State Transition Field: A New Framework for Mobile Dynamic Caching

Abstract

AbstractIn this chapter, we present research on dynamic probabilistic caching. First, we establish a theoretic model, i.e., the state transition field (STF) theory. The STF characterizes the dynamic change of the cache state distribution in the vector space as a result of content requests and replacements. We consider the case of time-invariant content popularity first and show that the STF can be used to analyze replacement schemes. Then, in the case of time-varying content popularity, we investigate the impact of time-varying content popularity on the instantaneous STF and how such impact affects the performance of a replacement scheme. We show that many metrics, such as instantaneous state caching probability and average cache hit probability over an arbitrary sequence of requests, can be found using the instantaneous STF. Last, we design dynamic probabilistic caching that converges to the optimal content caching probabilities under time-invariant content popularity and adapts to the time-varying instantaneous content popularity under time-varying content popularity.KeywordsMobile edge cachingDynamic probabilistic cachingCache replacement policyTime-varying content popularityCache state transition