Correlating resource demand information with ARM data for application services

Ciavotta

et al. 2014

J Internet Serv Appl

218

Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management.

Section: Workload Inferencementioning

confidence: 99%

Quality-of-service in cloud computing: modeling techniques and their applications

Ardagna

Ciavotta

et al. 2014

J Internet Serv Appl

218

“…In [Rolia and Vetland 1995], linear regression is introduced for resource demand estimation and papers such as [Rolia and Vetland 1998] and [Pacifici et al 2008] propose to use multivariate linear regression to estimate mean service demands. In [Zhang et al 2007], a regression based methodology is presented to estimate the CPU demand in multi-tier software systems.…”

Section: Related Workmentioning

confidence: 99%

A Bayesian Approach to Parameter Inference in Queueing Networks

Wang

ACM Trans. Model. Comput. Simul.

Sutton

2016

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.

“…In particular, methods for regressing CPU utilization and throughput to obtain service demand have gained much attention [20,26,27,37]. Later [4] has proposed an approach for robust demand estimation, based on a Least Trimmed Squares regression technique.…”

Section: Related Workmentioning

confidence: 99%

Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data

Wang

SIGMETRICS Perform. Eval. Rev.

2015

Resource demand estimation is essential for the application of analyical models, such as queueing networks, to realworld systems. In this paper, we investigate maximum likelihood (ML) estimators for service demands in closed queueing networks with load-independent and load-dependent service times. Stemming from a characterization of necessary conditions for ML estimation, we propose new estimators that infer demands from queue-length measurements, which are inexpensive metrics to collect in real systems. One advantage of focusing on queue-length data compared to response times or utilizations is that confidence intervals can be rigorously derived from the equilibrium distribution of the queueing network model. Our estimators and their confidence intervals are validated against simulation and real system measurements for a multi-tier application.