Database system performance evaluation models: A survey

Osman, Rasha; Knottenbelt, William J.

doi:10.1016/j.peva.2012.05.006

Cited by 36 publications

(31 citation statements)

References 56 publications

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“…Thus arrivals are assumed to follow a Poisson process, or else a closed model is used, and objects within the database are often assumed to be uniformly accessed [525]. However, real workloads are more complex.…”

Section: Database Systemsmentioning

confidence: 99%

Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

197

131

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Reliable performance evaluations require the use of representative workloads. This is no easy task since modern computer systems and their workloads are complex, with many interrelated attributes and complicated structures. Experts often use sophisticated mathematics to analyze and describe workload models, making these models difficult for practitioners to grasp. This book aims to close this gap by emphasizing the intuition and the reasoning behind the definitions and derivations related to the workload models. It provides numerous examples from real production systems, with hundreds of graphs. Using this book, readers will be able to analyze collected workload data and clean it if necessary, derive statistical models that include skewed marginal distributions and correlations, and consider the need for generative models and feedback from the system. The descriptive statistics techniques covered are also useful for other domains.

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Section: Database Systemsmentioning

confidence: 99%

Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

197

131

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“…Database performance modelling has been traditionally focused on relational databases [24]. With the increasing growth in popularity of NoSQL databases over the last ten years, research work in performance evaluation has been increasingly conducted also on these emerging database systems.…”

Section: Related Workmentioning

confidence: 99%

A Queueing Network Model for Performance Prediction of Apache Cassandra

Dipietro¹,

Casale²,

Serazzi³

2017

Proceedings of the 10th EAI International Conference on Performance Evaluation Methodologies and Tools

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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 parameters, 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.

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“…[2] When it comes to the performance evaluation problem, however, it seems impossible to obtain a one-size-fits-all methodology for evaluating performance of all kinds of software systems. Although there exist standard principles for guiding particular activities involved in performance evaluation (e.g., using randomization to eliminate measurement bias), different system domains might come with domainspecific evaluation concerns and experiments, because the features and characteristics of different software systems could vary significantly (e.g., performance evaluation of componentbased software systems [12] vs. performance evaluation of database-oriented software systems [20]). …”

Section: Introductionmentioning

confidence: 99%

“…Firstly, it borrows common lessons from the existing performance evaluation work in computer science (e.g., [6,10,11,12,20]). Secondly, it refers to the guidelines from Design of Experiments (DOE) [18].…”

Section: Introductionmentioning

confidence: 99%

DoKnowMe

Zheng

O’Brien

Kihl

2016

SIGMETRICS Perform. Eval. Rev.

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Software engineering considers performance evaluation to be one of the key portions of software quality assurance. Unfortunately, there seems to be a lack of standard methodologies for performance evaluation even in the scope of experimental computer science. Inspired by the concept of "instantiation" in object-oriented programming, we distinguish the generic performance evaluation logic from the distributed and ad-hoc relevant studies, and develop an abstract evaluation methodology (by analogy of "class") we name Domain Knowledge-driven Methodology (DoKnowMe). By replacing five predefined domain-specific knowledge artefacts, DoKnowMe could be instantiated into specific methodologies (by analogy of "object") to guide evaluators in performance evaluation of different software and even computing systems. We also propose a generic validation framework with four indicators (i.e. usefulness, feasibility, effectiveness and repeatability), and use it to validate DoKnowMe in the Cloud services evaluation domain. Given the positive and promising validation result, we plan to integrate more common evaluation strategies to improve DoKnowMe and further focus on the performance evaluation of Cloud autoscaler systems.

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Database system performance evaluation models: A survey

Cited by 36 publications

References 56 publications

Workload Modeling for Computer Systems Performance Evaluation

Workload Modeling for Computer Systems Performance Evaluation

A Queueing Network Model for Performance Prediction of Apache Cassandra

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