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Poster: MADES - a multi-layered, adaptive, distributed event store

Published: 29 June 2013 Publication History

Abstract

Application performance monitoring (APM) is shifting towards capturing and analyzing every event that arises in an enterprise infrastructure. Current APM systems, for example, make it possible to monitor enterprise applications at the granularity of tracing each method invocation (i.e., an event). Naturally, there is great interest in monitoring these events in real-time to react to system and application failures and in storing the captured information for an extended period of time to enable detailed system analysis, data analytics, and future auditing of trends in the historic data. However, the high insertion-rates (up to millions of events per second) and the purposely limited resource, a small fraction of all enterprise resources (i.e., 1-2% of the overall system resources), dedicated to APM are the key challenges for applying current data management solutions in this context. Emerging distributed key-value stores, often positioned to operate at this scale, induce additional storage overhead when dealing with relatively small data points (e.g., method invocation events) inserted at a rate of millions per second. Thus, they are not a promising solution for such an important class of workloads given APM's highly constrained resource budget. In this paper, to address these shortcomings, we present Multi-layered, Adaptive, Distributed Event Store (MADES): a massively distributed store for collecting, querying, and storing event data at a rate of millions of events per second.

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C. Gaspar. Deploying nagios in a large enterprise environment. In LISA'07.
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H.-A. Jacobsen, V. Muthusamy, and G. Li. The PADRES event processing network: Uniform querying of past and future events. it - Information Technology'09.
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A. Lakshman and P. Malik. Cassandra: a decentralized structured storage system. SIGOPS Review'10.
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M. L. Massie, B. N. Chun, and D. E. Culler. The Ganglia Distributed Monitoring System: Design, Implementation, and Experience. Parallel Computing'04.
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T. Rabl, M. Sadoghi, H.-A. Jacobsen, S. Gómez-Villamor, V. Muntés-Mulero, and S. Mankowskii. Solving Big Data Challenges for Enterprise Application Performance Management. PVLDB, 5(12):1724--1735, 2012.

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  • (2012)Processing Big Events with Showers and StreamsRevised Selected Papers of the First Workshop on Specifying Big Data Benchmarks - Volume 816310.1007/978-3-642-53974-9_6(60-71)Online publication date: 17-Dec-2012

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  1. Poster: MADES - a multi-layered, adaptive, distributed event store

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    cover image ACM Conferences
    DEBS '13: Proceedings of the 7th ACM international conference on Distributed event-based systems
    June 2013
    360 pages
    ISBN:9781450317580
    DOI:10.1145/2488222
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Publication History

    Published: 29 June 2013

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    Author Tags

    1. apm
    2. event processing
    3. event storage
    4. mades

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    DEBS '13 Paper Acceptance Rate 16 of 58 submissions, 28%;
    Overall Acceptance Rate 145 of 583 submissions, 25%

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    • (2012)Processing Big Events with Showers and StreamsRevised Selected Papers of the First Workshop on Specifying Big Data Benchmarks - Volume 816310.1007/978-3-642-53974-9_6(60-71)Online publication date: 17-Dec-2012

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