High-level approach to modeling of observed system behavior

Begin, Thomas; Brandwajn, Alexandre; Baynat, Bruno; Wolfinger, Bernd E.; Fdida, Serge

doi:10.1016/j.peva.2009.11.002

Cited by 11 publications

(9 citation statements)

References 21 publications

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“…It shows an example of how we discover a queueing model, f P , whose performance match as closely as possible those known from the centroid points. We observe that a single M/G/1 queue (with a mean rate of service of 5.01 packets/ms, a coefficient of variation equal to 2.02 and an offset equal to 0.08, see [4] for more details) adequately reproduces the behavior exhibited by centroid points. Note that, in this example, P corresponds to the packet delay.…”

Section: B Knowledge Planementioning

confidence: 68%

“…One can consi overhead costs for network nodes in terms of CPU memory consumption, the ease of configuration or the of the decision. Many studies comparing admission solutions ( [10], [4], [12]) aimed to quantify, for a given imental scenario, the attained trade-off between of uti of the link and the packet loss rate or delay.…”

Section: Methodsmentioning

confidence: 99%

“…Second, using Begin et al method [4], we attempt to automatically discover a queueing model that correctly reproduces the behavior exhibited by centroid points. The parameters of the discovered queueing model are automatically determined accordingly.…”

Section: B Knowledge Planementioning

confidence: 99%

“…Many solutions ( [10], [4], [12 imental scenario, the a of the link and the pac In our work, we aim SBAC solution to ach a new flow is accepted if P + α σ p < P T (8) where P T represents the target performance (typically a maximum tolerable delay or loss rate), σ p is the standard deviation of P , as delivered by the discovered queueing model, and α is a conservativeness tuning parameter.…”

Section: IVmentioning

confidence: 99%

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KBAC: Knowledge-Based Admission Control

Ammar

Begin

Guérin-Lassous

et al. 2012

37th Annual IEEE Conference on Local Computer Networks

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Abstract-Many methods have been proposed in the literature to perform admission control in order to provide a sufficient level of Quality of Service (QoS) to accepted flows. In this paper, we introduce a novel data-driven method based on a timevarying model that we refer to as Knowledge-Based Admission Control solution (KBAC). Our KBAC solution consists of three main stages: (i) collect measurements on the on-going traffic over the communication link; (ii) maintain an up-to-date broad view of the link behavior, and feed it to a Knowledge Plane; (iii) model the observed link behavior by a mono-server queue whose parameters are set automatically and which predicts the expected QoS if a flow requesting admission were to be accepted. Our KBAC solution provides a probabilistic guarantee whose admission threshold is either expressed, as a bounded delay or as a bounded loss rate. We run extensive simulations to assess the behavior of our KBAC solution in the case of a delay threshold. The results show that our KBAC solution leads to a good trade-off between flow performance and resource utilization. This ability stems from the quick and automatic adjustment of its admission policy according to the actual variations on the traffic conditions.

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Section: B Knowledge Planementioning

confidence: 68%

Section: Methodsmentioning

confidence: 99%

Section: B Knowledge Planementioning

confidence: 99%

Section: IVmentioning

confidence: 99%

See 2 more Smart Citations

KBAC: Knowledge-Based Admission Control

Ammar

Begin

Guérin-Lassous

et al. 2012

37th Annual IEEE Conference on Local Computer Networks

Self Cite

View full text Add to dashboard Cite

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“…Besides, Sokolsky [10] followed a formal approach to model resource constraints in real-time embedded systems which is easily extensible to include new kinds of resources and resource constraints. Begin et al [11] proposed a high-level approach for modeling an observed system behavior with little knowledge of the system internal structure or operation. This is done by adequately selecting the parameters of a set of queueing systems and queueing networks.…”

Section: Related Workmentioning

confidence: 99%

Validated Model-Based Performance Prediction of Multi-Core Software Routers

Meyer

Wohlfart

Raumer

et al. 2014

PIK - Praxis Der Informationsverarbeitung Und Kommunikation

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Abstract-The possibility of using flexible and cost-efficient commodity hardware instead of expensive custom hardware has triggered wide interest in software routers. Performance measurement and simulation are important approaches for identifying bottlenecks of such systems to predict and improve the performance. We measure the performance of software routers using current multi-core hardware architectures. We introduce an innovative and validated node model for intra-node resource contention and realized a resource management extension for the widely-used network simulator ns-3 which allows to evaluate and predict the performance of current and future software router architectures.

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Performance models of storage contention in cloud environments

et al. 2012

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We propose simple models to predict the performance degradation of disk requests due to storage device contention in consolidated virtualized environments. Model parameters can be deduced from measurements obtained inside Virtual Machines (VMs) from a system where a single VM accesses a remote storage server. The parameterized model can then be used to predict the effect of storage contention when multiple VMs are consolidated on the same server. We first propose a trace-driven approach that evaluates a queueing network with fair share scheduling using simulation. The model parameters consider Virtual Machine Monitor level disk access optimizations and rely on a calibration technique. We further present a measurement-based approach that allows a distinct characterization of read/write performance attributes. In particular, we define simple linear prediction models for I/O request mean response times, throughputs and read/write mixes, as well as a simulation model for predicting response time distributions. We found

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High-level approach to modeling of observed system behavior

Cited by 11 publications

References 21 publications

KBAC: Knowledge-Based Admission Control

KBAC: Knowledge-Based Admission Control

Validated Model-Based Performance Prediction of Multi-Core Software Routers

Performance models of storage contention in cloud environments

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