Worst-case Congestion Research Articles

Ordinary and Prophet Planning Under Uncertainty in Bernoulli Congestion Games

Uncertain Demand in Transportation Reduces Worst-Case Congestion In “Ordinary and Prophet Planning Under Uncertainty in Bernoulli Congestion Games,” Cominetti, Scarsini, Schröder, and Stier-Moses characterize the inefficiency introduced by self-minded travelers in congested networks, such as those in big cities. When the likelihood that travelers are present in a congested network is smaller, the uncertainty affects the resulting conditions but also what a social planner would do. Contrary to what might be expected, the consequence is a reduction of the worst-case expressions for the inefficiency of the prevailing traffic conditions with respect to the routes of a social planner. This reduction holds under two possible informational assumptions for the social planner. An ordinary planner only has statistical information based on past data in terms of the expected number of travelers, whereas a prophet planner is informed of the participation outcomes right when travelers start their trips.

Preventive Start-Time Optimization to Determine Link Weights Against Probabilistic Link Failures

This article proposes a network design model to minimize the worst-case network congestion against multiple link failures, where open shortest path first link weights are determined at the beginning of network operation. In the proposed model, which is called the preventive start-time optimization model against multiple link failures (PSO-M), the number of multiple link failure patterns to support is restricted by introducing a probabilistic constraint called probabilistic guarantee . If the total probability of non-connected failure patterns does not exceed a specified probability, PSO-M provides a feasible solution of link weights. Otherwise, no feasible solution can be obtained. We introduce an extended model of PSO-M, called PSO-M with link reinforcement (PSO-MLR), where links are reinforced under a budget constraint. Link reinforcement in PSO-MLR has two purposes: maintaining network connectivity and reducing the worst-case congestion ratio. Numerical results show that PSO-M offers lower worst-case congestion ratios than the start-time optimization model, where link weights are obtained against the non-failure pattern assuming that multiple link failures are possible. The superiority of PSO-M strengthens as the average node degree of the network increases. Given a fixed budget, PSO-MLR allows the worst-case congestion ratio to be varied within a specific range. PSO-MLR can support a part of non-connected failure patterns to determine link weights, and so is a valuable enhancement of PSO-M.

Preventive start‐time optimisation of open shortest path first link weights for hose model

Optimising link weights in an open shortest path first network is a challenging traffic engineering problem to reduce network congestion. Most of the previous studies have focused on the application of start-time optimisation (SO) and run-time optimisation on both pipe and hose models of link weight optimisation. In a more recent study, an efficient policy, preventive start-time optimisation (PSO), has been introduced for link weight optimisation. However, no studies have been reported on the application of PSO to the hose model where the exact traffic demand between each source and destination node pair does not need to be specified. A PSO policy for the hose model to optimise the link weights against link failures is proposed. The proposed scheme employs a heuristic algorithm to determine a suitable set of link weights to reduce worst-case congestion for any single link failure. It efficiently selects the worst-case performance traffic matrix and reduces the worst-case congestion ratio as compared with a brute-force scheme which is computationally expensive when searching the link weight space against all the possible traffic matrices and topologies created by single link failures. The numerical results show that the proposed scheme is more effective in the reduction of worst-case congestion ratio than the scheme utilising SO.

The robust joint solution for channel assignment and routing for wireless mesh networks with time partitioning

Joint channel assignment and routing is an essential yet challenging issue for multi-radio multi-channel wireless mesh networks. Though several works are presented in the existing literature to approach this problem, the key question – how to ensure that the resulting network performance can closely track the optimal solution under high traffic variability without incurring too much overhead, remains unanswered.In this work, we present a new solution called “Robust joint Channel Assignment and Routing with Time partitioning (RCART)” for WMNs. RCART consists of three steps: (1) Time Partitioning and Traffic Characterization, which accomplishes the goal of partitioning time into periodic intervals with consistent properties which can be routed efficiently, (2) Robust Routing, which finds a robust routing scheme that provides an upper bound on the worst-case network performance for traffic demands that fall into a convex region, (3) Channel Assignment, which allocates radios to fixed channels during the time interval identified in step 1 and based on the knowledge of traffic distribution from step 2, using the worst-case congestion ratio as a robustness metric in its objective. Introducing time partitions as an additional control variable in the robust mesh routing RCART solution significantly improves average-case performance. Performance evaluation is conducted for RCART using real traffic demand traces. The results show that our RCART solution significantly outperforms the existing works without time partitioning or with simpler traffic profile models.

Optimization of OSPF Link Weights to Counter Network Failure

A key traffic engineering problem in the Open Shortest Path First (OSPF)-based network is the determination of optimal link weights. From the network operators' point of view, there are two approaches to determining a set of link weights: Start-time Optimization (SO) and Run-time Optimization (RO). We previously presented a Preventive Start-time Optimization (PSO) scheme that determines an appropriate set of link weights at start time. It can counter both unexpected network congestion and network instability and thus overcomes the drawbacks of SO and RO, respectively. The previous work adopts a preventive start-time optimization algorithm with limited candidates, named PSO-L (PSO for Limited candidates). Although PSO-L relaxes the worst-case congestion, it does not confirm the optimal worst-case performance. To pursue this optimality, this paper proposes a preventive start-time optimization algorithm with a wide range of candidates, named PSO-W (PSO for Wide-range candidates). PSO-W upgrades the objective function of SO that determines the set of link weights at start time by considering all possible single link failures; its goal is to minimize the worst-case congestion. Numerical results via simulations show that PSO-W effectively relaxes the worst-case network congestion compared to SO, while it avoids the network instability caused by the run-time changes of link weights caused by RO. At the same time, PSO-W yields performance superior to that of PSO-L.

A Control Theory Approach for Congestion Control in Intranetwork

This paper presents a congestion control technique in an intranetwork gateway by using a control theory approach. Such an approach is very much necessary to determine how essentially information regarding congestion can be sent to the hosts as a precautionary measure in time. As a result those hosts connected in the intra net will take necessary steps to avoid further worst case congestion in the gateway of the network. Our proposed scheme can be viewed as a two tier model, where admittance of various connections is done according to the traffic parameter declaration by the hosts in the first step and once accepted, periodic signals can be sent to the hosts to regulate their flow of traffic in accordance to the declared parameters. The main idea in our approach is to observe network states at different point of time at the gateway before sending any information to the hosts inside the network. In order to observe these states at the gateway, two important parameters such as process estimation and process parameters need to be calculated and to get them correctly, we propose to use Kalman filter in our approach. Kalman filter is derived from discrete time space analysis and is different from other filters in use, which estimates a process by using a form of feed back control. We believe, such an approach can be widely used in today’s Internet technology making use of Resource Reservation Protocol (RSVP) and other signalling protocols to achieve a desired Quality of Service (QoS) amongst a variety of users.