Rein: Taming Tail Latency in Key-Value Stores via Multiget Scheduling
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Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionComputer Science Program
Date
2017-04-17Online Publication Date
2017-04-17Print Publication Date
2017Permanent link to this record
http://hdl.handle.net/10754/625020
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We tackle the problem of reducing tail latencies in distributed key-value stores, such as the popular Cassandra database.We focus on workloads of multiget requests, which batch together access to several data elements and parallelize read operations across the data store machines. We first analyze a production trace of a real system and quantify the skew due to multiget sizes, key popularity, and other factors. We then proceed to identify opportunities for reduction of tail latencies by recognizing the composition of aggregate requests and by carefully scheduling bottleneck operations that can otherwise create excessive queues. We design and implement a system called Rein, which reduces latency via inter-multiget scheduling using low overhead techniques. We extensively evaluate Rein via experiments in Amazon Web Services (AWS) and simulations. Our scheduling algorithms reduce the median, 95, and 99 percentile latencies by factors of 1.5, 1.5, and 1.9, respectively.Citation
Reda W, Canini M, Suresh L, Kostić D, Braithwaite S (2017) Rein. Proceedings of the Twelfth European Conference on Computer Systems - EuroSys ’17. Available: http://dx.doi.org/10.1145/3064176.3064209.Sponsors
Waleed Reda was supported by a fellowship from the Erasmus Mundus Joint Doctorate in Distributed Computing (EMJD-DC) program funded by the European Commission (EACEA) (FPA 2012-0030). This project is in part financially supported by the Swedish Foundation for Strategic Research.Publisher
ACMConference/Event name
12th European Conference on Computer Systems, EuroSys 2017Additional Links
http://dl.acm.org/citation.cfm?doid=3064176.3064209http://kth.diva-portal.org/smash/get/diva2:1085916/FULLTEXT01
ae974a485f413a2113503eed53cd6c53
10.1145/3064176.3064209