Routing Policies Research Articles

Capacity Optimization in Dynamically Routing Computer Network Systems

A computer network system is a complex system with a great number of dynamic components. There are many devices in the system, such as computers, routers, lines, hubs, and switches. In addition to these hardware systems, many protocols are integrated to set the rules and provide the way of communication. Due to the nature of the system, it is hard to formulate and solve problems analytically without making any assumptions. One of the prominent problems that occur in computer systems is the line capacity assignment problem. In the previous mathematical models, message routes were predetermined and the dynamic nature of the system was neglected. This study deals with the line capacity assignment problem under a dynamically routing policy. Four different computer network topologies are used and solved by two heuristic algorithms via simulation. A dynamic search approach based on the occupancy rate of lines is used to define the consecutive routes of messages. The performances of harmony search and genetic algorithms via simulation are compared with the results of OptQuest, one of the optimization packet programs embedded in simulation software Arena®.

Genetic Programming With Niching for Uncertain Capacitated Arc Routing Problem

The uncertain capacitated arc routing problem is an important optimization problem with many real-world applications. Genetic programming is considered a promising hyper-heuristic technique to automatically evolve routing policies that can make effective real-time decisions in an uncertain environment. Most existing research on genetic programming hyper-heuristic for the uncertain capacitated arc routing problem only focused on the test performance aspect. As a result, the routing policies evolved by genetic programming are usually too large and complex, and hard to comprehend. To evolve effective, smaller, and simpler routing policies, this article proposes a novel genetic programming approach, which simplifies the routing policies during the evolutionary process using a niching technique. The simplified routing policies are stored in an external archive. We also developed new elitism, parent selection, and breeding schemes for generating offspring from the original population and the archive. The experimental results show that the newly proposed approach can achieve significantly better test performance than the current state-of-the-art genetic programming algorithms for the uncertain capacitated arc routing problem. The evolved routing policies are smaller, and thus potentially more interpretable.

An Approach to Combine the Power of Deep Reinforcement Learning with a Graph Neural Network for Routing Optimization

Routing optimization has long been a problem in the networking field. With the rapid development of user applications, network traffic is continuously increasing in dynamicity, making optimization of the routing problem NP-hard. Traditional routing algorithms cannot ensure both accuracy and efficiency. Deep reinforcement learning (DRL) has recently shown great potential in solving networking problems. However, existing DRL-based routing solutions cannot process the graph-like information in the network topology and do not generalize well when the topology changes. In this paper, we propose AutoGNN, which combines a GNN and DRL for the automatic generation of routing policies. In AutoGNN, the traffic distribution in the network topology is processed by a GNN, while a DRL framework is used to train the parameters of neural networks without human expertise. Our experimental results show that AutoGNN can improve the average end-to-end delay of the network by up to 19.7% as well as present more robustness against topology changes.

Whose express access? Assessing the equity implications of bus express routes in Montreal, Canada

Express buses—characterized by limited stops and sometimes higher frequencies or priority traffic measures—offer a cost-effective and efficient way to boost service convenience and reliability for riders. This paper assesses how the accessibility benefits of express bus route policy are distributed in Montreal, Canada, while providing a pathway for public transportation agencies to assess their policies and plans. To isolate the impact of bus express routes, we use General Transit Speed Specification (GTFS) data, the Open Trip Planner multimodal routing engine, and the 2013 edition of Montreal’s origin-destination survey to contrast travel time and accessibility at the trip and census-tract levels under two scenarios: one with the existing, complete network and the second a counterfactual scenario with no express bus routes. Our results indicate that bus express routes enable an overall increase in accessibility for the overall population. However, the accessibility benefits do not accrue evenly, as expected, but also tend to benefit a more significant number of higher incomes. This occurs despite the location of low-income populations in some outlying areas of the city, which express bus routes are supposed to serve. This paper closes with policy recommendations that help planners balance economic, environmental, and equity goals, perhaps one of the most complex challenges they face nowadays.

