A Cooperative Caching System in Heterogeneous Edge Networks

Recently, the rapid growth of video content and the increasing demand for high Quality of Experience (QoE) have significantly strained the backbone network. Edge caching is a promising approach to alleviate the strain by caching content closer to users. However, it confronts challenges stemming from the low capability of individual edge nodes and the high density of their distribution, resulting in low hit ratios and unbalanced workloads. In this paper, we conduct in-depth analyses of these challenges and formulate a typical cooperative edge caching problem. Based on the insights, we introduce MagNet, a cooperative edge caching system featuring two key mechanisms: Automatic Content Congregating (ACC) and Mutual Assistance Group (MAG). ACC improves hit ratios by intelligently guiding requests to their optimal edges, thereby facilitating content aggregation. Complementing this, Quick Cache is implemented to accelerate this congregation process by prefetching content and optimizing cache space, effectively boosting hit ratios. MAG, on the other hand, achieves workload balance by dynamically forming groups to augment edge capabilities and redistribute requests on overloaded edges. To elucidate the design principles of MagNet, we conduct detailed component-level comparisons and quantitative analyses. To validate the overall performance, we compare it with various caching solutions using real-world datasets, demonstrating significant performance improvements.