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  • 1. Ahmed, J.
    et al.
    Johnsson, A.
    Moradi, F.
    Pasquini, R.
    Flinta, C.
    Stadler, Rolf
    KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Online approach to performance fault localization for cloud and datacenter services2017Ingår i: Proceedings of the IM 2017 - 2017 IFIP/IEEE International Symposium on Integrated Network and Service Management, Institute of Electrical and Electronics Engineers Inc. , 2017, s. 873-874Konferensbidrag (Refereegranskat)
    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. © 2017 IFIP.

  • 2. Ahmed, J.
    et al.
    Johnsson, A.
    Yanggratoke, Rerngvit
    KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre. KTH, Skolan för elektro- och systemteknik (EES), Kommunikationsnät.
    Ardelius, J.
    Flinta, C.
    Stadler, Rolf
    KTH, Skolan för elektro- och systemteknik (EES), Kommunikationsnät. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Predicting SLA conformance for cluster-based services using distributed analytics2016Ingår i: Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium, IEEE conference proceedings, 2016, s. 848-852Konferensbidrag (Refereegranskat)
    Abstract [en]

    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.

  • 3. Ahmed, J.
    et al.
    Josefsson, T.
    Johnsson, A.
    Flinta, C.
    Moradi, F.
    Pasquini, R.
    Stadler, Rolf
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Nätverk och systemteknik. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Automated diagnostic of virtualized service performance degradation2018Ingår i: IEEE/IFIP Network Operations and Management Symposium: Cognitive Management in a Cyber World, NOMS 2018, Institute of Electrical and Electronics Engineers Inc. , 2018, s. 1-9Konferensbidrag (Refereegranskat)
    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. © 2018 IEEE.

  • 4.
    Burgess, Mark
    et al.
    Oslo Univ Coll, Oslo, Norway..
    Disney, Matthew
    Oslo Univ Coll, Oslo, Norway..
    Stadler, Rolf
    KTH.
    Network patterns in cfengine and scalable data aggregation2007Ingår i: USENIX ASSOCIATION PROCEEDING OF THE 21ST LARGE INSTALLATION SYSTEMS ADMINISTRATION CONFERENCE, USENIX ASSOC , 2007, s. 275-+Konferensbidrag (Refereegranskat)
    Abstract [en]

    Network patterns are based on generic algorithms that execute on tree-based overlays. A set of such patterns has been developed at KTH to support distributed monitoring in networks with non-trivial topologies. We consider the use of this approach in logical peer networks in cfengine as a way of scaling aggregation of data to large organizations. Use of 'deep' network structures can lead to temporal anomalies. We show how to minimize temporal fragmentation during data aggregation by using time offsets and what effect these choices might have on power consumption. We offer proof of concept for this technology to initiate either multicast or inverse multicast pulses through sensor networks.

  • 5. Flinta, C.
    et al.
    Johnsson, A.
    Ahmed, J.
    Moradi, F.
    Pasquini, R.
    Stadler, Rolf
    KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Real-time resource prediction engine for cloud management2017Ingår i: Proceedings of the IM 2017 - 2017 IFIP/IEEE International Symposium on Integrated Network and Service Management, Institute of Electrical and Electronics Engineers Inc. , 2017, s. 877-878Konferensbidrag (Refereegranskat)
    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.

