The EELRU adaptive replacement algorithm

The wide performance gap between processors and disks ensures that effective page replacement remains an important consideration in modern systems. This paper presents early eviction LRU (EELRU), an adaptive replacement algorithm. EELRU uses aggregate recency information to recognize the reference behavior of a workload and to adjust its speed of adaptation. An on-line cost/benefit analysis guides replacement decisions. This analysis is based on the LRU stack model (LRUSM) of program behavior. Essentially, EELRU is an on-line approximation of an optimal algorithm for the LRUSM. We prove that EELRU offers strong theoretical guarantees of performance relative to the LRU replacement algorithm. EELRU can never be more than a factor of 3 worse than LRU, while in a common best case it can be better than LRU by a large factor (proportional to the number of pages in memory). al of EELRU is to provide a simple replacement algorithm that adapts to reference patterns at all scales. Thus, EELRU should perform well for a wider range of programs and memory sizes than other algorithms. Practical experiments validate this claim. For a large number of programs and wide ranges of memory sizes, we show that EELRU outperforms LRU, typically reducing misses by 10–30%, and occasionally by much more—sometimes by a factor of 2–10. It rarely performs worse than LRU, and then only by a small amount.