Exploiting Correlations in Request Streams: A Case for Hybrid Caching in Cache Networks

Abstract

With the increasing popularity of cache networks, in recent years, multiple static and dynamic caching strategies have been proposed that seek to improve user-level performance. Most existing caching strategies rely heavily on assumptions such as content popularity following a well-known Zipfian distribution and request streams following an Independent Reference Model (IRM). In this paper, we consider multiple real-world user request stream traces to investigate the validity of these assumptions and observe that they do not hold true. We conduct a detailed factor analysis and observe that violation of the IRM assumption significantly impacts the performance of caching strategies. We identify the interplay between the skewness of the content popularity distribution and the request stream correlation among unpopular content as the key factor impacting performance. We identify that in the high popularity skewness-low correlation regime, static caching outperforms dynamic caching, while the reverse is true in the low popularity skewness-high correlation regime. For the high popularity skewness-high correlation regime, static and dynamic caching provide similar performance. For this scenario, we propose Hybrid Caching that effectively combines static and dynamic caching strategies. The main idea is to split the cache into two parts—a static cache that statically caches content based on popularity and a dynamic cache that exploits the correlation in request streams. We conduct experiments on multiple real-world networks (e.g., WIDE, GEANT, GARR) and demonstrate that Hybrid Caching outperforms static or dynamic caching alone in the high popularity skewness-high correlation regime.