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Low Latency Geo-distributed Data Analytics by Qifan Pu; Ganesh Ananthanarayanan; Peter Bodik; Srikanth Kandula; Aditya Akella; Paramvir Bahl; Ion Stoica is a Computer Science article available to read on EtoBox.

What is Low Latency Geo-distributed Data Analytics about?

Low latency analytics on geographically distributed datasets (across datacenters, edge clusters) is an upcoming and increasingly important challenge. The dominant approach of aggregating all the data to a single datacenter significantly inflates the timeliness of analytics. At the same time, running queries over geo-distributed inputs using the current intra-DC analytics frameworks also leads to high query response times because these frameworks cannot cope with the relatively low and variable capacity of WAN links. We present Iridium, a system for low latency geo-distributed analytics. Iridium achieves low query response times by optimizing placement of both data and tasks of the queries. The joint data and task placement optimization, however, is intractable. Therefore, Iridium uses an online heuristic to redistribute datasets among the sites prior to queries' arrivals, and places the tasks to reduce network bottlenecks during the query's execution. Finally, it also contains a knob to budget WAN usage. Evaluation across eight worldwide EC2 regions using production queries show that Iridium speeds up queries by 3× -- 19× and lowers WAN usage by 15% -- 64% compared to existing base

Who reads Low Latency Geo-distributed Data Analytics?

Author

Qifan Pu; Ganesh Ananthanarayanan; Peter Bodik; Srikanth Kandula; Aditya Akella; Paramvir Bahl; Ion Stoica

Publisher

ACM

Published

2015

Language

EN

Field

Computer Science (Physical Sciences)