Cross-Layer Peer-to-Peer Track Identification and Optimization Based on Active Networking

P2P applications appear to emerge as ultimate killer applications due to their ability to construct highly dynamic overlay topologies with rapidly-varying and unpredictable traffic dynamics, which can constitute a serious challenge even for significantly over-provisioned IP networks. As a result, ISPs are facing new, severe network management problems that are not guaranteed to be addressed by statically deployed network engineering mechanisms. As a first step to a more complete solution to these problems, this paper proposes a P2P measurement, identification and optimisation architecture, designed to cope with the dynamicity and unpredictability of existing, well-known and future, unknown P2P systems. The purpose of this architecture is to provide to the ISPs an effective and scalable approach to control and optimise the traffic produced by P2P applications in their networks. This can be achieved through a combination of different application and network-level programmable techniques, leading to a crosslayer identification and optimisation process. These techniques can be applied using Active Networking platforms, which are able to quickly and easily deploy architectural components on demand. This flexibility of the optimisation architecture is essential to address the rapid development of new P2P protocols and the variation of known protocols

Why (and How) Networks Should Run Themselves

The proliferation of networked devices, systems, and applications that we depend on every day makes managing networks more important than ever. The increasing security, availability, and performance demands of these applications suggest that these increasingly difficult network management problems be solved in real time, across a complex web of interacting protocols and systems. Alas, just as the importance of network management has increased, the network has grown so complex that it is seemingly unmanageable. In this new era, network management requires a fundamentally new approach. Instead of optimizations based on closed-form analysis of individual protocols, network operators need data-driven, machine-learning-based models of end-to-end and application performance based on high-level policy goals and a holistic view of the underlying components. Instead of anomaly detection algorithms that operate on offline analysis of network traces, operators need classification and detection algorithms that can make real-time, closed-loop decisions. Networks should learn to drive themselves. This paper explores this concept, discussing how we might attain this ambitious goal by more closely coupling measurement with real-time control and by relying on learning for inference and prediction about a networked application or system, as opposed to closed-form analysis of individual protocols

Optimal experiment design in a filtering context with application to sampled network data

We examine the problem of optimal design in the context of filtering multiple random walks. Specifically, we define the steady state E-optimal design criterion and show that the underlying optimization problem leads to a second order cone program. The developed methodology is applied to tracking network flow volumes using sampled data, where the design variable corresponds to controlling the sampling rate. The optimal design is numerically compared to a myopic and a naive strategy. Finally, we relate our work to the general problem of steady state optimal design for state space models.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS283 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Optimization of Planck/LFI on--board data handling

To asses stability against 1/f noise, the Low Frequency Instrument (LFI) onboard the Planck mission will acquire data at a rate much higher than the data rate allowed by its telemetry bandwith of 35.5 kbps. The data are processed by an onboard pipeline, followed onground by a reversing step. This paper illustrates the LFI scientific onboard processing to fit the allowed datarate. This is a lossy process tuned by using a set of 5 parameters Naver, r1, r2, q, O for each of the 44 LFI detectors. The paper quantifies the level of distortion introduced by the onboard processing, EpsilonQ, as a function of these parameters. It describes the method of optimizing the onboard processing chain. The tuning procedure is based on a optimization algorithm applied to unprocessed and uncompressed raw data provided either by simulations, prelaunch tests or data taken from LFI operating in diagnostic mode. All the needed optimization steps are performed by an automated tool, OCA2, which ends with optimized parameters and produces a set of statistical indicators, among them the compression rate Cr and EpsilonQ. For Planck/LFI the requirements are Cr = 2.4 and EpsilonQ <= 10% of the rms of the instrumental white noise. To speedup the process an analytical model is developed that is able to extract most of the relevant information on EpsilonQ and Cr as a function of the signal statistics and the processing parameters. This model will be of interest for the instrument data analysis. The method was applied during ground tests when the instrument was operating in conditions representative of flight. Optimized parameters were obtained and the performance has been verified, the required data rate of 35.5 Kbps has been achieved while keeping EpsilonQ at a level of 3.8% of white noise rms well within the requirements.Comment: 51 pages, 13 fig.s, 3 tables, pdflatex, needs JINST.csl, graphicx, txfonts, rotating; Issue 1.0 10 nov 2009; Sub. to JINST 23Jun09, Accepted 10Nov09, Pub.: 29Dec09; This is a preprint, not the final versio