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With the rapid growth of internet traffic, the traditional host-to-host TCP/IP architecture is subject to many service limitations faced with content-oriented applications. Various novel network architectures have been proposed to solve these limitations, among which Information-Centric Networking (ICN) is one of the most prominent. ICN features the decoupling of content (service) from the physical devices storing (providing) it through location-independent naming, and offers inherent enhancement to network performance, such as multicast and in-network caching. ICN in-network caching has been extensively studied, and we believe that it may also be the main incentive for ISPs to deploy ICN. A CDN (content delivery network) is a typical content-oriented network paradigm that aims to provide the fast delivery of content. In this paper, we leverage the advantages of the in-network caching of ICN to enhance the content delivery efficiency of CDN by integrating ICN as a service. First, we present our design of a content delivery network enhanced with ICN, called IECDN. Additionally, we formulate a mathematical model to optimize the performance of our proposed design and conduct a series of evaluations. The results indicate that our proposed design provides significant performance gains while reducing bandwidth consumption and shows better resilience to traffic surge. [ABSTRACT FROM AUTHOR]

In recent years, Internet traffic has increased due to its widespread use. This can be attributed to the growth of social games on smartphones and video distribution services with increasingly high image quality. In these situations, a routing mechanism is required to control congestion, but most existing routing protocols select a single optimal path. This causes the load to be concentrated on certain links, increasing the risk of congestion. In addition to the optimal path, the network has redundant paths leading to the destination node. In this study, we propose a multipath control with multi-commodity flow problem. Comparing the proposed method with OSPF, which is single-path control, and OSPF-ECMP, which is multipath control, we confirmed that the proposed method records higher packet arrival rates. This is expected to reduce congestion.

In recent years, Internet traffic has increased due to its widespread use. This can be attributed to the growth of social games on smartphones and video distribution services with increasingly high image quality. In these situations, a routing mechanism is required to control congestion, but most existing routing protocols select a single optimal path. This causes the load to be concentrated on certain links, increasing the risk of congestion. In addition to the optimal path, the network has redundant paths leading to the destination node. In this study, we propose a multipath control with multi-commodity flow problem. Comparing the proposed method with OSPF, which is single-path control, and OSPF-ECMP, which is multipath control, we confirmed that the proposed method records higher packet arrival rates. This is expected to reduce congestion. [ABSTRACT FROM AUTHOR]

With the rapid growth of internet traffic, the traditional host-to-host TCP/IP architecture is subject to many service limitations faced with content-oriented applications. Various novel network architectures have been proposed to solve these limitations, among which Information-Centric Networking (ICN) is one of the most prominent. ICN features the decoupling of content (service) from the physical devices storing (providing) it through location-independent naming, and offers inherent enhancement to network performance, such as multicast and in-network caching. ICN in-network caching has been extensively studied, and we believe that it may also be the main incentive for ISPs to deploy ICN. A CDN (content delivery network) is a typical content-oriented network paradigm that aims to provide the fast delivery of content. In this paper, we leverage the advantages of the in-network caching of ICN to enhance the content delivery efficiency of CDN by integrating ICN as a service. First, we present our design of a content delivery network enhanced with ICN, called IECDN. Additionally, we formulate a mathematical model to optimize the performance of our proposed design and conduct a series of evaluations. The results indicate that our proposed design provides significant performance gains while reducing bandwidth consumption and shows better resilience to traffic surge.

This paper proposes a complete network capacity analysis framework for low-earth-orbit (LEO) mega-constellations, where a network capacity estimation problem considering the link packet loss rate is formulated with the support of a time-variant network topology model and a task distribution model. This problem is solved in two steps. First, without considering the link packet loss rate, an improved fully polynomial-time approximation (IFPTA) algorithm is proposed to provide a sub-optimal solution to the multi-commodity flow (MCF) problem, in which a simpler definition of a commodity is given and proved to be equivalent to the original problem. Second, a Jackson-network-based capacity fallback approach is proposed to control the link packet loss rate below a given threshold. Numerical results illustrate the superiority of the proposed IFPTA algorithm in terms of accuracy and time complexity compared to existing solutions. In addition, the capacity characteristics of mega-constellations are analyzed by utilizing the proposed capacity analysis framework, including the relationship between constellation size and capacity, network capacity bottlenecks, and the influence of task distributions.

