Change search
Refine search result
1 - 20 of 20
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Ahmed, Jawwad Imtiaz
    et al.
    Ericsson Research, Sweden.
    Josefsson, Tim
    Uppsala University, Sweden.
    Johnsson, Andreas
    Ericsson Research, Sweden.
    Flinta, Christofer
    Ericsson Research, Sweden.
    Moradi, Farnaz
    Ericsson Research, Sweden.
    Pasquini, Rafael
    UFU Federal University of Uberlandia, Brazil.
    Stadler, Rolf
    RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.
    Automated diagnostic of virtualized service performance degradation2018In: IEEE/IFIP Network Operations and Management Symposium: Cognitive Management in a Cyber World, NOMS 2018, 2018Conference paper (Refereed)
    Abstract [en]

    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.

  • 2.
    Ahmed, Jawwad
    et al.
    Ericsson Research, Sweden.
    Johnsson, Andreas
    Ericsson Research, Sweden.
    Moradi, Farnaz
    Ericsson Research, Sweden.
    Pasquini, Rafael
    KTH Royal Institute of Technology, Sweden; Federal University of Uberlandia, Brazil.
    Flinta, Christofer
    Ericsson Research, Sweden.
    Stadler, Rolf
    RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.
    Online approach to performance fault localization for cloud and datacenter services2017In: Proceedings of the IM 2017 - 2017 IFIP/IEEE International Symposium on Integrated Network and Service Management, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 873-874Conference paper (Refereed)
    Abstract [en]

    Automated detection and diagnosis of the performance faults in cloud and datacenter environments is a crucial task to maintain smooth operation of different services and minimize downtime. We demonstrate an effective machine learning approach based on detecting metric correlation stability violations (CSV) for automated localization of performance faults for datacenter services running under dynamic load conditions.

  • 3. Brunner, Marcus
    et al.
    Galis, Alex
    Cheng, Lawrence
    Colas, Jorge Andres
    Ahlgren, Bengt
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gunnar, Anders
    RISE, Swedish ICT, SICS.
    Abrahamsson, Henrik
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Szabo, Robert
    Csaba, Simon
    Nielsen, Johan
    Schuetz, Simon
    Prieto, Alberto Gonzalez
    Stadler, Rolf
    Molnar, Gergely
    Towards Ambient Networks Management2005In: IEEE MATA 2005 Second International Workshop on Mobility Aware Technologies and Applications, 2005, 1Conference paper (Refereed)
  • 4.
    Flinta, Christofer
    et al.
    Ericsson Research, Sweden.
    Johnsson, Andreas
    Ericsson Research, Sweden.
    Ahmed, Jawwad
    Ericsson Research, Sweden.
    Moradi, Farnaz
    Ericsson Research, Sweden.
    Pasquini, Rafael
    RISE - Research Institutes of Sweden, ICT, SICS. Federal University of Uberlandia, Brazil.
    Stadler, Rolf
    RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.
    Real-time resource prediction engine for cloud management2017In: Proceedings of the IM 2017 - 2017 IFIP/IEEE International Symposium on Integrated Network and Service Management, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 877-878Conference paper (Refereed)
    Abstract [en]

    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. © 2017 IFIP.

  • 5. Jurca, Dan
    et al.
    Stadler, Rolf
    RISE, Swedish ICT, SICS.
    Computing histograms of local variables for real-time monitoring using aggregation trees2009Conference paper (Refereed)
  • 6.
    Moradi, F.
    et al.
    Ericsson Research, Sweden.
    Stadler, Rolf
    RISE - Research Institutes of Sweden, ICT, SICS.
    Johnsson, A.
    Ericsson Research, Sweden.
    Performance prediction in dynamic clouds using transfer learning2019In: 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 242-250Conference paper (Refereed)
    Abstract [en]

    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.
    Pasquini, Rafael
    et al.
    UFU Federal University of Uberlândia, Brazil; KTH Royal Institute of Technology, Sweden.
    Moradi, Fahrnaz
    Ericsson Research, Sweden.
    Ahmed, Jawwad
    Ericsson Research, Sweden.
    Johnsson, Andreas
    Ericsson Research, Sweden.
    Flinta, Cristofer
    Ericsson Research, Sweden.
    Stadler, Rolf
    RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.
    Predicting SLA conformance for cluster-based services2017In: 2017 IFIP Networking Conference, IFIP Networking 2017 and Workshops, 2017, p. 1-2Conference paper (Refereed)
    Abstract [en]

    The ability to predict conformance or violation for given Service-level Agreements (SLAs) is critical for service assurance. We demonstrate a prototype for real-time conformance prediction based on the concept of the capacity region, which abstracts the underlying ICT infrastructure with respect to the load it can carry for a given SLA. The capacity region is estimated through measurements and statistical learning. We demonstrate prediction for a key-value store (Voldemort) that runs on a server cluster located at KTH.

