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ISTC-CC NEWSLETTER
RESEARCH HIGHLIGHTS
Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
ISTC-CC provides a listing of useful benchmarks for cloud computing.
Another list highlighting Open Source Software Releases.
Second GraphLab workshop should be even bigger than the first! GraphLab is a new programming framework for graph-style data analytics.
ISTC-CC Abstract
Draco: Statistical Diagnosis of Chronic Problems in Large Distributed Systems
IEEE/IFIP Conference on Dependable Systems and Networks (DSN'12), June 2012.
Soila Pertet Kavulya, Kaustubh Joshi*, Matti Hiltunen*, Scott Daniels*,
Rajeev Gandhi, Priya Narasimhan
Carnegie Mellon University
*AT&T Research Labs
Chronics are recurrent problems that often fly under the radar of operations teams because they do not affect enough users or service invocations to set off alarm thresholds. In contrast with major outages that are rare, often have a single cause, and as a result are relatively easy to detect and diagnose quickly, chronic problems are elusive because they are often triggered by complex conditions, persist in a system for days or weeks, and coexist with other problems active at the same time. In this paper, we present Draco, a scalable engine to diagnose chronics that addresses these issues by using a "topdown" approach that starts by heuristically identifying user interactions that are likely to have failed, e:g:, dropped calls, and drills down to identify groups of properties that best explain the difference between failed and successful interactions by using a scalable Bayesian learner. We have deployed Draco in production for the VoIP operations of a major ISP. In addition to providing examples of chronics that Draco has helped identify, we show via a comprehensive evaluation on production data that Draco provided 97% coverage, had fewer than 4% false positives, and outperformed state-of-the-art diagnostic techniques by up to 56% for complex chronics.
FULL PAPER: pdf
