MSRG Publication:: Solving Big Data Challenges for Enterprise Application Performance Management

Solving Big Data Challenges for Enterprise Application Performance Management

Tilmann Rabl, Mohammad Sadoghi, Hans-Arno Jacobsen, Sergio Gomez-Villamor, Victor Muntes-Mulero, and Serge Mankovskii.

In Proceedings of the 38th Conference on Very Large Databases (VLDB), 2012.

Share

Abstract

As the complexity of enterprise systems increases, the need for monitoring and analyzing such systems also grows. A number of companies have built sophisticated monitoring tools that go far beyond simple resource utilization reports. For example, based on instrumentation and specialized APIs, it is now possible to monitor single method invocations and trace individual transactions across geographically distributed systems. This high-level of detail enables more precise forms of analysis and prediction but comes at the price of high data rates (i.e., big data). To maximize the benefit of data monitoring, the data has to be stored for an extended period of time for ulterior analysis. This new wave of big data analytics imposes new challenges especially for the application performance monitoring systems. The monitoring data has to be stored in a system that can sustain the high data rates and at the same time enable an up-to-date view of the underlying infrastructure. With the advent of modern key-value stores, a variety of data storage systems have emerged that are built with a focus on scalability and high data rates as predominant in this monitoring use case.
In this work, we present our experience and a comprehensive performance evaluation of six modern (open-source) data stores in the context of application performance monitoring as part of CA Technologies initiative. We evaluated these systems with data and workloads that can be found in application performance monitoring, as well as, on-line advertisement, power monitoring, and many other use cases. We present our insights not only as performance results but also as lessons learned and our experience relating to the setup and configuration complexity of these data stores in an industry setting.

Download




Tags: big data, apm, application performance management, key-value stores, benchmarking


Readers who enjoyed the above work, may also like the following:
  • Discussion of BigBench: A Proposed Industry Standard Performance Benchmark for Big Data.
    Chaitanya Baru, Milind Bhandarkar, Carlo Curino, Manuel Danisch, Michael Frank, Bhaskar Gowda, Hans-Arno Jacobsen, Huang Jie, Dileep Kumar, Raghunath Nambiar, Meikel Poess, Francois Raab, Tilmann Rabl, Nishkam Ravi, Kai Sachs, Saptak Sen, Lan Yi, and Choonhan Youn.
    In Sixth TPC Technology Conference on Performance Evaluation & Benchmarking, pages 44-63, 2014. Springer Berlin Heidelberg.
    Tags: bigbench, big data, benchmarking
  • BigBench Specification V0.1.
    Tilmann Rabl, Ahmad Ghazal, Minqing Hu, Alain Crolotte, Francois Raab, Meikel Poess, and Hans-Arno Jacobsen.
    In Proceedings of the 2012 Workshop on Big Data Benchmarking, pages 164-202, 2013.
    Tags: bigbench, big data, benchmarking
  • Big Data Generation.
    Tilmann Rabl and Hans-Arno Jacobsen.
    In Proceedings of the Workshop on Big Data Benchmarking, pages 20-27, 2013.
    Tags: pdgf, big data, benchmarking