Deep Reinforcement Learning-Based Policy for Baseband Function Placement and Routing of RAN in 5G and Beyond

In this paper, we propose a deep reinforcement learning (DRL)-based algorithm to generate policies of Baseband Function (BBF) placement and routing. In order to explore the performance of the proposed algorithm in practical systems, the online scenario with the completely random requests is used in the simulation considering C-RAN and NG-RAN architectures. Besides, an Integer Linear Programming (ILP) model is formulated to generate the optimal solution as the benchmark. The simulation results show that DRL-based algorithm converges in a short time, and its performance closes to the optimal benchmark obtained by ILP in terms of latency and bandwidth for the online scenarios. In addition, the performance of the generated policies based on DRL is compared with a classic heuristic algorithm, i.e., first-fit algorithm. The performance of DRL-based algorithm is superior to the first-fit algorithm from above two perspectives. The fast convergence and the near-optimal performance prove that the DRL-based algorithm is a promising approach for the BBF placement and routing of RAN in 5G and Beyond.

A Markov decision process model on dynamic routing for target surveillance

We consider the problem of dynamically routing a fleet of vehicles tasked with surveilling a set of targets over a collection of waypoints. As motivated by threat detection, the fleet must accrue sufficient coverage on a set of fixed targets while being able to accommodate time-sensitive targets (TSTs) that may become active at a random time during the planning horizon by dynamically adjusting their routes. We propose a Markov decision process model and online routing decision policies based on direct look-ahead strategies via two-stage stochastic programming. We implement the proposed stochastic programming-based look-ahead strategies within a rolling-horizon procedure. Numerical experiment results on a set of benchmark instances show the effectiveness of the proposed approach compared to static routing policies.

A comprehensive study of different objectives and solutions of controller placement problem in software‐defined networks

AbstractSoftware‐defined network (SDN) is a programmable networking paradigm that enables logically centralized network management. In order to overcome the shortcomings of traditional networks, SDN has come into the picture. SDN is a recent and fast‐forwarding technique that involves separating the network's control plane from the data plane. This network also allows us to directly communicate with the applications through application programming interfaces. A network administrator can directly modify and update the routing policies and rules in SDN. Apart from the advantages given by the SDN, it has also been found that separation of control plane directly affects the network performance. Finding the optimum number of controllers and their locations to improve the performance is one of the fundamental research problems in SDN which is also commonly known as the controller placement problem (CPP). In this article, we have addressed the CPP in SDN in detail and investigated the optimization objectives that influence CPP. The state‐of‐the‐art of this article is to review almost all the optimization objective along with their mathematical formulation and survey the CPP solutions proposed by the other researchers'. Classified the CPP into three parts based on the objectives and solutions of CPP and reviewed the impact of these objectives on network performance. This article gives a comprehensive overview of current strategies on CPP in SDN and at last, we give some research issues that can be further explored by the researchers.

A Crowdsourcing Renewable Energy Delivery Framework with Dynamic Pricing and Routing Policy

Electric trucks (ETs) possess large-capacity batteries and are widely adopted in express industry. The rapid development of battery technologies and discharging facilities endows ETs with another role as temporary mobile energy storage (MES) to deliver energy and feed energy back to grid at charging stations. Thus, widespread ETs have great potential to relieve load pressure on transmission lines of the grid and assist the grid to allocate energy. By assuming that energy couriers (ECs, couriers with ETs) can be recruited through cloud platform, surplus renewable energy (RE) harvested at renewable energy plants (REPs) can be delivered to charging stations deployed at limited-capacity feeders by couriers who take the energy delivery orders. Existing works focus more on centralized energy delivery scheme based on utility-owned MES and it is not economical and robust due to extra labor cost and the RE generation uncertainty. In this paper, we propose a crowdsourcing RE delivery framework with coordination of express network. To improve the economy of the delivery system, RE delivery is crowdsourced to individual courier. Additional RE delivery for couriers can inflict extra cost (charging and discharging time, detouring, etc.) compared with the original express tasks and couriers are stimulated to participate in the RE delivery by a pricing and routing policy based on contract theory. To deal with the uncertainties and enhance the robustness, the policy is dynamically updated according to real-time observation values of system status utilizing Lyapunov Optimization. Simulation results on California highway dataset demonstrate the long-term effectiveness of the proposed dynamic policy.

