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The Nearest Replica Can Be Farther Than You Think
KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikationssystem, CoS, Network Systems Laboratory (NS Lab).ORCID-id: 0000-0001-7642-6591
Complutense University of Madrid.
KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikationssystem, CoS, Radio Systems Laboratory (RS Lab).ORCID-id: 0000-0002-6066-746X
KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikationssystem, CoS, Network Systems Laboratory (NS Lab).ORCID-id: 0000-0002-1256-1070
2015 (engelsk)Inngår i: Proceedings of the ACM Symposium on Cloud Computing 2015, Association for Computing Machinery (ACM), 2015, s. 16-29Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Modern distributed systems are geo-distributed for reasons of increased performance, reliability, and survivability. At the heart of many such systems, e.g., the widely used Cassandra and MongoDB data stores, is an algorithm for choosing a closest set of replicas to service a client request. Suboptimal replica choices due to dynamically changing network conditions result in reduced performance as a result of increased response latency. We present GeoPerf, a tool that tries to automate the process of systematically testing the performance of replica selection algorithms for geodistributed storage systems. Our key idea is to combine symbolic execution and lightweight modeling to generate a set of inputs that can expose weaknesses in replica selection. As part of our evaluation, we analyzed network round trip times between geographically distributed Amazon EC2 regions, and showed a significant number of daily changes in nearestK replica orders. We tested Cassandra and MongoDB using our tool, and found bugs in each of these systems. Finally, we use our collected Amazon EC2 latency traces to quantify the time lost due to these bugs. For example due to the bug in Cassandra, the median wasted time for 10% of all requests is above 50 ms.

sted, utgiver, år, opplag, sider
Association for Computing Machinery (ACM), 2015. s. 16-29
Emneord [en]
Geo-Distributed Systems, Replica Selection Algorithms, Symbolic Execution
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-171434DOI: 10.1145/2806777.2806939ISI: 000380606400002Scopus ID: 2-s2.0-84958960133OAI: oai:DiVA.org:kth-171434DiVA, id: diva2:844010
Konferanse
ACM Symposium on Cloud Computing August 27 - 29, 2015,Hawaii
Forskningsfinansiär
EU, European Research Council, 259110
Merknad

To obtain the data used in this work please contact dmk@kth.se and kirillb@kth.se.

QC 20150812

Tilgjengelig fra: 2015-08-03 Laget: 2015-08-03 Sist oppdatert: 2018-10-07bibliografisk kontrollert

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