet the storage and retrieval needs of edge computing systems. We thoroughly examine the current collaborative caching techniques in edge computing, with a focus on recent developments and emerging approaches. We have categorized collaborative caching strategies into four main types: models based on stochastic, game theoretic, and mathematical methods to deal with uncertainty in network conditions and optimize resource allocation; machine learning-based models that employ artificial intelligence (AI) for content popularity prediction and cache optimization; heuristic models providing lightweight solutions for cache placement and replacement; and hybrid models combining multiple strategies. Each of these models is intended to maximize performance under various network and data conditions. We conclude by looking into some of the open problems and difficulties with edge caching to promote further research in this field.