Adaptive, Robust, Real-time Monitoring for Large-scale Networks and Networked Systems - MONITOR
Academic coordinator: Mads Dam (FM)
Project leader: Dan Jurca (LCN)
Other project members: Aurell (CB), Dam (FM), Johansson (CT), Stadler (LCN), Krishnamurthy (SICS, not funded by this project)
The objective of the MONITOR project is to study protocols and principles of information aggregation in large-scale and dynamic networks under strong performance, security, and real-time constraints. The primary application domain is distributed network management. A key function that a distributed management layer must provide is estimating aggregates of local variables in real time. Examples of aggregates include sums, averages, extremal values, percentiles and histograms of device counters, to provide management information concerning distribution of flows, node utilization, etc. The project will examine and compare various approaches to distributed information aggregation, propose new solutions where applicable, and evaluate them according to a range of criteria including accuracy, scalability, privacy, robustness to node and link failures, including both random (crash) failures, malicious failures, and node corruption, (self-) configurability, and tunability. Besides theoretical results, the outcome of the project will include new protocol designs, and experimental evaluation in terms of simulations and testbed implementations.
Below we list a set of work topics including names of senior researchers we expect to be involved. Students and postdocs will also be involved. Each work topic is expected to result in 1-3 publications including at least one journal publication within the 2 year span of the project. Lead scientist for each deliverable is underlined.
Researchers: Dam, Johansson, Stadler
Spanning trees is a common technique for robust aggregation. We examine the use of spanning trees for distributed threshold detection.
Researchers: Dam, Stadler.
Efficient solutions for threshold detection using gossiping are not currently known. We will propose and evaluate solutions for this.
Researchers: Aurell, Dam, Johansson, Krishnamurthy, Stadler.
We examine analytic methods to evaluate performance characteristics of different aggregation schemes, and to trade off performance parameters against each other.
Researchers: Dam, Stadler
Privacy is an important concerning, for instance in multidomain management. We will explore the applicability of secure multiparty computation for this.
Researchers: Dam, Stadler.
We examine ways of extending existing gossiping protocols to harden them against crash failures.
Researchers: Aurell, Dam, Johansson, Krishnamurthy, Stadler
As yet, little work has been done to analyze the behaviour of state aggregation protocols under churn, i.e. under dynamic failures and topology changes. This work topic extends previous work by Krishnamurthy and Aurell on this topic.
Title: Service Middleware for Self-Managing Large-Scale Systems
Journal: IEEE Transactions on Network and Service Management (TNSM)
Date: December 2007
Date: July 2008,
This paper develops a new incremental subgradient method for distributed optimization which relies on peer-to-peer communication only. Such methods are useful for a wide range of distributed management problems, including distributed computation of averages and rankings.
Booktitle: Proceedings IEEE Conference on Decision and Control
Date: December 2008
Detailed packet-level simulations of a selection of distributed optimization methods are performed for resource-constrained wireless sensor networks, and an evaluation and comparison of the convergence speed and signaling requirements is presented.
This paper presents a novel distributed optimization scheme which combines consensus iterations with subgradient optimization. Similar to the Markov randomized subgradient method developed by the authors, the novel scheme relies on peer-to-peer communication only. Theoretical convergence results are combined with numerical simulations.
Title: “Key Research Challenges in Network Management,”
Journal: IEEE Communications Magazine, Vol.45, Issue 10, October 2007.
Title: Controlling Performance Trade-offs in Decentralized Network Monitoring
Institution: Royal Institute of Technology (KTH)
Date: May 2008
Note: Submitted to the
19th IFIP/IEEE Distributed Systems: Operations and Management (DSOM 2008),
Title: Monitoring Flow Aggregates with Controllable Accuracy
In: Proceedings 10th
IFIP/IEEE International Conference on Management of Multimedia and
Date: October 2007
Journal: IEEE Transactions on Network and Service Management (TNSM), Vol. 4, No. 1, June 2007
Journal: Computer Networks
Date: February 2008
We propose a tree-based algorithm for distributed, scalable threshold detection for dynamic networks. The algorithm uses hysteresis to reduce management overhead when aggregates are far from the relevant thresholds.
Date: September 2008
The paper explores several candidate protocols which uses hysteresis-like mechanisms to reduce overhead for the case of threshold detection
Journal: IEEE Transactions on Network and Service Management
Date: March 2008
We propose versions of the Push-Synopses protocol due to Kempe, Dobra, and Gehrke, FOCS'03, hardened against crash failures.