Building Quality-of-Service Monitoring Systems for Traffic Engineering and Service Management
Journal of Network and Systems Management
The role of agents in enterprise system management: a position paper
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Context Distribution System through brokers and simple APIs
International Journal of Internet Protocol Technology
Decentralized detection of global threshold crossings using aggregation trees
Computer Networks: The International Journal of Computer and Telecommunications Networking
Self-adaptive software: Landscape and research challenges
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Gossiping for threshold detection
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
Pervasive and Mobile Computing
A geometric approach to monitoring threshold functions over distributed data streams
Ubiquitous knowledge discovery
Decentralized computation of threshold crossing alerts
DSOM'05 Proceedings of the 16th IFIP/IEEE Ambient Networks international conference on Distributed Systems: operations and Management
A survey and taxonomy of on-chip monitoring of multicore systems-on-chip
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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Networks are monitored in order to ensure that the system operates within desirable parameters. The increasing complexity of networks and services provided by them increases this need for monitoring. Monitoring consists of measuring properties of the network, and of inferring an aggregate predicate from these measurements. Conducting such monitoring introduces traffic overhead that may reduce the overall effective throughput. This paper studies ways to minimize the monitoring communication overhead in IP networks. We develop and analyze several monitoring algorithms that achieve significant reduction in the management overhead while maintaining the functionality. The main idea is to combine global polling with local event driven reporting. The amount of traffic saving depends on the statistical characterization of the monitored data. We indicate the specific statistical factors that affect the saving and show how to choose the right algorithm for the type, of monitored data. In particular, our results show that for Internet traffic our algorithms can save more than 90% of the monitoring traffic