Soft Cache Hits: Improving Performance Through Recommendation and Delivery of Related Content

Abstract

Pushing popular content to small cells with local storage (“helper” nodes) has been proposed to cope with the ever-growing data demand. Nevertheless, the collective storage of a few nearby helper nodes may not suffice to achieve a high hit rate in practice. In this paper, we introduce the concept of “soft cache hits” (SCHs). An SCH occurs if a user’s requested content is not in the local cache, but the user can be (partially) satisfied by a related content that is. In case of a cache miss, an application proxy (e.g., YouTube) running close to the helper node (e.g., at a multi-access edge computing server) can recommend the most related files that are locally cached. This system could be activated during periods of predicted congestion, or for selected users (e.g., low-cost plans), to improve cache hit ratio with limited (and tunable) user quality of experience performance impact. Beyond introducing a model for soft cache hits, our next contribution is to show that the optimal caching policy should be revisited when SCHs are allowed. In fact, we show that optimal caching with SCH is NP-hard even for a single cache . To this end, we formulate the optimal femto-caching problem with SCH in a sufficiently generic setup and propose efficient algorithms with provable performance. Finally, we use a large range of real datasets to corroborate our proposal.