A steadily increasing number of users consume videos over the Internet. In current video platforms, players run a control algorithm that dynamically chooses the video bitrate to match the time-varying network bandwidth. Such an algorithm strives to improve the quality individually perceived by users. Consequently, this control architecture leads, in the optimal case, to maximize the average quality perceived collectively by all users rather than to a quality-fair distribution of resources, possibly leading to user abandonment for those users receiving a lower quality. Therefore, we argue that well-designed video delivery networks should gracefully degrade the perceived quality equally for all users when resources become scarce. In this paper, we propose the Multi-Commodity Flow Problem (MCFP) optimization framework to address the issue of designing a QoE-fair optimal allocation strategy. We show how to make the resulting problem tractable for video platforms serving massive audiences. The performance of the proposed optimal fair resource allocation strategy is tested through realistic simulations involving thousands of concurrent users on two real networks by varying both the total load on the network and the system parameters. [ABSTRACT FROM AUTHOR]

This paper deals with multi-depot bus scheduling (MDBS) problem. Depot workload balancing constraints are introduced. In this case, the problem can be stated as a two-objective multi-commodity flow problem with soft constraints. Two state-of-the-art heuristics are developed including schedule-based and cluster-based heuristics, both of them extend auction algorithm. Also three different approaches are proposed to satisfy depot workload balancing constraints. The convergence of each algorithm is investigated and its complexity obtained. To illustrate the main concepts and results, a small example is solved. Also, to demonstrate the performance of the proposed algorithms, some benchmark examples are considered and CPU-time and optimality gap are compared. The results show a great improvement in the CPU time and quality of the solution of the proposed algorithms. Also the extension of the proposed algorithms under ε-scaling approach is analysed. [ABSTRACT FROM AUTHOR]

In a multihop wireless network,wireless interference is crucial to the multi-commodity flow problem,which studies the maximum throughput between multiple pairs of sources and sinks.Based on the observation that network coding (NC) could help to decrease the impacts of wireless interference,a framework was proposed to solve the problem for multihop wireless networks with NC.By introducing hyperarcs to model all possible (uncoded or encoded) transmissions and using the conflict graph of hyperarcs to describe the new conflict relations modified by NC (e.g.,in the protocol interference model),the problem was formulated to compute the maximum throughput of multiple unicast flows supported by the multihop wireless network with given NC settings,in which the constraints were rebuilt from the conflict graph of hyperarcs.Furthermore,a practical algorithm was proposed to collect maximal independent sets,instead of collecting all maximal independent sets in the conflict graph of hyperarcs (which is NP-hard),and some numerical results were illustrated.

Abstract: This paper deals with multi-commodity flow problem with fractional objective function. The optimality conditions and the duality concepts of this problem are given. For this aim, the fractional linear programming formulation of this problem is considered and the weak duality, the strong direct duality and the weak complementary slackness theorems are proved applying the traditional duality theory of linear programming problems which is different from same results in Chadha and Chadha (2007) [1]. In addition, a strong (strict) complementary slackness theorem is derived which is firstly presented based on the best of our knowledge. These theorems are transformed in order to find the new reduced costs for fractional multi-commodity flow problem. These parameters can be used to construct some algorithms for considered multi-commodity flow problem in a direct manner. Throughout the paper, the boundedness of the primal feasible set is reduced to a weaker assumption about solvability of primal problem which is another contribution of this paper. Finally, a real world application of the fractional multi-commodity flow problem is presented. [Copyright &y& Elsevier]

The Gate Allocation Problem (GAP) is one of the numerous operational problems that all busy airports have to handle and to optimize every day. In this article, we focus on two important objectives for the GAP: the robustness and the taxiing times. We first analyze a basic model which optimizes the robustness of the solution to the GAP and propose a more realistic and compact Multi-commodity Flow Problem model (MFP) to optimize both the robustness and the taxiing times. Based on tests with real data at Paris-Charles-de-Gaulle airport, this new efficient MFP model outperforms the basic one by orders of magnitude. • New Multi-commodity Flow model optimally solves large-scale Gate Allocation Problem. • Schedule robustness and taxiing optimized to improve airport operations. • Outperforms former model by orders of magnitude on real data at major airport. • Fast enough to enable real-time applications. [ABSTRACT FROM AUTHOR]