Transient and steady-state regime of a family of list-based cache replacement algorithms
Faculty of Sciences. Mathematics and Computer Science
Publication type
Computer. Automation
Source (journal)
Performance evaluation review
43(2015) , p. 123-136
Target language
English (eng)
Full text (Publishers DOI)
University of Antwerp
In this paper we study the performance of a family of cache replacement algorithms. The cache is decomposed into lists. Items enter the cache via the rst list. An item enters the cache via the rst list and jumps to the next list whenever a hit on it occurs. The classical policies FIFO, RANDOM, CLIMB and its hybrids are obtained as special cases. We present explicit expressions for the cache content distribution and miss probability under the IRM model. We develop an algorithm with a time complexity that is polynomial in the cache size and linear in the number of items to compute the exact miss probability. We introduce lower and upper bounds on the latter that can be computed in a time that is linear in the cache size times the number of items. We further introduce a mean eld model to approximate the transient behavior of the miss probability and prove that this model becomes exact as the cache size and number of items tends to innity. We show that the set of ODEs associated to the mean eld model has a unique xed point that can be used to approximate the miss probability in case the exact computation becomes too time consuming. Using this approximation, we provide guidelines on how to select a replacement algorithm within the family considered such that a good trade-o is achieved between the cache reactivity and its steady-state hit probability. We simulate these cache replacement algorithms on traces of real data and show that they can outperform LRU. Finally, we also disprove the well-known conjecture that the CLIMB algorithm is the optimal nite-memory replacement algorithm under the IRM model.
Full text (open access)