  • 6.
    Moradi, Farnaz
    et al.
    Ericsson Res, Stockholm, Sweden..
    Stadler, Rolf
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Nätverk och systemteknik. Swedish Inst Comp Sci RISE SICS, Stockholm, Sweden..
    Johnsson, Andreas
    Ericsson Res, Stockholm, Sweden..
    Performance Prediction in Dynamic Clouds using Transfer Learning2019Ingår i: 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019, IEEE, 2019, s. 242-250, artikel-id 8717847Konferensbidrag (Refereegranskat)
    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.
    KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre. SICS, Kista, Sweden.
    Moradi, Farnaz
    Ahmed, Jawwad
    Johnsson, Andreas
    Flinta, Christofer
    Stadler, Rolf
    KTH, Skolan för elektro- och systemteknik (EES), Nätverk och systemteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre. SICS, Kista, Sweden.
    Predicting SLA Conformance for Cluster-based Services2017Ingår i: 2017 IFIP Networking Conference (IFIP NETWORKING) and Workshops, IEEE, 2017Konferensbidrag (Refereegranskat)
    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.
    KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre. Fac Comp FACOM UFU, Uberlandia, MG, Brazil; Swedish Inst Comp Sci, Stockholm, Sweden.
    Stadler, Rolf
    KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Learning End-to-end Application QoS from OpenFlow Switch Statistics2017Ingår i: 2017 IEEE CONFERENCE ON NETWORK SOFTWARIZATION (IEEE NETSOFT), IEEE , 2017Konferensbidrag (Refereegranskat)
    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, Forough Shahab
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Nätverk och systemteknik.
    Stadler, Rolf
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Nätverk och systemteknik.
    Predicting Distributions of Service Metrics using Neural Networks2018Ingår i: 2018 14TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM) / [ed] Salsano, S Riggio, R Ahmed, T Samak, T DosSantos, CRP, IEEE , 2018, s. 45-53Konferensbidrag (Refereegranskat)
    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 Voll service and a KY 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.
    Samani, Forough Shahab
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Nätverk och systemteknik.
    Stadler, Rolf
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Nätverk och systemteknik. RISE SICS, Lulea, Sweden..
    Johnsson, Andreas
    Ericsson Res, Gothenburg, Sweden..
    Flinta, Christofer
    Ericsson Res, Gothenburg, Sweden..
    Demonstration: Predicting Distributions of Service Metrics2019Ingår i: 2019 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Institute of Electrical and Electronics Engineers (IEEE), 2019, s. 745-746, artikel-id 8717915Konferensbidrag (Refereegranskat)
    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 real-time, 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.
    KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre. Swedish Institute of Computer Science (RISE SICS) Kista Sweden.
    Pasquini, Rafael
    Fodor, Viktoria
    KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Learning from Network Device Statistics2017Ingår i: Journal of Network and Systems Management, ISSN 1064-7570, E-ISSN 1573-7705, Vol. 25, nr 4, s. 672-698Artikel i tidskrift (Refereegranskat)
    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.
    Uddin, Misbah
    et al.
    KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Stadler, Rolf
    KTH, Skolan för elektro- och systemteknik (EES), Kommunikationsnät. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    A bottom-up approach to real-time search in large networks and clouds2016Ingår i: Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium, IEEE conference proceedings, 2016, s. 985-990Konferensbidrag (Refereegranskat)
    Abstract [en]

    Networked systems, such as telecom networks and cloud infrastructures, generate and hold vast amounts of configuration and operational data. The goal of this work is to make all this data available through a real-time search process named network search, which will enable new real-time management solutions. The thesis contains several contributions towards engineering a network search system. Key elements of our design are a weakly structured information model that includes spatial properties, a query language that supports location- and schema-oblivious search queries, a peer-to-peer architecture, an echo protocols for scalable query processing, and an indexing protocol for efficient routing for spatial queries. The data against which network search is performed is maintained in local real-time databases close to the data sources. The design follows a bottom-up approach in the sense that the topology for query routing is constructed from the underlying network topology. We have built a prototype of the system on a cloud testbed and developed applications that use network search functionality. Testbed measurements suggest that it is feasible to engineer a network search system that processes queries at low latency and low overhead and that can scale to 100'000 nodes. Simulation results for spatial queries show that query processing achieves response times and incurs overhead close to an optimal protocol, and that it remains accurate under significant churn.

  • 13.
    Uddin, Misbah
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Stadler, Rolf
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Nätverk och systemteknik. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Clemm, Alexander
    Huawei USA Futurewei Technol Inc, Santa Clara, CA USA..
    A bottom-up design for spatial search in large networks and clouds2018Ingår i: International Journal of Network Management, ISSN 1055-7148, E-ISSN 1099-1190, Vol. 28, nr 6, artikel-id e2041Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    APPENDIX Information in networked systems often has spatial semantics: routers, sensors, or virtual machines have coordinates in a geographical or virtual space, for instance. In this paper, we propose a design for a spatial search system that processes queries against spatial information that is maintained in local databases inside a large networked system. In contrast to previous works in spatial databases and peer-to-peer designs, our design is bottom-up, which makes query routing network aware and thus efficient, and which facilitates system bootstrapping and adaptation. Key to our design is a protocol that creates and maintains a distributed index of object locations based on information from local databases and the underlying network topology. The index builds upon minimum bounding rectangles to efficiently encode locations. We present a generic search protocol that is based on an echo protocol and uses the index to prune the search space and perform query routing. The response times of search queries increase with the diameter of the network, which is asymptotically optimal. We study the performance of the protocol through simulation in static and dynamic network environments, for different network topologies, and for network sizes up to 100 000 nodes. In most experiments, the overhead incurred by our protocol lies well below 30% of a hypothetical optimal protocol. In addition, the protocol provides high accuracy under significant churn.

  • 14.
    Yanggratoke, Rerngvit
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Ahmed, Jawwad
    Ardelius, John
    Flinta, Christofer
    Johnsson, Andreas
    Gillblad, Daniel
    Stadler, Rolf
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Nätverk och systemteknik. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre. Swedish Institute of Computer Science (SICS), Sweden.
    A service-agnostic method for predicting service metrics in real time2018Ingår i: International Journal of Network Management, ISSN 1055-7148, E-ISSN 1099-1190, Vol. 28, nr 2, artikel-id e1991Artikel i tidskrift (Refereegranskat)
    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.

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