Time-shift replacement algorithm for main memory performance optimization

Page replacement algorithms of main memory in modern operating systems are crucial in system performance. When memory is full, a page replacement algorithm exploits temporal locality and frequency of page references to evict the page that is least likely to be accessed in the near future. Subsequently, loading the majority of data directly from memory improves performance by reducing I/O waits of accessing slow storage. Research of replacement algorithms that maximizes hit ratio while incurring as less overhead as possible has been constantly studied. In this paper, we propose a time-shift least recently used (TSLRU) algorithm that converts frequency information of page references into temporal locality. Frequent accesses of a page are thus recognized and accumulated in terms of time. Moreover, pages being loaded into memory for the first time are not necessarily the most recently used pages. As a result, one-pass pages are evicted sooner in our algorithm than in traditional LRU algorithm. Our performance evaluations show that the TSLRU outperforms conventional page replacement algorithms on both artificial and real application traces. For example, hit ratio of TSLRU advances ARC by 4.17% and LRU by 5.91% on normal distributed workloads. Moreover, TSLRU outperforms ARC by over 2% on half of the application traces tested.