Francesco Lo Presti

Network tomography on general topologies

In this paper we consider the problem of inferring link-level loss rates from end-to-end multicast measurements taken from a collection of trees. We give conditions under which loss rates are identifiable on a specified set of links. Two algorithms are presented to perform the link-level inferences for those links on which losses can be identified. One, the minimum variance weighted average (MVWA) algorithm treats the trees separately and then averages the results. The second, based on expectation-maximization (EM) merges all of the measurements into one computation. Simulations show that EM is slightly more accurate than MVWA, most likely due to its more efficient use of the measurements. We also describe extensions to the inference of link-level delay, inference from end-to-end unicast measurements, and inference when some measurements are missing.

Multicast-based inference of network-internal delay distributions

Packet delay greatly influences the overall performance of network applications. It is therefore important to identify causes and locations of delay performance degradation within a network. Existing techniques, largely based on end-to-end delay measurements of unicast traffic, are well suited to monitor and characterize the behavior of particular end-to-end paths. Within these approaches, however, it is not clear how to apportion the variable component of end-to-end delay as queueing delay at each link along a path. Moreover, there are issues of scalability for large networks.In this paper, we show how end-to-end measurements of multicast traffic can be used to infer the packet delay distribution and utilization on each link of a logical multicast tree. The idea, recently introduced in [3] and [4], is to exploit the inherent correlation between multicast observations to infer performance of paths between branch points in a tree spanning a multicast source and its receivers. The method does not depend on cooperation from intervening network elements; because of the bandwidth efficiency of multicast traffic, it is suitable for large-scale measurements of both end-to-end and internal network dynamics. We establish desirable statistical properties of the estimator, namely consistency and asymptotic normality. We evaluate the estimator through simulation and observe that it is robust with respect to moderate violations of the underlying model.

The use of end-to-end multicast measurements for characterizing internal network behavior

We present a novel methodology for identifying internal network performance characteristics based on end-to-end multicast measurements. The methodology, solidly grounded on statistical estimation theory, can be used to characterize the internal loss and delay behavior of a network. Measurements on the MBone have been used to validate the approach in the case of losses. Extensive simulation experiments provide further validation of the approach, not only for losses, but also for delays. We also describe our strategy for deploying the methodology on the Internet. This includes the continued development of the National Internet Measurement Infrastructure to support RTP-based end-to-end multicast measurements and the development of software tools to analyze the traces. Once complete, this combined software/hardware infrastructure will provide a service for understanding and forecasting the performance of the Internet.

Fluid models and solutions for large-scale IP networks

In this paper we present a scalable model of a network of Active Queue Management (AQM) routers serving a large population of TCP flows. We present efficient solution techniques that allow one to obtain the transient behavior of the average queue lengths, packet loss probabilities, and average end-to-end latencies. We model different versions of TCP as well as different versions of RED, the most popular AQM scheme currently in use. Comparisons between our models and <tt>ns</tt> simulation show our models to be quite accurate while at the same time requiring substantially less time to solve, especially when workloads and bandwidths are high.

MOSES: A Framework for QoS Driven Runtime Adaptation of Service-Oriented Systems

Architecting software systems according to the service-oriented paradigm and designing runtime self-adaptable systems are two relevant research areas in todays software engineering. In this paper, we address issues that lie at the intersection of these two important fields. First, we present a characterization of the problem space of self-adaptation for service-oriented systems, thus providing a frame of reference where our and other approaches can be classified. Then, we present MOSES, a methodology and a software tool implementing it to support QoS-driven adaptation of a service-oriented system. It works in a specific region of the identified problem space, corresponding to the scenario where a service-oriented system architected as a composite service needs to sustain a traffic of requests generated by several users. MOSES integrates within a unified framework different adaptation mechanisms. In this way it achieves greater flexibility in facing various operating environments and the possibly conflicting QoS requirements of several concurrent users. Experimental results obtained with a prototype implementation of MOSES show the effectiveness of the proposed approach.

Qos-driven runtime adaptation of service oriented architectures

Runtime adaptation is recognized as a viable way for a service-oriented system to meet QoS requirements in its volatile operating environment. In this paper we propose a methodology to drive the adaptation of such a system, that integrates within a unified framework different adaptation mechanisms, to achieve a greater flexibility in facing different operating environments and the possibly conflicting QoS requirements of several concurrent users. To determine the most suitable adaptation action(s), the methodology is based on the formulation and solution of a linear programming problem, which is derived from a behavioral model of the system updated at runtime by a monitoring activity. Numerical experiments show the effectiveness of our approach. Besides the methodology, we also present a prototype tool that implements it.

Network Delay Tomography from End-to-End Unicast Measurements

In this paper, we explore the use of end-to-end unicast traffic measurements to estimate the delay characteristics of internal network links. Experiments consist of back-to-back packets sent from a sender to pairs of receivers. Building on recent work [11,5,4], we develop efficient techniques for estimating the link delay distribution. Moreover, we also provide a method to directly estimate the link delay variance, which can be extended to the estimation of higher order cumulants. Accuracy of the proposed techniques depends on strong correlation between the delay seen by the two packets along the shared path. We verify the degree of correlation in packet pairs through network measurements. We also use simulation to explore the performance of the estimator in practice and observe good accuracy of the inference techniques.

Network tomography from measured end-to-end delay covariance

End-to-end measurement is a common tool for network performance diagnosis, primarily because it can reflect user experience and typically requires minimal support from intervening network elements. However, pinpointing the site of performance degradation from end-to-end measurements is a challenging problem. We show how end-to-end delay measurements of multicast traffic can be used to infer the under-lying logical multicast tree and the packet delay variance on each of its links. The method does not depend on cooperation from intervening network elements; multicast probing is bandwidth efficient. We establish desirable statistical properties of the estimator, namely consistency and asymptotic normality. We evaluate the approach through simulations, and analyze its failure modes and their probabilities.

Network loss tomography using striped unicast probes

In this paper, we explore the use of end-to-end unicast traffic as measurement probes to infer link-level loss rates. We leverage off of earlier work that produced efficient estimates for link-level loss rates based on end-to-end multicast traffic measurements. We design experiments based on the notion of transmitting stripes of packets (with no delay between transmission of successive packets within a stripe) to two or more receivers. The purpose of these stripes is to ensure that the correlation in receiver observations matches as closely as possible what would have been observed if a multicast probe followed the same path to the receivers. Measurements provide good evidence that a packet pair to distinct receivers introduces considerable correlation which can be further increased by simply considering longer stripes. Using an M/M/1/K model for a link, we theoretically confirm this benefit for stripes. We also use simulation to explore how well these stripes translate into accurate link-level loss estimates. We observe good accuracy with packet pairs, with a typical error of about 1%, which significantly decreases as stripe length is increased

Scalable fluid models and simulations for large-scale IP networks

In this article we present a scalable model of a network of Active Queue Management (AQM) routers serving a large population of Transport Control Protocol (TCP) flows. We present efficient solution techniques that allow one to obtain the transient behavior of the average queue lengths and packet loss/mark probabilities of AQM routers, and average end-to-end throughput and latencies of TCP users. We model different versions of TCP as well as different implementations of RED Random Early Detection (RED), the most popular AQM scheme currently in use. Comparisons between the models and ns simulation show our models to be quite accurate while at the same time requiring substantially less time to solve than packet level simulations, especially when workloads and bandwidths are high.