IScIDE 2015: Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques pp 308-315 | Cite as
MAD: A Monitor System for Big Data Applications
Abstract
A big data application usually needs to build a pipeline on the top of workflow engine which connects relevant periodic workflow jobs. It’s crucial to timely alert pipeline issues, provide an issue diagnosis subsystem to find out root cause from a variety of sources, and measure pipeline/service by predefined metrics. In this paper, we identify three indispensable qualities monitor systems must fulfill namely timeliness, accuracy and flexibility. We find that the conventional monitoring tools lack at least one of three qualities, and introduce a general purpose MAD (Monitoring, Alerting and Diagnosis) system for big data applications to keep data freshness, collect measurement metrics to meet SLA.
Keywords
MAD (Monitoring Alerting and Diagnosis) Hadoop Oozie SLAReferences
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