Graphs are commonly used to model the topological structure of internetworks, to study problems ranging from routing to resource reservation. A variety of graphs are found in the literature, including fixed topologies such as rings or stars, "well-known" topologies such as the ARPAnet, and randomly generated topologies. While many researchers rely upon graphs for analytic and simulation studies, there has been little analysis of the implications of using a particular model, or how the graph generation method may a ect the results of such studies. Further, the selection of one generation method over another is often arbitrary, since the differences and similarities between methods are not well understood. This paper considers the problem of generating and selecting graph models that reflect the properties of real internetworks. We review generation methods in common use, and also propose several new methods. We consider a set of metrics that characterize the graphs produced by a method, and we quantify similarities and differences amongst several generation methods with respect to these metrics. We also consider the effect of the graph model in the context of a speciffic problem, namely multicast routing.

Introduction A natural and well-studied problem in algorithmic graph theory and network optimization is that of computing a "shortest path" between two nodes, s and t, in a graph whose edges have "weights" associated with them, and we consider the "length" of a path to be the sum of the weights of the edges that comprise it. Efficient algorithms are well known for this problem, as briefly summarized below. The shortest path problem takes on a new dimension when considered in a geometric domain. In contrast to graphs, where the encoding of edges is explicit, a geometric instance of a shortest path problem is usually specified by giving geometric objects that implicitly encode the graph and its edge weights. Our goal in devising efficient geometric algorithms is generally to avoid explicit construction of the entire underlying graph, since the full induced graph may be very large (even exponential in the input size, or infinite). Computing an optimal

We deal with the competitive analysis of algorithms for managing data in a distributed environment. We deal with the file allocation problem ([DF], [ML]), where copies of a file may be be stored in the local storage of some subset of processors. Copies may be replicated and discarded over time so as to optimize communication costs, but multiple copies must be kept consistent and at least one copy must be stored somewhere in the network at all times. We deal with competitive algorithms for minimizing communication costs, over arbitrary sequences of reads and writes, and arbitrary network topologies. We define the constrained file allocation problem to be the solution of many individual file allocation problems simultaneously, subject to the constraints of local memory size. We give competitive algorithms for this problem on the uniform network topology. We then introduce distributed competitive algorithms for on-line data tracking (a generalization of mobile user tracking [AP1...

This paper deals with the file allocation problem [BFR92] concerning the dynamic optimization of communication costs to access data in a distributed environment. We develop a dynamic file re-allocation strategy that adapts on-line to a sequence of read and write requests whose location and relative frequencies are completely unpredictable. This is achieved by replicating the file in response to read requests and migrating the file in response to write requests while paying the associated communications costs, so as to be closer to processors that access it frequently. We develop first explicit deterministic on-line strategy assuming existence of global information about the state of the network; previous (deterministic) solutions were complicated and more expensive. Our solution has (optimal) logarithmic competitive ratio. The paper also contains the first explicit deterministic data migration [BS89] algorithm achieving the best known competitive ratio for this problem. Using somewhat ...

In this paper, we present, QoSMIC, a multicast protocol for the Internet that supports QoS-sensitive routing, and minimizes the importance of a priori configuration decisions (such ascore selection). The protocol is resource-efficient, robust, exible, and scalable. In addition, our protocol is provably loop-free. Our protocol starts with a resources-saving tree (Shared Tree) and individual receivers switch to a QoS-competitive tree (Source-Based Tree) when necessary. In both trees, the new destination is able to choose the most promising among several paths. An innovation is that we use dynamic routing information without relying on a link state exchange protocol to provide it. Our protocol limits the effect of preconfiguration decisions drastically, by separating the management from the data transfer functions; administrative routers are not necessarily part of the tree. This separation increases the robustness, and flexibility of the protocol. Furthermore, QoSMIC is able to adapt dynamically to the conditions of the network. The QoSMIC protocol introduces several new ideas that make it more exible than other protocols proposed to date. In fact, many of the other protocols, (such asYAM, PIM-SM, BGMP, CBT) can be seen as special cases of QoSMIC. This paper presents the motivation behind, and the design of QoSMIC, and provides both analytical and experimental results to support our claims.

