Adaptive Replacement Algorithm Templates and EELRU

Abstract

Replacement algorithms are a major component of operating system design. Every replacement algorithm, however, is pathologically bad for some scenarios, and often these scenarios correspond to common program patterns. This has prompted the design of adaptive replacement algorithms: algorithms that emulate two (or more) basic algorithms and pick the decision of the best one based on recent past behavior. The authors are interested in a special case of adaptive replacement algorithms, which are instances of adaptive replacement templates (ARTs). An ART is a template that can be applied to any two algorithms and yield a combination with some guarantees on the properties of the combination, relative to the properties of the component algorithm. For instance, they show ARTs that for any two algorithms A and B produce a combined algorithm AB that is guaranteed to emulate within a factor of 2 the better of A and B on the current input. They call this guarantee a robustness property. This performance guarantee of ARTs makes them effective but a naïve implementation may not be practically efficient—e.g., because it requires significant space to emulate both component algorithms at the same time. In practice, instantiations of an ART can be specialized to be highly efficient. The authors demonstrate this through a case study. They present the EELRU adaptive replacement algorithm, which pre-dates ARTs but is truly a highly optimized multiple ART instantiation. EELRU is well-known in the research literature and outperforms the well-known LRU algorithm when there is benefit to be gained, while emulating LRU otherwise.