====== CACHEUS ======

===== Authors =====

  * Liana Valdez 
  * Farzana Yusuf
  * Steven Lyons
  * Eysler Paz
  * [[http://www.cs.fiu.edu/~raju/|Raju Rangaswami]]
  * Jason Liu
  * Ming Zhao
  * Giri Narasimhan

===== Abstract =====

  Recent advances in machine learning open up new and attractive approaches 
  for solving classic problems in computing systems. For storage systems, 
  cache replacement is one such problem because of its enormous impact on 
  performance. CACHEUS represents a new class of fully adaptive, 
  machine-learned caching algorithms that utilize a combination of experts 
  designed to address a variety of workload primitive types. The experts 
  used by CACHEUS include the state-of-the-art ARC, LIRS and LFU, and two 
  new ones – SR-LRU, a scan-resistant version of LRU, and CR-LFU, a 
  churn-resistant version of LFU. CACHEUS using the newly proposed 
  lightweight experts, SR-LRU and CR-LFU, is the most consistently performing 
  caching algorithm across a range of workloads and cache sizes. Furthermore, 
  CACHEUS enables augmenting state-of-the-art algorithms (e.g., LIRS, ARC) 
  by combining it with a complementary cache replacement algorithm (e.g., 
  LFU) to to better handle a wider variety of workload primitive types.
===== Publications =====

  *  **Learning Cache Replacement with CACHEUS** {{http://www.cs.fiu.edu/~raju/WWW/publications/fast21/paper.pdf|pdf}}\\ Liana V. Rodriguez, Farzana Yusuf, Steven Lyons, Eysler Paz, Raju Rangaswami, Jason Liu, Ming Zhao, and Giri Narsimhan\\ Proceedings of the USENIX Conference on File and Storage Technologies (FAST), 2021.



===== Public Software =====

  * Cacheus sources can be accessed at [[https://github.com/sylab/cacheus|here]]. This repo includes the parsing code to run cache simulation with several workloads available on the [[http://iotta.snia.org/tracetypes/3|SNIA Website]] such as FIU, MSR, Nexus 5 Smartphone, CloudCache and CloudVPS.

===== Acknowledgement of Support =====

This work was supported in part by a NetApp Faculty Fellowship, and NSF grants CCF-1718335, CNS-1563883, and CNS-1956229.