A Deep Reinforcement Learning-Based Geographic Packet Routing Optimization

Packet routing is a fundamental problem in wireless networks in which routers decide the next hop for each packet in order to deliver it to its destination as quickly as possible. In order to overcome the shortcoming of non optimal forwarding path caused by fixed forwarding mode in geographic location-based routing algorithm, we investigated a new efficient packet routing strategy which combined with Deep Reinforcement Learning (DRL) algorithm (Proximal Policy Optimization: PPO) to minimize the hops and reduce the probability of encountering “routing hole” during the forwarding of the packets in complex networks. Each node in our network can make routing decisions to learn a routing policy that can not only use the greedy mode to maintain the efficiency of routing policy but also reduce the use of peripheral forwarding mode. The learning process of the DRL agent is based on the status information of the packet being transmitted and information of neighbor nodes within the communication range. Extensive simulations over dynamic network topologies and the number of nodes have shown that our packets routing agent can learn the optimal policy in terms of average packets delivery rate and the number of hops compared with Greedy Perimeter Stateless Routing (GPSR) protocol. The performance of PPO in large-scale action space is also verified, which provides a basis for the future researches to combine PPO technique with packet routing optimization.

A Vehicle Routing Problem with Time Windows and Workload Balancing for COVID-19 Testers: A Case Study

Due to the COVID-19 pandemic, laboratories have faced unprecedented demand for in-home delivery test services. This drastic demand increase requires a rapid reaction from laboratories to manage their testers in order to respond to the high demand volume and avoid unnecessary costs. This study provides an optimization model based on the vehicle routing problem with time windows by considering the testers’ workload balancing to improve laboratories’ assignment and routing policies. A medical lab that has faced this situation for its in-home test services is taken as a real-world case in the current study. A mixed-integer programming model is solved for small instances using the CPLEX solver, and an adaptive large neighborhood search algorithm is implemented for large instances. Ultimately, the obtained solutions are compared to the real-world implementation of the lab on a dataset of six consecutive days, and the results are further discussed.

RLBEEP: Reinforcement-Learning-Based Energy Efficient Control and Routing Protocol for Wireless Sensor Networks

One of the most important topics in the field of wireless sensor networks is the development of approaches to improve network lifetime. In this paper, an energy-efficient control and routing protocol for wireless sensor networks is presented. This algorithm is based on reinforcement learning for energy management in the network. This protocol seeks to optimize routing policies to maximize the long-term reward received by each node, using reinforcement learning, which is a machine learning approach. In order to improve the lifetime of wireless sensor network, three energy management approaches have been proposed. The first approach is to navigate correctly using reinforcement learning to reduce the length of the routes and to improve energy consumption. The second approach is to exploit a sleep scheduling technique to improve node energy consumption. The last approach is used to restrict data transmission of each node based on the received data change rate. Simulation results show that in terms of network lifespan, the proposed method significantly outperforms previous reported methods.

ELITE: An Intelligent Digital Twin-based Hierarchical Routing Scheme for Softwarized Vehicular Networks

Software-Defined Vehicular Network (SDVN) is a networking architecture that can provide centralized control for vehicular networks. However, the design for routing policies in SDVNs is generally influenced by several limitations, such as frequent topological changes, complex service requests, and long model training time. Intelligent Digital Twin-based Software-Defined Vehicular Networks (IDT-SDVN) can overcome these weaknesses and maximize the advantages of the conventional SDVN architecture by enabling the controller to construct virtual network spaces and provide virtual instances of corresponding physical objects within the Digital Twin (DT). In this paper, we propose a junction-based hierarchical routing scheme in IDT-SDVN, namely, intelligent digital twin hierarchical (ELITE) routing. The proposed scheme is conducted in four phases: policy training and generation in the virtual network, and deployment and relay selection in physical networks. First, the policy learning phase employs several parallel agents in DT networks and derives multiple single-target policies. Second, the generation phase combines the learned policies and generates new policies based on complex communication requirements. Third, the deployment phase selects the most suitable generated policy according to the real-time network status and message types. A road path is calculated by the controller based on the selected policy and then sent to the requester vehicle. Finally, the relay selection phase is utilized to determine relay vehicles in a hop-by-hop process along the selected path. Simulation results demonstrate that ELITE achieves substantial improvements in terms of packet delivery ratio, end-to-end delay, and communication overhead compared with its counterparts.

