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Monitoring Flow Aggregates with Controllable Accuracy
KTH, School of Electrical Engineering (EES), Communication Networks.
KTH, School of Electrical Engineering (EES), Communication Networks.
2007 (English)In: Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349, Vol. 4787, 64-75 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we show the feasibility of real-time flow monitoringwith controllable accuracy in today’s IP networks. Our approach is based onNetflow and A-GAP. A-GAP is a protocol for continuous monitoring ofnetwork state variables, which are computed from device metrics usingaggregation functions, such as SUM, AVERAGE and MAX. A-GAP isdesigned to achieve a given monitoring accuracy with minimal overhead. AGAPis decentralized and asynchronous to achieve robustness and scalability.The protocol incrementally computes aggregation functions inside the networkand, based on a stochastic model, it dynamically configures local filters thatcontrol the overhead and accuracy. We evaluate a prototype in a testbed of 16commercial routers and provide measurements from a scenario where theprotocol continuously estimates the total number of FTP flows in the network.Local flow metrics are read out from Netflow buffers and aggregated in realtime.We evaluate the prototype for the following criteria. First, the ability toeffectively control the trade off between monitoring accuracy and processingoverhead; second, the ability to accurately predict the distribution of theestimation error ; third, the impact of a sudden change in topology on theperformance of the protocol. The testbed measurements are consistent withsimulation studies we performed for different topologies and network sizes,which proves the feasibility of the protocol design, and, more generally, thefeasibility of effective and efficient real-time flow monitoring in large networkenvironments.

Place, publisher, year, edition, pages
2007. Vol. 4787, 64-75 p.
Keyword [en]
Computer simulation; Error analysis; Network protocols; Robustness (control systems); Routers; Scalability; Controllable accuracy; Estimation error; Local flow metrics
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-9465DOI: 10.1007/978-3-540-75869-3_6ISI: 000251162800006Scopus ID: 2-s2.0-38149116163OAI: oai:DiVA.org:kth-9465DiVA: diva2:114072
Note
Conference: 10th IFIP/IEEE International Conference on Management of Multimedia and Mobile Networks and Services. San Jose, CA. OCT 31-NOV 02, 2007 Available from: 2008-11-05 Created: 2008-11-05 Last updated: 2017-12-14Bibliographically approved
In thesis
1. Adaptive Real-time Monitoring for Large-scale Networked Systems
Open this publication in new window or tab >>Adaptive Real-time Monitoring for Large-scale Networked Systems
2008 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

Large-scale networked systems, such as the Internet and server clusters, are omnipresent today. They increasingly deliver services that are critical to both businesses and the society at large, and therefore their continuous and correct operation must be guaranteed. Achieving this requires the realization of adaptive management systems, which continuously reconfigure such large-scale dynamic systems, in order to maintain their state near a desired operating point, despite changes in the networking conditions.The focus of this thesis is continuous real-time monitoring, which is essential for the realization of adaptive management systems in large-scale dynamic environments. Real-time monitoring provides the necessary input to the decision-making process of network management, enabling management systems to perform self-configuration and self-healing tasks.We have developed, implemented, and evaluated a design for real-time continuous monitoring of global metrics with performance objectives, such as monitoring overhead and estimation accuracy. Global metrics describe the state of the system as a whole, in contrast to local metrics, such as device counters or local protocol states, which capture the state of a local entity. Global metrics are computed from local metrics using aggregation functions, such as SUM, AVERAGE and MAX.Our approach is based on in-network aggregation, where global metrics are incrementally computed using spanning trees. Performance objectives are achieved through filtering updates to local metrics that are sent along that tree. A key part in the design is a model for the distributed monitoring process that relates performance metrics to parameters that tune the behavior of a monitoring protocol. The model allows us to describe the behavior of individual nodes in the spanning tree in their steady state. The model has been instrumental in designing a monitoring protocol that is controllable and achieves given performance objectives.We have evaluated our protocol, called A-GAP, experimentally, through simulation and testbed implementation. It has proved to be effective in meeting performance objectives, efficient, adaptive to changes in the networking conditions, controllable along different performance dimensions, and scalable. We have implemented a prototype on a testbed of commercial routers. The testbed measurements are consistent with simulation studies we performed for different topologies and network sizes. This proves the feasibility of the design, and, more generally, the feasibility of effective and efficient real-time monitoring in large network environments.

Place, publisher, year, edition, pages
Stockholm: KTH, 2008. 46 p.
Series
Trita-EE, ISSN 1653-5146 ; 2008:051
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-9459 (URN)978-91-7415-168-8 (ISBN)
Public defence
2008-11-21, Salongen, KTHB,, Osquarsbacke 31, KTH, Stockholm, 10:00 (English)
Opponent
Supervisors
Note
QC 20100727Available from: 2008-11-05 Created: 2008-11-05 Last updated: 2010-07-27Bibliographically approved

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