Munin: Graphisches Netzwerk- und System-Monitoring

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Ob ein Dienst gerade läuft oder nicht, lässt sich leicht mit Nagios überwachen. Belastungsspitzen, Schwachstellen im Netzwerkdurchsatz oder Speicherlecks auf Servern erkennt man hingegen besser per Langzeitmonitoring. Entsprechende, per Webserver einsehbare Graphen erstellt Munin auf der Basis von Tobias Oetikers RRD-Tool. Dank einer Flexibilität empfiehlt sich Munin als würdiger Nachfolger des legendären MRTG.

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... There are many tools for cluster-wide monitoring and visualization such as Ganglia [37], Collectd [13], and Munin [39]. These tools are designed for large scale data gathering, transport, and visualization. ...
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Inspired by Google's BigTable, a variety of scalable, semi-structured, weak-semantic table stores have been developed and optimized for different priorities such as query speed, ingest speed, availability, and interactivity. As these systems mature, performance benchmarking will advance from measuring the rate of simple workloads to understanding and debugging the performance of advanced features such as ingest speed-up techniques and function shipping filters from client to servers. This paper describes YCSB++, a set of extensions to the Yahoo! Cloud Serving Benchmark (YCSB) to improve performance understanding and debugging of these advanced features. YCSB++ includes multi-tester coordination for increased load and eventual consistency measurement, multi-phase workloads to quantify the consequences of work deferment and the benefits of anticipatory configuration optimization such as B-tree pre-splitting or bulk loading, and abstract APIs for explicit incorporation of advanced features in benchmark tests. To enhance performance debugging, we customized an existing cluster monitoring tool to gather the internal statistics of YCSB++, table stores, system services like HDFS, and operating systems, and to offer easy post-test correlation and reporting of performance behaviors. YCSB++ features are illustrated in case studies of two BigTable-like table stores, Apache HBase and Accumulo, developed to emphasize high ingest rates and finegrained security.
... Some approaches focus primarily on building scalable monitoring infrastructures, while others aim at pinpointing faults and diagnosing performance problems. Ganglia [12], Collectd [5] and Munin [24], focus on collecting host-wide performance metrics, that is, the metrics are summed for all running processes on a node. RRD- Tool [13] has been used in these systems to bound storage space and generate time-series graphs for their user interfaces . ...
Frameworks for large scale data-intensive applications, such as Hadoop and Dryad, have gained tremendous popularity. Understanding the resource requirements of these frame-works and the performance characteristics of distributed ap-plications is inherently difficult. We present an approach, based on resource attribution, that aims at facilitating per-formance analyses of distributed data-intensive applications. This approach is embodied in Otus, a monitoring tool to attribute resource usage to jobs and services in Hadoop clusters. Otus collects and correlates performance metrics from distributed components and provides views that dis-play time-series of these metrics filtered and aggregated us-ing multiple criteria. Our evaluation shows that this ap-proach can be deployed without incurring major overheads. Our experience with Otus in a production cluster suggests its effectiveness at helping users and cluster administrators with application performance analysis and troubleshooting.
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