| 1. |
- Rao, Akhila, et al.
(författare)
-
Prediction and exposure of delays from a base station perspective in 5G and beyond networks
- 2022
-
Ingår i: 5G-MeMU 2022 - Proceedings of the ACM SIGCOMM 2022 Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases - Part of SIGCOMM 2022. - New York, NY, USA : Association for Computing Machinery, Inc. - 9781450393935 ; , s. 8-14
-
Konferensbidrag (refereegranskat)abstract
- The inherent flexibility of 5G networks come with a high degree of configuration and management complexity. This makes the performance outcome for UEs, more than ever, dependent on intricate configurations and interplay between algorithms at various network components. In this paper, we take initial steps towards a performance exposure system at the base station using a data-driven approach for predicting performance violations in terms of RTT, as observed by the UE, in a 5G mmWave network. We present ML models to predict RTT using low-level and high-frequency base station metrics from a 5G mmWave testbed based on commercially available equipment. Predicting UE performance from a base station perspective, and exposing this knowledge, is valuable for applications to proactively address performance violations. We also compare several methods for feature reduction, which have a significant impact on monitoring load. We demonstrate our model's ability to identify RTT violations, paving the way for network providers towards an intelligent performance exposure system.
|
|
| 2. |
- Ahmed, J., et al.
(författare)
-
Automated diagnostic of virtualized service performance degradation
- 2018
-
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
-
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.
|
|
| 3. |
- Flinta, C., et al.
(författare)
-
Real-time resource prediction engine for cloud management
- 2017
-
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
-
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.
|
|
| 4. |
- Johnsson, Andreas, et al.
(författare)
-
Developing environmentally friendly rolling lubricants
- 2011
-
Ingår i: Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology. - : SAGE Publications. - 2041-305X. ; , s. 932-939
-
Konferensbidrag (refereegranskat)abstract
- Swerea MEFOS pilot mill has been used as a rolling lubricant development tool. The objectives were to improve the lubrication during rolling and thereby improve the operation of the rolling mills. This was done taking into account both the technological aspect, rolling mill output, and the ecological aspect, investigating ways to use rolling lubricants that are more ecologic sustainable, i.e. alternatives to oil-based lubricants as well as the recycling/recovering of oilbased lubricants. Oil-in-water emulsions and an aqueous solution, by the producer termed a conditional emulsion, were tested where the lubricant formulation was altered to optimize the performance for hot rolling of aluminium. Cold-rolling tests were also run on steel coils, where the presence of rust protective oil was studied as well as the influence of aged emulsion behaviour compared with fresh emulsion. Finally, Sapa developed a vacuum evaporator system for recovering oil from dumped emulsion generated during aluminium hot rolling, followed by separation, centrifugation, and flocculation. The cleaned concentrates were reconditioned for reuse. The recycled and recovered emulsion was tested and compared with the original fresh emulsion in Swerea MEFOS pilot mill with results as good as the fresh emulsion. © Swerea MEFOS AB, Luleå, Sweden, 2011.
|
|
| 5. |
|
|
| 6. |
- Moradi, F., et al.
(författare)
-
Performance prediction in dynamic clouds using transfer learning
- 2019
-
Ingår i: 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019. - : Institute of Electrical and Electronics Engineers Inc.. - 9783903176157 ; , s. 242-250
-
Konferensbidrag (refereegranskat)abstract
- Learning a performance model for a cloud service is challenging since its operational environment changes during execution, which requires re-training of the model in order to maintain prediction accuracy. Training a new model from scratch generally involves extensive new measurements and often generates a data-collection overhead that negatively affects the service performance.In this paper, we investigate an approach for re-training neural-network models, which is based on transfer learning. Under this approach, a limited number of neural-network layers are re-trained while others remain unchanged. We study the accuracy of the re-trained model and the efficiency of the method with respect to the number of re-trained layers and the number of new measurements. The evaluation is performed using traces collected from a testbed that runs a Video-on-Demand service and a Key-Value Store under various load conditions. We study model re-training after changes in load pattern, infrastructure configuration, service configuration, and target metric. We find that our method significantly reduces the number of new measurements required to compute a new model after a change. The reduction exceeds an order of magnitude in most cases.
|
|
| 7. |
- Samani, Forough Shahab, et al.
(författare)
-
Demonstration : Predicting distributions of service metrics
- 2019
-
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
-
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.
|
|