  • 8.
    Pasquini, Rafael
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS. Federal University of Uberlandia, Brazil; KTH Royal Institute of Technology, Sweden.
    Stadler, Rolf
    RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.
    Learning end-to-end application QoS from openflow switch statistics2017In: 2017 IEEE Conference on Network Softwarization: Softwarization Sustaining a Hyper-Connected World: en Route to 5G, NetSoft 2017, Institute of Electrical and Electronics Engineers Inc. , 2017Conference paper (Refereed)
    Abstract [en]

    We use statistical learning to estimate end-to-end QoS metrics from device statistics, collected from a server cluster and an OpenFlow network. The results from our testbed, which runs a video-on-demand service and a key-value store, demonstrate that the learned models can estimate QoS metrics like frame rate or response time with errors bellow 10% for a given client. Interestingly, we find that service-level QoS metrics seem "encoded" in network statistics and it suffices to collect OpenFlow per port statistics to achieve accurate estimation at small overhead for data collection and model computation.

  • 9.
    Samani, F. S.
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.
    Stadler, Rolf
    RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.
    Predicting Distributions of Service Metrics using Neural Networks2018In: 14th International Conference on Network and Service Management, CNSM 2018 and Workshops, 1st International Workshop on High-Precision Networks Operations and Control, HiPNet 2018 and 1st Workshop on Segment Routing and Service Function Chaining, SR+SFC 2018, 2018, p. 45-53Conference paper (Refereed)
    Abstract [en]

    We predict the conditional distributions of service metrics, such as response time or frame rate, from infrastructure measurements in a cloud environment. From such distributions, key statistics of the service metrics, including mean, variance, or percentiles can be computed, which are essential for predicting SLA conformance or enabling service assurance. We model the distributions as Gaussian mixtures, whose parameters we predict using mixture density networks, a class of neural networks. We apply the method to a VoD service and a KV store running on our lab testbed. The results validate the effectiveness of the method when applied to operational data. In the case of predicting the mean of the frame rate or response time, the accuracy matches that of random forest, a baseline model.

  • 10.
    Shahab, F.
    et al.
    KTH Royal Institute of Technology, Sweden.
    Stadler, Rolf
    RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.
    Johnsson, A.
    Ericsson Research, Sweden.
    Flinta, C.
    Ericsson Research, Sweden.
    Demonstration: Predicting distributions of service metrics2019In: 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 745-746Conference paper (Refereed)
    Abstract [en]

    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.

  • 11.
    Stadler, Rolf
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.
    Pasquini, Rafael
    UFU Federal University of Uberlândia, Brazil.
    Fodor, Viktoria
    KTH Royal Institute of Technology, Sweden.
    Learning from Network Device Statistics2017In: Journal of Network and Systems Management, ISSN 1064-7570, E-ISSN 1573-7705, Vol. 25, no 4, p. 672-698Article in journal (Refereed)
    Abstract [en]

    We estimate end-to-end service metrics from network device statistics. Our approach is based upon statistical, supervised learning, whereby the mapping from device-level to service-level metrics is learned from observations, i.e., through monitoring the system. The approach enables end-to-end performance prediction without requiring an explicit model of the system, which is different from traditional engineering techniques that use stochastic modeling and simulation. The fact that end-to-end service metrics can be estimated from local network statistics with good accuracy in the scenarios we consider suggests that service-level properties are “encoded” in network-level statistics. We show that the set of network statistics needed for estimation can be reduced to a set of measurements along the network path between client and service backend, with little loss in estimation accuracy. The reported work is largely experimental and its results have been obtained through testbed measurements from a video streaming service and a KV store over an OpenFlow network .

