An Offline Demand Estimation Method for Multi-threaded Applications

Pérez, Juan F.; Pacheco-Sanchez, Sergio; Casale, Giuliano

doi:10.1109/mascots.2013.10

Cited by 18 publications

(29 citation statements)

References 16 publications

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“…A different approach has been used for the estimation of the time that each class spends inside the CPU (demand). The demand has been calculated using the Complete Information algorithm [26]. To calculate demands, the algorithm requires request timestamps and response times.…”

Section: Model Parametrizationmentioning

confidence: 99%

“…All the CPU demands are calculated with a single job in the system and the collected latencies are processed with the Complete Information algorithm [26] to obtain the final demand for each class. The model is parameterised with the calculated demands for all the classes and, according to the real implementation, the same number of CPU cores are set for each node.…”

Section: Case Study: Applicability Of Our Model To Other Nosql Databasesmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Model Parametrizationmentioning

confidence: 99%

Section: Case Study: Applicability Of Our Model To Other Nosql Databasesmentioning

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

Self Cite

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show abstract

“…The true value θ ij of the service demand is estimated using the Complete Information (CI) algorithm proposed in [Perez et al 2013], which is able to return the near exact demand from the dataset given the full sample path of the system from the application logs. In order to avoid assuming knowledge on the population of the model, which may be unrealistic in some applications, the model population K is estimated from the empirical dataset as the maximum number of concurrently executing requests in the system across all the recordings.…”

Section: Case Studymentioning

confidence: 99%

A Bayesian Approach to Parameter Inference in Queueing Networks

Wang

Casale

Sutton

2016

ACM Trans. Model. Comput. Simul.

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The application of queueing network models to real-world applications often involves the task of estimating the service demand placed by requests at queueing nodes. In this paper, we propose a methodology to estimate service demands in closed multi-class queueing networks based on Gibbs sampling. Our methodology requires measurements of the number of jobs at resources and can accept prior probabilities on the demands.Gibbs sampling is challenging to apply to estimation problems for queueing networks since it requires to efficiently evaluate a likelihood function on the measured data. This likelihood function depends on the equilibrium solution of the network, which is difficult to compute in closed models due to the presence of the normalizing constant of the equilibrium state probabilities. To tackle this obstacle, we define a novel iterative approximation of the normalizing constant and show the improved accuracy of this approach, compared to existing methods, for use in conjunction with Gibbs sampling. We also demonstrate that, as a demand estimation tool, Gibbs sampling outperforms other popular Markov Chain Monte Carlo approximations. Experimental validation based on traces from a cloud application demonstrates the effectiveness of Gibbs sampling for service demand estimation in real-world studies.

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“…The FG component thus supports the following three demand estimation methods: the utilization-based optimization (UBO) method from [15], the utilization-based regression (UBR) method from [12], and the Extended RPS method from [16]. A short description of these methods is provided in Sect.…”

Section: Fg Analyzermentioning

confidence: 99%