OpuS: Fair and Efficient Cache Sharing for In-Memory Data Analytics

Yinghao Yu, Wei Wang, Jun Zhang, Qizhen Weng, Khaled Ben Letaief

Abstract: We study the fair cache allocation problem in shared cloud environments, where many users and applications contend for the main memory to cache shared datasets or files. Unlike other resources such as CPUs and networks, in-memory caches can be non-exclusively shared across many users, e.g., a cached columnar dataset queried by many Spark SQL jobs. This results in a unique challenge of the "free-riding" problem, where a user lies about its caching preferences to trick other users to cache files for it, using their allocated cache space. We show that existing cache allocation policies either suffer from such manipulations or result in poor efficiency. To address this problem, we propose a new cache allocation algorithm, termed OpuS, or Opportunistic Sharing for high efficiency. We show that OpuS provides performance isolation between users and is strategy-proof against "free-riding" manipulations. We have implemented OpuS as a pluggable cache manager in Alluxio, a popular memory-centric filesystem. Cluster deployment and trace-driven simulations demonstrate that OpuS allocates each user a fair share of caches while achieving near-optimal efficiency in cache utilization.