  • 12. Wuhib, Fetahi
    et al.
    Dam, Mads
    CNS.
    Stadler, Rolf
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    A Gossiping Protocol for Detecting Global Threshold Crossings2010In: IEEE Transactions on Network and Service Management (TNSM), Vol. 7, no 1Article in journal (Refereed)
  • 13. Wuhib, Fetahi
    et al.
    Dam, Mads
    RISE, Swedish ICT, SICS.
    Stadler, Rolf
    RISE, Swedish ICT, SICS.
    Gossiping for threshold detection2009Conference paper (Refereed)
  • 14. Wuhib, Fetahi
    et al.
    Stadler, Rolf
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Distributed monitoring and resource management for large cloud environments2011Conference paper (Refereed)
  • 15. Wuhib, Fetahi
    et al.
    Stadler, Rolf
    RISE, Swedish ICT, SICS.
    Lindgren, H.
    Dynamic Resource Allocation with Management Objectives: Implementation for an OpenStack Cloud2012Conference paper (Refereed)
  • 16. Wuhib, Fetahi
    et al.
    Stadler, Rolf
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Spreitzer, Mike
    A Gossip Protocol for Dynamic Resource Management in Large Cloud Environments2012In: IEEE Transactions on Network and Service management (TNSM), Vol. 9Article in journal (Refereed)
  • 17. Wuhib, Fetahi
    et al.
    Stadler, Rolf
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Spreitzer, Mike
    Gossip-based Resource Management for Cloud Environments2010Conference paper (Refereed)
  • 18. Yanggratoke, Rerngvit
    et al.
    Ahmed, Jawwad
    Ardelius, John
    RISE, Swedish ICT, SICS.
    Flinta, Christofer
    Johnsson, Andreas (Ericsson Research)
    Gillblad, Daniel
    RISE, Swedish ICT, SICS.
    Stadler, Rolf
    Predicting Real-time Service-level Metrics from Device Statistics2014Report (Other academic)
    Abstract [en]

    While real-time service assurance is critical for emerging telecom cloud services, understanding and predicting performance metrics for such services is hard. In this paper, we pursue an approach based upon statistical learning whereby the behavior of the target system is learned from observations. We use methods that learn from device statistics and predict metrics for services running on these devices. Specifically, we collect statistics from a Linux kernel of a server machine and predict client-side metrics for a video-streaming service (VLC). The fact that we collect thousands of kernel variables, while omitting service instrumentation, makes our approach service- independent and unique. While our current lab configuration is simple, our results, gained through extensive experimentation, prove the feasibility of accurately predicting client-side metrics, such as video frame rates and RTP packet rates, often within 10-15% error (NMAE), also under high computational load and across traces from different scenarios.

  • 19.
    Yanggratoke, Rerngvit
    et al.
    KTH Royal Institute of Technology, Sweden.
    Ahmed, Jawwad
    Ericsson Research, Sweden.
    Ardelius, John
    RISE - Research Institutes of Sweden, ICT, SICS.
    Flinta, Christofer
    Ericsson Research, Sweden.
    Johnsson, Andreas
    Ericsson Research, Sweden.
    Gillblad, Daniel
    RISE - Research Institutes of Sweden, ICT, SICS.
    Stadler, Rolf
    RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.
    A service-agnostic method for predicting service metrics in real time2018In: International Journal of Network Management, ISSN 1055-7148, E-ISSN 1099-1190, Vol. 28, no 2, article id e1991Article in journal (Refereed)
    Abstract [en]

    We predict performance metrics of cloud services using statistical learning, whereby the behaviour of a system is learned from observations. Specifically, we collect device and network statistics from a cloud testbed and apply regression methods to predict, in real-time, client-side service metrics for video streaming and key-value store services. Results from intensive evaluation on our testbed indicate that our method accurately predicts service metrics in real time (mean absolute error below 16% for video frame rate and read latency, for instance). Further, our method is service agnostic in the sense that it takes as input operating systems and network statistics instead of service-specific metrics. We show that feature set reduction significantly improves the prediction accuracy in our case, while simultaneously reducing model computation time. We find that the prediction accuracy decreases when, instead of a single service, both services run on the same testbed simultaneously or when the network quality on the path between the server cluster and the client deteriorates. Finally, we discuss the design and implementation of a real-time analytics engine, which processes streams of device statistics and service metrics from testbed sensors and produces model predictions through online learning. 

  • 20. Yanggratoke, Rerngvit
    et al.
    Wuhib, Fetahi
    Stadler, Rolf
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gossip-based Resource Allocation for Green Computing in Large Clouds2011Conference paper (Refereed)
1 - 20 of 20
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
v. 2.35.7