Daniel Berend

The goal of this paper is to present an efficient realistic metric for evaluating cache scheduling algorithms in multi-user multi-cache environments. In a previous work, the requests sequence was set deliberately by an opponent (offline optimal) algorithm in an extremely unrealistic way, leading to an unlimited competitive ratio and to extremely unreasonable and unrealistic cache management strategies. In this paper, we propose to analyze the performance of cache management in a typical scenario, i.e., we consider all possibilities with their (realistic) distribution. In other words, we analyze the average case and not the worst case of scheduling scenarios. In addition, we present an efficient, according to our novel average case analysis, online heuristic algorithm for cache scheduling. The algorithm is based on machine-learning concepts, it is flexible and easy to implement.