Low Latency Geo-distributed Data Analytics

TLDR

Low‑latency analytics on geographically distributed datasets is increasingly important, yet aggregating data to a single datacenter inflates timeliness and current intra‑DC frameworks suffer high response times due to limited WAN capacity. We present Iridium, a system designed to enable low‑latency geo‑distributed analytics. Iridium achieves this by jointly optimizing data and task placement, using an online heuristic to redistribute datasets before queries, scheduling tasks to avoid network bottlenecks, and providing a knob to limit WAN usage. Evaluation across eight worldwide EC2 regions shows Iridium speeds queries 3×–19× and reduces WAN usage by 15%–64% compared to existing baselines.

Abstract

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 baselines.