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.
Abstract-We explore the use of end-to-end multicast traffic as measurement probes to infer network-internal characteristics. We have developed in an earlier paper [2] a Maximum Likelihood Estimator for packet loss rates on individual links based on losses observed by multicast receivers. This technique exploits the inherent correlation between such observations to infer the performance of paths between branch points in the multicast tree spanning the probe source and its receivers. We evaluate through analysis and simulation the accuracy of our estimator under a variety of network conditions. In particular, we report on the error between inferred loss rates and actual loss rates as we vary the network topology, propagation delay, packet drop policy, background traffic mix, and probe traffic type. In all but one case, estimated losses and probe losses agree to within 2 percent on average. We feel this accuracy is enough to reliably identify congested links in a wide-area internetwork.
Wc prcscnt a novel methodology for identifying intcrnal iichvork performance characteristics baucd on cnd-to-end mullicnst measuretncnts. The mcthodology, solidly groiinded on statistical cstiination thcory, c a n bc uscd to clifiracterize the internal loss atid dciay lrrcliavior of a nctwork. Measurcnicnts on thc MI3anc havc been uscd tu validate thc approach in the case of Insscs. Extensive simulation experitncnts providc furihcr validaiion of the apprnacli, not only for losses, but also for delays. Wc also dcscribe our stratcgy for deploying the methodology on thc Intcrnct. This includcs the continued developmciit of the Naliorinl Internet Measurement hCrastriicture to support RTP-hascd cnd-to-cntl multicast ineasurcmciit~ sild the dcvclopnicnt of software tools tu analyze the traces. Once complete, this combincd softwarc/hardware inlrastriicturc will providc B scrvicc for understanding and forccasting tlic pcrformancc of the Internet.
Abstract-The sizes of the BGP routing tables have increased by an order of magnitude over the last six years. This dramatic growth of the routing table can decrease the packet forwarding speed and demand more router memory space. In this paper, we explore the extent that various factors contribute to the routing table size and characterize the growth of each contribution. We begin with measurement study using routing tables of Oregon route views server to determine the contributions of multi-homing, load balancing, address fragmentation, and failure to aggregate to routing table size. We find that the contribution of address fragmentation is the greatest and is three times to that of multihoming or load balancing. The contribution of failure to aggregate is the least. Although multihoming and load balancing contribute less to routing table size than address fragmentation does, we observe that the contribution of multihoming and that of load balancing grow faster than the routing table does and that the load balancing has surpassed multihoming becoming the fastest growing contributor. Moreover, we find that both load balancing and multihoming contribute to routing table growth by introducing more prefixes of length greater than 17 but less than 25, which is the fastest growing prefixes. Next, we compare the growth of the routing table to the expanding of IP addresses that can be routed and conclude that the growth of routable IP addresses is much slower than that of routing table size. Last, we demonstrate that our findings based on the view derived from the Oregon server are accurate through the evaluation using additional 15 routing tables collected from different locations in the Internet.
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