End-to-end quality of service seen by applications: A statistical learning approach

Belzarena, Pablo; Aspirot, Laura

doi:10.1016/j.comnet.2010.06.004

Cited by 5 publications

(2 citation statements)

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“…The advantage of this approach is that it guarantees optimal performance, but its main downside is that it results in a costly O(n 2 ) probing overhead as the number of participating nodes n increases, since the topology of a routing overlay is that of a complete graph. Moreover, considering that measuring accurately the delay suffered by an application requires to inject a train of probe packets with a rate similar to that of the application [5] the cost will depend on the application and could be high at the overlay scale. Even more so, if other performance metrics were to be considered, such as bandwidth, the cost can rapidly become prohibitive.…”

Section: Contextmentioning

confidence: 99%

Joint Minimization of Monitoring Cost and Delay in Overlay Networks: Optimal Policies with a Markovian Approach

et al. 2018

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Continuous monitoring of network resources enables to make more-informed resource allocation decisions but incurs overheads. We investigate the trade-off between monitoring costs and benefits of accurate state information for a routing problem. In our approach link delays are modeled by Markov chains or hidden Markov models. The current delay information on a link can be obtained by actively monitoring this link at a fixed cost. At each time slot, the decision maker chooses to monitor a subset of links with the objective of minimizing a linear combination of long-run average delay and monitoring costs. This decision problem is modeled as a Markov Decision Process whose solution is computed numerically. In addition, in simple setttings we prove that immediate monitoring cost and delay minimization leads to a threshold policy on a filter which sums up information from past measurements. The lightweight method as well as the optimal policy are tested on several use-cases. We demonstrate on an overlay of 30 nodes of RIPE Atlas that we obtain delay values close to the performance of the always best path with an extremely low monitoring effort when delays between nodes are modeled with hierarchical Dirichlet process hidden Markov models.

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Section: Contextmentioning

confidence: 99%

Joint Minimization of Monitoring Cost and Delay in Overlay Networks: Optimal Policies with a Markovian Approach

et al. 2018

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“…In these scenarios, the resources are owned by individuals and they are shared in return for access to other resources. This could hypothetically transform the cloud's economics and raises doubts about the reliability and QoS warranties [11] [13].…”

Section: Introductionmentioning

confidence: 99%

Input Projection Algorithms Influence in Prediction and Optimization of QoS Accuracy

Albu

Dzițac

Popenţiu-Vlădicescu

et al. 2014

INT J COMPUT COMMUN

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Regardless of new achievements in the research of prediction models, QoS is still a great issue for high quality web services and remains one of the key subjects that need to be studied. We believe that QoS should not only be measured, but have to be predicted in development and implementation phases. In this paper we assess how different input projection algorithms influence the prediction accuracy of a Multi-Layer Perceptron (MLP) trained with large datasets of web services QoS values.

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A Unified View to Machine Learning and Control for Measurement-based Equivalent Bandwidth

Mongelli

Muselli

Marchese

2020

2020 16th International Conference on the Design of Reliable Communication Networks DRCN 2020

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End-to-end quality of service seen by applications: A statistical learning approach

Cited by 5 publications

References 40 publications

Joint Minimization of Monitoring Cost and Delay in Overlay Networks: Optimal Policies with a Markovian Approach

Joint Minimization of Monitoring Cost and Delay in Overlay Networks: Optimal Policies with a Markovian Approach

Input Projection Algorithms Influence in Prediction and Optimization of QoS Accuracy

A Unified View to Machine Learning and Control for Measurement-based Equivalent Bandwidth

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