Jointly Optimal Routing and Caching for Arbitrary Network Topologies

We study a problem of fundamental importance to ICNs, namely, minimizing routing costs by jointly optimizing caching and routing decisions over an arbitrary network topology. We consider both source routing and hop-by-hop routing settings. The respective offline problems are NP-hard. Nevertheless, we show that there exist polynomial time approximation algorithms producing solutions within a constant approximation from the optimal. We also produce distributed, adaptive algorithms with the same approximation guarantees. We simulate our adaptive algorithms over a broad array of different topologies. Our algorithms reduce routing costs by several orders of magnitude compared to prior art, including algorithms optimizing caching under fixed routing.Comment: This is the extended version of the paper "Jointly Optimal Routing and Caching for Arbitrary Network Topologies", appearing in the 4th ACM Conference on Information-Centric Networking (ICN 2017), Berlin, Sep. 26-28, 201

Operations Research Games: A Survey

This paper surveys the research area of cooperative games associated with several types of operations research problems in which various decision makers (players) are involved.Cooperating players not only face a joint optimisation problem in trying, e.g., to minimise total joint costs, but also face an additional allocation problem in how to distribute these joint costs back to the individual players.This interplay between optimisation and allocation is the main subject of the area of operations research games.It is surveyed on the basis of a distinction between the nature of the underlying optimisation problem: connection, routing, scheduling, production and inventory.cooperative games;operational research

Quality-of-service provisioning in high speed networks : routing perspectives

The continuous growth in both commercial and public network traffic with various quality-of-service (QoS) requirements is calling for better service than the current Internet\u27s best effort mechanism. One of the challenging issues is to select feasible paths that satisfy the different requirements of various applications. This problem is known as QoS routing. In general, two issues are related to QoS routing: state distribution and routing strategy. Routing strategy is used to find a feasible path that meets the QoS requirements. State distribution addresses the issue of exchanging the state information throughout the network, and can be further divided into two sub-problems: when to update and how to disseminate the state information. In this dissertation, the issue of when to update link state information from the perspective of information theory is addressed. Based on the rate-distortion analysis, an efficient scheme, which outperforms the state of the art in terms of both protocol overhead and accuracy of link state information, is presented. Second, a reliable scheme is proposed so that, when a link is broken, link state information is still reachable to all network nodes as long as the network is connected. Meanwhile, the protocol overhead is low enough to be implemented in real networks. Third, QoS routing is NP-complete. Hence, tackling this problem requires heuristics. A common approach is to convert this problem into a shortest path or k-shortest path problem and solve it by using existing algorithms such as Bellman-Ford and Dijkstra algorithms. However, this approach suffers from either high computational complexity or low success ratio in finding the feasible paths. Hence, a new problem, All Hops k-shortest Path (AHKP), is introduced and investigated. Based on the solution to AHKP, an efficient self-adaptive routing algorithm is presented, which can guarantee in finding feasible paths with fairly low average computational complexity. One of its most distinguished properties is its progressive property, which is very useful in practice: it can self-adaptively minimize its computational complexity without sacrificing its performance. In addition, routing without considering the staleness of link state information may generate a significant percentage of false routing. Our proposed routing algorithm is capable of minimizing the impact of stale link state information without stochastic link state knowledge. Fourth, the computational complexities of existing s-approximation algorithms are linearly proportional to the adopted linear scaling factors. Therefore, two efficient algorithms are proposed for finding the optimal (the smallest) linear scaling factor such that the computational complexities are reduced. Finally, an efficient algorithm is proposed for finding the least hop(s) multiple additive constrained path for the purpose of saving network resources

Efficient Route Planning with Temporary Driving Bans, Road Closures, and Rated Parking Areas

We study the problem of planning routes in road networks when certain streets or areas are closed at certain times. For heavy vehicles, such areas may be very large since many European countries impose temporary driving bans during the night or on weekends. In this setting, feasible routes may require waiting at parking areas, and several feasible routes with different trade-offs between waiting and driving detours around closed areas may exist. We propose a novel model in which driving and waiting are assigned abstract costs, and waiting costs are location-dependent to reflect the different quality of the parking areas. Our goal is to find Pareto-optimal routes with regards to arrival time at the destination and total cost. We investigate the complexity of the model and determine a necessary constraint on the cost parameters such that the problem is solvable in polynomial time. We present a thoroughly engineered implementation and perform experiments on a production-grade real world data set. The experiments show that our implementation can answer realistic queries in around a second or less which makes it feasible for practical application