Title :
SKVM: Scaling in-memory Key-Value store on multicore
Author :
Ran Zheng;Wenjin Wang;Hai Jin;Qin Zhang
Author_Institution :
Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
fDate :
7/1/2015 12:00:00 AM
Abstract :
SKVM is a high performance in-memory Key-Value (KV) store for multicore, which is designed for high concurrent data access. There are some problems of existing systems dealing with high concurrent data processing on multicore: lock competition, cache coherency overhead, and large numbers of concurrent network connections. To solve the problems and make the in-memory KV store scale well on multicore, high concurrent data access is divided into two steps: high concurrent connection processing and high concurrent data processing. Half sync/half async model (HSHA) is adopted to eliminate network bottleneck, which can support high concurrent network connection. Through data partition, lock competition is eliminated and cache movement is reduced. Furthermore, consistent hash is adopted as data distribution strategy which can improve the scalability of system on multicore. Though some of these ideas appear elsewhere, SKVM is the first to combine them together. The experimental results show that SKVM can reach at most 2.4x higher throughput than Memcached, and scales near linearly with the number of cores under any workload.
Keywords :
"Multicore processing","Instruction sets","Scalability","Data processing","Computational modeling","Message systems","Data structures"
Conference_Titel :
Computers and Communication (ISCC), 2015 IEEE Symposium on
DOI :
10.1109/ISCC.2015.7405580