Smart caching based on user behavior for mobile edge computing

Abstract

With the explosive growth in wireless data traffic, it is difficult to support the massive consumption of backhaul link bandwidth. Mobile edge computing (MEC) uses edge caching to store content closer to the end user in case to reduce the computational overhead and latency. Caching decisions depend on user demand, which is associated with user behavior. Popular content shows the preferences of user behavior. Combining user context behavior to benefit caching, our optimization objective is to maximize the hit rate of contents within limited time. This optimization problem can be modeled as a knapsack problem. Even if the context and content popularity are known for all circumstances, it is still NP-hard. We thus propose a heuristic Smart caching algorithm (Smart-caching) within the MEC paradigm. Smart-caching constructs the user behavior preferences to reflect different user demands for content. Based on this construct, Smart-caching employs contextual zoom caching to find the context-aware content popularity with diversity via partitioning and exploring the context space. Our experiments are implemented with a real large-volume dataset, i.e., China Mobile User Detail Record (UDR). The results show that Smart-caching achieves a better cache hit rate and stability with low overhead compared with other related caching algorithms.