SP-Cache: Load-balanced, Redundancy-free Cluster Caching with Selective Partition

View Researcher's Other Codes

Python code for the paper: “SP-Cache: Load-balanced, Redundancy-free Cluster Caching with Selective Partition”.

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Please contact us in case of a broken link from here

Authors Yinghao Yu, Renfei Huang, Wei Wang, Jun Zhang, and Khaled Ben Letaief
Journal/Conference Name 2018 Proceedings of IEEE/ACM International Conference for High Performance Computing, Networking, Storage, and Analysis (SC18)
Paper Category
Paper Abstract Data-intensive clusters increasingly employ in-memory solutions to improve I/O performance. However, the routinely observed file popularity skew and load imbalance create hot spots, which significantly degrade the benefits of in-memory caching. Common approaches to tame load imbalance include copying multiple replicas of hot files and creating parity chunks using storage codes. Yet, these techniques either suffer from high memory overhead due to cache redundancy or incur non-trivial encoding/decoding complexity. In this paper, we propose an effective approach to achieve load balancing without cache redundancy or encoding/decoding overhead. Our solution, termed SP-Cache,selectively partitions files based on their popularity and evenly caches those partitions across the cluster. We develop an efficient algorithm to determine the optimal number of partitions for a hot file—too few partitions are incapable of mitigating hot spots, while too many are susceptible to stragglers. We implemented SP-Cache in Alluxio, a popular in-memory distributed storage for data-intensive clusters. EC2 deployment and trace-driven simulations show that, compared to the state-of-the-art solution called EC-Cache [1], SP-Cache reduces the file access latency by up to 40% in both the mean and the tail, using 40%less memory.
Date of publication 2018
Code Programming Language Python

Copyright Researcher 2021