Paper Info
Title
An in-memory object caching framework with adaptive load balancing
Abstract
The extreme latency and throughput requirements of modern web applications are driving the use of distributed in-memory object caches such as Memcached. While extant caching systems scale-out seamlessly, their use in the cloud --- with its unique cost and multi-tenancy dynamics --- presents unique opportunities and design challenges. In this paper, we propose MBal, a high-performance in-memory object caching framework with adaptive <u>M</u>ultiphase load <u>B</u>alancing, which supports not only horizontal (scale-out) but vertical (scale-up) scalability as well. MBal is able to make efficient use of available resources in the cloud through its fine-grained, partitioned, lockless design. This design also lends itself naturally to provide adaptive load balancing both within a server and across the cache cluster through an event-driven, multi-phased load balancer. While individual load balancing approaches are being lever-aged in in-memory caches, MBal goes beyond the extant systems and offers a holistic solution wherein the load balancing model tracks hotspots and applies different strategies based on imbalance severity -- key replication, server-local or cross-server coordinated data migration. Performance evaluation on an 8-core commodity server shows that compared to a state-of-the-art approach, MBal scales with number of cores and executes 2.3x and 12x more queries/second for GET and SET operations, respectively.
Year
DOI
Venue
2015
10.1145/2741948.2741967
EuroSys
Field
DocType
Citations 
Load balancing (computing),Cache,Computer science,Set operations,Real-time computing,Throughput,Web application,Data migration,Scalability,Cloud computing,Distributed computing
Conference
14
PageRank 
References 
Authors
0.62
31
3
Name
Order
Citations
PageRank
Yue Cheng1759.77
Aayush Gupta221311.81
Ali R. Butt365147.51