The Vision of Autonomic Computing
Computer
Gossip-Based Computation of Aggregate Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Balancing energy efficiency and quality of aggregate data in sensor networks
The VLDB Journal — The International Journal on Very Large Data Bases
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Gossip-based aggregation in large dynamic networks
ACM Transactions on Computer Systems (TOCS)
Network Management Fundamentals
Network Management Fundamentals
Self-Configuration of Network Services with Biologically Inspired Learning and Adaptation
Journal of Network and Systems Management
Controlling performance trade-offs in adaptive network monitoring
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
A-GAP: An Adaptive Protocol for Continuous Network Monitoring with Accuracy Objectives
IEEE Transactions on Network and Service Management
IEEE Journal on Selected Areas in Communications
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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. 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. 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 has been instrumental in designing a monitoring protocol that is controllable and achieves given performance objectives. Our design 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, which proves the feasibility of the design, and, more generally, the feasibility of effective and efficient real-time monitoring in large network environments.