Multicast services have been increasingly used in large scale continuous media applications. The quality-of-service (QoS) requirements of these continuous media applications prompt the necessity for QoS-driven, constraint-based multicast routing. This article provides a comprehensive overview of existing multicast routing algorithms, protocols, and their QoS extension. In particular, we classify multicast routing problems according to their optimization functions and performance constraints, present basic routing algorithms in each problem class, and discuss their strengths and weakness. We also categorize existing multicast routing protocols, outline the issues and challenges in providing QoS in multicast routing, and point out possible future research directions.

We consider the problem of establishing minimum-cost multicast connections over coded packet networks, i.e., packet networks where the contents of outgoing packets are arbitrary, causal functions of the contents of received packets. We consider both wireline and wireless packet networks as well as both static multicast (where membership of the multicast group remains constant for the duration of the connection) and dynamic multicast (where membership of the multicast group changes in time, with nodes joining and leaving the group). For static multicast, we reduce the problem to a polynomial-time solvable optimization problem, ... and we present decentralized algorithms for solving it. These algorithms, when coupled with existing decentralized schemes for constructing network codes, yield a fully decentralized approach for achieving minimum-cost multicast. By contrast, establishing minimum-cost static multicast connections over routed packet networks is a very difficult problem even using centralized computation, except in the special cases of unicast and broadcast connections. For dynamic multicast, we reduce the problem to a dynamic programming problem and apply the theory of dynamic programming to suggest how it may be solved.

In this paper, we formulate and provide optimal solutions for a broad class of problems in which a decision-maker is required to select from among numerous competing options. The goal of the decision-maker is to select the option that will have the best future performance. This task is made difficult by the constraint that the decision-maker has no way to predict the future performance of any of the options. Somewhat surprisingly, we find that the decision-maker can still (at least in several important scenarios) pick a winner with high probability. Our result has several applications. For example, consider the problem of scheduling background jobs on a network of workstations (NOW) when very little is known about the future speed or availability of each workstation. In this problem, the goal is to schedule each job on a workstation which will have enough idle capacity to complete the job within a reasonable or ...

In this paper, we propose and evaluate ARIES, a heuristic for updating multicast trees dynamically in large point-to-point networks. The algorithm is based on monitoring the accumulated damage to the multicast tree within local regions of the tree as nodes are added and deleted, and triggering a rearrangement when the number of changes within a connected subtree crosses a set threshold. We derive an analytical upper-bound on the competitiveness of the algorithm. We also present simulation results to compare the average-case performance of the algorithm with two other known algorithms for the dynamic multicast problem, GREEDY and EBA (Edge-Bounded Algorithm). Our results show that ARIES provides the best balance among competitiveness, computational effort, and changes in the multicast tree after each update. Keywords: multicast algorithms, on-line Steiner problem, rearrangeable multicast algorithms. 1 Introduction Many future applications of computer networks such as distance educati...

Abstract We present the Cost-Distance problem: finding a Steiner tree which optimizes the sum of edge costs along one metric and the sum of source-sink distances along an unrelated second metric. We give the first known O(log k) randomized approximation scheme for Cost-Distance, where k is the number of sources. We reduce many common network design problems to CostDistance, obtaining (in some cases) the first known logarithmic approximation for them. These problems include single-sink buy-at-bulk with variable pipe types between different sets of nodes, facility location with buy-at-bulk type costs on edges, and maybecast with combind cost and distance metrics. Our algorithm is also the algorithm of choice for several previous network design problems, due to its ease of implementation and fast running time. 1 Introduction Consider designing a network from the ground up. We are given a set of customers, and need to place various servers and network links in order to cheaply provide sufficient service. If we only need to place the servers, this becomes the facility location problem and constant-approximations are known. If a single server handles all customers, and we impose the additional constraint that the set of available network link types is the same for every pair of nodes (subject to constant scaling factors on cost) then this is the single sink buy-at-bulk problem. We give the first known approximation for the general version of this problem with both servers and network links. We reduce the network design problem to an elegant theoretical framework: the Cost-Distance problem. We are given a graph with a single distinguished sink node (server). Every edge in this graph can be measured along two metrics; the first will be called cost and the second will be length. Note that the two metrics are entirely independent, and that there may be any number of parallel edges in the graph. We are given a set of sources (customers). Our objective is to construct a Steiner tree connecting the sources to the sink while minimizing the combined sum of the cost of the edges in the tree and sum over sources of the weighted length from source to sink.