A Queueing Network Model for Performance Prediction of Apache Cassandra

NoSQL databases such as Apache Cassandra have attracted large interest in recent years thanks to their high availability, scalability, flexibility and low latency. Still there is limited research work on performance engineering methods for NoSQL databases, which yet are needed since these systems are highly distributed and thus can incur significant cost/performance trade-offs. To address this need, we propose a novel queueing network model for the Cassandra NoSQL database aimed at supporting resource provisioning. The model defines explicitly key configuration parameters of Cassandra such as consistency levels and replication factor, allowing engineers to compare alternative system setups. Experimental results based on the YCSB benchmark indicate that, with a small amount of training for the estimation of its input param- eters, the proposed model achieves good predictive accuracy across different loads and consistency levels. The average performance errors of the model compared to the real results are between 6% and 10%. We also demonstrate the applicability of our model to other NoSQL databases and other possible utilisation of it

A Unifying Framework for the Approximate Solution of Closed Multiclass Queuing Networks

Queuing network models of modern computing systems must consider a large number of components (e.g., Web servers, DB servers, application servers, firewall, routers, networks) and hundreds of customers with very different resource requirements. The complexity of such models makes the application of exact solution techniques prohibitively expensive, motivating research on approximate methods. This paper proposes an interpolation-matching framework that allows a unified view of approximate solution techniques for closed product-form queuing networks. Depending upon the interpolating functional form and the matching populations selected, a large versatile family of new approximations can be generated. It is shown that all the known approximation strategies, including Linearizer, are instances of the interpolation-matching framework. Furthermore, a new approximation technique, based on a third-order polynomial, is obtained using the interpolation-matching framework. The new technique is shown to be more accurate than other known methods

Quantitative System Evaluation with Java Modeling Tool

Java Modelling Tools (JMT) is a suite of open source applications for performance evaluation and workload characterization of computer and communication systems based on queueing networks. JMT includes tools for workload characterization (JWAT), solution of queueing networks with analytical algorithms (JMVA), simulation of general-purpose queueing models (JSIM), bottleneck identification (JABA), and teaching support for Markov chain models underlying queueing systems (JMCH). This tutorial summarizes the main features of the tools that compose the suite. Furthermore, using a composite case study, we provide intuition on the versatility of JMT in dealing with the different aspects of quality-of-service (QoS) evaluation, what-if analysis, and software performance tuning