SP-Cache: Load-Balanced, Redundancy-Free Cluster Caching with Selective Partition

Speaker:        Dr. Wei WANG
                Department of Computer Science and Engineering
                Hong Kong University of Science and Technology

Title:          "SP-Cache: Load-Balanced, Redundancy-Free Cluster Caching
                 with Selective Partition"

Date:           Monday, 23 September 2019

Time:           4:00pm - 5:00pm

Venue:          Lecture Theater F (near lift no. 25/26), HKUST

Abstract:

Big data clusters increasingly employ in-memory solutions to improve I/O
performance. However, the routinely observed file popularity skew and load
imbalance create hot spots in a cluster, 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 talk, I will present an effective approach to achieve
load balancing without cache redundancy or encoding/decoding overhead. Our
solution, which we call 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 have
implemented SP-Cache in Alluxio, a popular in-memory distributed storage
for data-intensive clusters. Real cloud deployment and trace-driven
simulations show that, compared to the state-of-the-art solution, SP-Cache
reduces the file access latency by up to 40% in both the mean and the
tail, using 40% less memory.


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Biography:

Wei Wang is currently an Assistant Professor in the Department of Computer
Science and Engineering at the Hong Kong University of Science and
Technology (HKUST). He is also affiliated with the HKUST Big Data
Institute. Wei received the Ph.D. degree in Electrical and Computer
Engineering from the University of Toronto in 2015, and the M.Eng. and
B.Eng degrees in Electrical Engineering from Shanghai Jiao Tong
University. His research interests cover the broad area of distributed
systems, with special emphasis on big data and machine learning systems,
cloud computing, and computer networks. He is a recipient of the 2015
Chinese Government Award for Outstanding PhD Students Abroad and the Best
Paper Finalist Award at the USENIX ICAC 2013. He was recently named as the
Distinguished TPC member of IEEE INFOCOM 2018 and 2019.