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Auflistung nach Schlagwort "Performance Modeling"

1 - 2 von 2
  • Konferenzbeitrag
    Distance-Based Sampling of Software Configuration Spaces
    (Software Engineering 2020, 2020) Kaltenecker, Christian; Grebhahn, Alexander; Siedmund, Norbert; Guo, Jianmei; Apel, Sven
    Configurable software systems provide configuration options to adjust and optimize their functional and non-functional properties. However, to obtain accurate performance predictions, a representative sample set of configurations is required. Different sampling strategies have been proposed, which come with different advantages and disadvantages. In our experiments, we found that most sampling strategies do not achieve a good coverage of the configuration space with respect to covering relevant performance values. That is, they miss important configurations with distinct performance behavior. Based on this observation, we devise a new sampling strategy that is based on a distance metric and a probability distribution to spread the configurations of the sample set across the configuration space. To demonstrate the merits of distance-based sampling, we compare it to state-of-the-art sampling strategies on 10 real-world configurable software systems. Our results show that distance-based sampling leads to more accurate performance models for medium to large sample sets.
  • Konferenzbeitrag
    Tuning Cassandra through Machine Learning
    (BTW 2023, 2023) Eppinger, Florian; Störl, Uta
    NoSQL databases have become an important component of many big data and real-time web applications. Their distributed nature and scalability make them an ideal data storage repository for a variety of use cases. While NoSQL databases are delivered with a default ”off-the-shelf” configuration, they offer configuration settings to adjust a database’s behavior and performance to a specific use case and environment. The abundance and oftentimes imperceptible inter-dependencies of configuration settings make it difficult to optimize and performance-tune a NoSQL system. There is no one-size-fits-all configuration and therefore the workload, the physical design, and available resources need to be taken into account when optimizing the configuration of a NoSQL database. This work explores Machine Learning as a means to automatically tune a NoSQL database for optimal performance. Using Random Forest and Gradient Boosting Decision Tree Machine Learning algorithms, multiple Machine Learning models were fitted with a training dataset that incorporates properties of the NoSQL physical configuration (replication and sharding). The best models were then employed as surrogate models to optimize the Database Management System’s configuration settings for throughput and latency using various Black-box Optimization algorithms. Using an Apache Cassandra database, multiple experiments were carried out to demonstrate the feasibility of this approach, even across varying physical configurations. The tuned Database Management System (DBMS) configurations yielded throughput improvements of up to 4%, read latency reductions of up to 43%, and write latency reductions of up to 39% when compared to the default configuration settings.