| 1. |
- Ahmed, J., et al.
(författare)
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Automated diagnostic of virtualized service performance degradation
- 2018
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Ingår i: Proceedings 2018 IEEE/IFIP Network Operations and Management Symposium, NOMS 2018. - New York : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1-9
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Konferensbidrag (refereegranskat)abstract
- Service assurance for cloud applications is a challenging task and is an active area of research for academia and industry. One promising approach is to utilize machine learning for service quality prediction and fault detection so that suitable mitigation actions can be executed. In our previous work, we have shown how to predict service-level metrics in real-time just from operational data gathered at the server side. This gives the service provider early indications on whether the platform can support the current load demand. This paper provides the logical next step where we extend our work by proposing an automated detection and diagnostic capability for the performance faults manifesting themselves in cloud and datacenter environments. This is a crucial task to maintain the smooth operation of running services and minimizing downtime. We demonstrate the effectiveness of our approach which exploits the interpretative capabilities of Self- Organizing Maps (SOMs) to automatically detect and localize different performance faults for cloud services.
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| 2. |
- Ahmed, J., et al.
(författare)
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Predicting SLA conformance for cluster-based services using distributed analytics
- 2016
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Ingår i: Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium. - : IEEE conference proceedings. - 9781509002238 ; , s. 848-852
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Konferensbidrag (refereegranskat)abstract
- Service assurance for the telecom cloud is a challenging task and is continuously being addressed by academics and industry. One promising approach is to utilize machine learning to predict service quality in order to take early mitigation actions. In previous work we have shown how to predict service-level metrics, such as frame rate for a video application on the client side, from operational data gathered at the server side. This gives the service provider early indications on whether the platform can support the current load demand. This paper extends previous work by addressing scalability issues for cluster-based services. Operational data being generated in large volumes, from several sources, and at high velocity puts strain on computational and communication resources. We propose and evaluate a distributed machine learning system based on the Winnow algorithm to tackle scalability issues, and then compare the new distributed solution with the previously proposed centralized solution. We show that network overhead and computational execution time is substantially reduced while maintaining high prediction accuracy making it possible to achieve real-time service quality predictions in large systems.
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| 3. |
- Flinta, C., et al.
(författare)
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Real-time resource prediction engine for cloud management
- 2017
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Ingår i: Proceedings of the IM 2017 - 2017 IFIP/IEEE International Symposium on Integrated Network and Service Management. - : Institute of Electrical and Electronics Engineers Inc.. - 9783901882890 ; , s. 877-878
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Konferensbidrag (refereegranskat)abstract
- Predicting resource requirements for cloud services is critical for dimensioning, anomaly detection and service assurance. We demonstrate a system for real-time estimation of the needed amount of infrastructure resources, such as CPU and memory, for a given service. Statistical learning methods on server statistics and load parameters of the service are used for learning a resource prediction model. The model can be used as a guideline for service deployment and for real-time identification of resource bottlenecks.
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| 4. |
- Hoque, R., et al.
(författare)
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A self-organizing scalable network tomography control protocol for active measurement methods
- 2010
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Ingår i: Proc. Int. Symp. Perform. Eval. Comput. Telecommun. Syst., SPECTS. - 9781565553415 ; , s. 65-72
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Konferensbidrag (refereegranskat)abstract
- Network tomography enables operators to monitor and take action based on path measurements between nodes, often located at the network edge. State-of-the-art network tomography solutions use tools to measure performance parameters such as jitter, loss and round-trip time between multiple nodes. This paper describes and analyses a self-organizing, distributed and scalable control protocol for methods conducting continuous active measurements. Performing path available capacity measurements pose some interesting scalability challenges solved by the control protocol. The control protocol also aims at providing full-mesh coverage of end-node measurement pairs in order to give the operator a "performance map". By setting a few parameters at startup the system autonomously control the network and node overhead. Further, it is shown that the control protocol is scalable with respect to the number of nodes that is involved in the measurements.
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| 5. |
- Samani, Forough Shahab, et al.
(författare)
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Demonstration : Predicting distributions of service metrics
- 2019
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Ingår i: 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019. - : Institute of Electrical and Electronics Engineers Inc.. - 9783903176157 ; , s. 745-746
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Konferensbidrag (refereegranskat)abstract
- The ability to predict conditional distributions of service metrics is key to understanding end-to-end service behavior. From conditional distributions, other metrics can be derived, such as expected values and quantiles, which are essential for assessing SLA conformance. Our demonstrator predicts conditional distributions and derived metrics estimation in realtime, using infrastructure measurements. The distributions are modeled as Gaussian mixtures whose parameters are estimated using a mixture density network. The predictions are produced for a Video-on-Demand service that runs on a testbed at KTH.
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| 6. |
- Yanggratoke, Rerngvit, et al.
(författare)
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A platform for predicting real-time service-level metrics from device statistics
- 2015
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Ingår i: Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management, IM 2015. - : IEEE conference proceedings. - 9783901882760 ; , s. 1141-1142
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Konferensbidrag (refereegranskat)abstract
- Predicting performance metrics for cloud services is critical for real-time service assurance. We demonstrate a platform for estimating real-time service-level metrics. Statistical learning methods on device statistics are used to predict metrics for services running on these devices.
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