Performance Model Estimation and Tracking Using Optimal Filters

Zheng, Tao; Woodside, C. Murray; Litoiu, Marin

doi:10.1109/tse.2008.30

Cited by 108 publications

(92 citation statements)

References 22 publications

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“…The problem is formulated as a multivariate linear regression problem and accounts for multiple effects such as data aging. Also, several works have shown how combining the queueing theoretic formulas used by regression methods with the Kalman filter can enable continuous demand tracking [41,42].…”

Section: Workload Inferencementioning

confidence: 99%

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

Ardagna

Casale

Ciavotta

et al. 2014

J Internet Serv Appl

211

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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.

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Section: Workload Inferencementioning

confidence: 99%

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

Ardagna

Casale

Ciavotta

et al. 2014

J Internet Serv Appl

211

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show abstract

“…To address this problem, our approach is to take coarse-grained measurements and apply statistical inference to estimate mean resource demands. Most of existing mean demand estimation approaches rely on the regression against utilization data [3][4][5][6][7][8][9][10][11][12][13], however, utilization measurements are not always available, for instance in Platform-as-a-Service (PaaS) deployments where the resource layer is hidden to the application and thus protected from external monitoring.…”

Section: Fg Analyzermentioning

confidence: 99%

Closing the Loop Between Ops and Dev

Wang

Casale

Iuhasz

2016

Model-Driven Development and Operation of Multi-Cloud Applications

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“…Approaches to resource demand estimation can be found in [18], [19], [27]. While in [18] applies the Service Demand Law directly for single workload classes, linear regression approaches to partition resource demand among multiple workload classes can be found in [19], [28].…”

Section: Related Workmentioning

confidence: 99%

“…While in [18] applies the Service Demand Law directly for single workload classes, linear regression approaches to partition resource demand among multiple workload classes can be found in [19], [28]. In [27], utilization and throughput data is used to build a Kalman filter estimator.…”

Section: Related Workmentioning

confidence: 99%

Automated extraction of architecture-level performance models of distributed component-based systems

Brosig

Huber

Kounev

2011

2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011)

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Abstract-Modern enterprise applications have to satisfy increasingly stringent Quality-of-Service requirements. To ensure that a system meets its performance requirements, the ability to predict its performance under different configurations and workloads is essential. Architecture-level performance models describe performance-relevant aspects of software architectures and execution environments allowing to evaluate different usage profiles as well as system deployment and configuration options. However, building performance models manually requires a lot of time and effort. In this paper, we present a novel automated method for the extraction of architecture-level performance models of distributed component-based systems, based on monitoring data collected at run-time. The method is validated in a case study with the industry-standard SPECjEnterprise2010 Enterprise Java benchmark, a representative software system executed in a realistic environment. The obtained performance predictions match the measurements on the real system within an error margin of mostly 10-20 percent.

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Performance Model Estimation and Tracking Using Optimal Filters

Cited by 108 publications

References 22 publications

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

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

Closing the Loop Between Ops and Dev

Automated extraction of architecture-level performance models of distributed component-based systems

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