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SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
The Vision of Autonomic Computing
Computer
Impact of Network Density on Data Aggregation in Wireless Sensor Networks
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
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
A-GAP: An Adaptive Protocol for Continuous Network Monitoring with Accuracy Objectives
IEEE Transactions on Network and Service Management
Adaptive real-time monitoring for large-scale networked systems
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
International Journal of Network Management
ASMTA'11 Proceedings of the 18th international conference on Analytical and stochastic modeling techniques and applications
A CIM-based framework to manage monitoring adaptability
Proceedings of the 8th International Conference on Network and Service Management
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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-GAP, 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 trade-off 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 AGAP 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.