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Controlling Performance Trade-offs in Adaptive Network Monitoring
KTH, School of Electrical Engineering (EES), Communication Networks.
KTH, School of Electrical Engineering (EES), Communication Networks.
2009 (English)In: 11th IFIP/IEEE International Symposium on Integrated Network Management (IM 2009), IEEE , 2009, 359--366 p.Conference paper, Published paper (Refereed)
Abstract [en]

A key requirement for autonomic (i.e., self-*) management systems is a short adaptation time to changes in the networking conditions. In this paper, we show that the adaptation time of a distributed monitoring protocol can be controlled. We show this for A-CAP, a protocol for continuous monitoring of global metrics with controllable accuracy. We demonstrate through simulations that, for the case of A-GAP, the choice of the topology of the aggregation tree controls the tradeoff between adaptation time and protocol overhead in steady-state. Generally, allowing a larger adaptation time permits reducing the protocol overhead. Our results suggest that the adaptation time primarily depends on the height of the aggregation tree and that the protocol overhead is strongly influenced by the number of internal nodes. We outline how A-GAP can be extended to dynamically self-configure and to continuously adapt its configuration to changing conditions, in order to meet a set of performance objectives, including adaptation time, protocol overhead, and estimation accuracy.

Place, publisher, year, edition, pages
IEEE , 2009. 359--366 p.
Keyword [en]
Adaptive management, real-time monitoring, large-scale distributed systems
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-9467DOI: 10.1109/INM.2009.5188836ISI: 000274304300057Scopus ID: 2-s2.0-70449386894ISBN: 978-1-4244-3486-2 (print)OAI: oai:DiVA.org:kth-9467DiVA: diva2:114075
Conference
IFIP/IEEE International Symposium on Integrated Network Management (IM 2009) New York, NY, JUN 01-05, 2009
Note
“© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” QC 20100617Available from: 2012-02-15 Created: 2008-11-05 Last updated: 2012-02-15Bibliographically 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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Citation style
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