Solving big data challenges for enterprise application performance management

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.

Energy-aware auto-scaling algorithms for Cassandra virtual data centers

“…Chalkiadaki and Magoutis [6], Dede et al [11], Kuhlenkamp et al [18], Rabl et al [25], Shankaranarayanan et al [28], Shi et al [30] are studies focusing on the horizontal scalability feature offered by such databases. Few studies consider vertical scaling, e.g.…”

“…In [25] the authors evaluate the performance of six SQL and no-SQL databases under the pressure of 5 different workloads. These benchmarking experiments has been extended in [18] with a performance evaluation of Cassandra on different Amazon EC2 infrastructure configurations.…”

“…1). Disk-bound installations have a performance degradation of two order of magnitude compared to CPU bound configurations [25].…”

Capacity Management as a Service for Enterprise Standard Software

“…If widespread and wellestablished standard software is utilized, there is a high likelihood that components of the system whose capacity is being managed, are already in production in various environments. Moreover, those components may already produce performance-related log data as part of APM activities [13], [34], [35], [36]. Thus, measurement data are present allowing for measurement-based techniques to be used for performance assessment in the design or redesign phase of planned and existing enterprise applications.…”

Performance Evaluation of NoSQL Databases

“…Rabl et al [25] benchmark six NoSQL datastores, identifying their ability to support application performance management tools. The authors report response times and throughput for workloads that scale as the number of nodes increases in a configuration.…”