Dez Dam Rehabilitation Project Role in Flood Damage Reduction

As a result of climate change, extreme precipitation events from mid-March to April 2019 in southwest Iran generated catastrophic floods taken 78 lives and left $4.1 billion financial damages. The extent of damages could have been even worse without the existence of upstream flood control dams including Dez dam. Dez Dam is the only large dam in its basin which play a significant role in flood control. A multi-purpose rehabilitation project including new power intakes, flushing tunnels and dam heightening with required investment of $350 million is defined in this dam to compensate reservoir live volume losses through 57 years of sedimentation. In this paper, the effect of dam heightening and the flushing tunnels will be evaluated by their roles in flood damage reduction. Assuming that the actual magnitude of a flood cannot be predicted beforehand, a flexible multi-stage routing method considering operator judgment during flood operations is proposed to produce an optimized flood routing policy. The related operation rule is derived for existing and rehabilitated dam and the expected annual damage (EAD) compared in both conditions. The results show that the dam heightening could be considered as an effective way to improve the dam role in flood damage reduction.

Load-adaptive energy-saving strategy based on matching game in edge-enhanced metro FiWi

This paper proposes an Edge-Enhanced Metro FiWi strategy to meet the increasing requirements of connectivity, reliability and latency for various Internet of Things applications in 5G era. Based on the fiber-wireless (FiWi) network, the networks architecture considered in this paper contains the metro network and access network facilitated by global control and management. The deployment of 5G makes energy consumption a severe issue, especially in access networks. Edge-Enhanced Metro FiWi with expanded coverage and intensive device deployment consumes even more energy, making the design of energy conservation mechanisms inevitable. Different from traditional energy-saving methods of optical network units (ONU) and mesh access points that control devices independently based on their own load conditions, this paper considers the joint optimization of sleep nodes across the network based on network states via matching game theory. A load adaptive energy-saving strategy is proposed to dynamically determine the optimal number of sleeping devices according to the network traffic. Considering the influence of route selection on sleeping node selection, our routing policy aggregates the traffic to the least number of destination nodes to set free as many ONUs as possible. Finally, a two-level many-to-one two-sided matching game model that maximizes the profit of each participant is adopted to solve the energy consumption minimization problem within delay constraints. The simulation results show that compared with threshold-based sleep mode and the shortest path first routing algorithm, the proposed strategy significantly reduces the energy consumption and keeps the delay under control.

Supervised Learning for Arrival Time Estimations in Restaurant Meal Delivery

Restaurant meal delivery companies have begun to provide customers with meal arrival time estimations to inform the customers’ selection. Accurate estimations increase customer experience, whereas inaccurate estimations may lead to dissatisfaction. Estimating arrival times is a challenging prediction problem because of uncertainty in both delivery and meal preparation process. To account for both processes, we present an offline and online-offline estimation approaches. Our offline method uses supervised learning to map state features directly to expected arrival times. Our online-offline method pairs online simulations with an offline approximation of the delivery vehicles’ routing policy, again achieved via supervised learning. Our computational study shows that both methods perform comparably to a full near-optimal online simulation at a fraction of the computational time. We present an extensive analysis on how arrival time estimation changes the experience for customers, restaurants, and the platform. Our results indicate that accurate arrival times not only raise service perception but also improve the overall delivery system by guiding customer selections, effectively resulting in faster delivery and fresher food.

Cluster-based routing and wavelength assignment for multiple multicasts in 3D Optical Network-on-Chip

The combination of three-dimensional integrated circuits (3D ICs) and Optical Network-on-Chip (ONoC) can provide significant performance gains for Chip Multi-Processors (CMPs). Multicast communication is one of the most important inter-core communication primitives, which widely exists in parallel computing applications in CMPs. However, most existing schemes only focus on the optimization of one multicast, which limits the practical applications since real systems often have to handle multiple multicasts requested from various applications concurrently. In this paper, we target on routing and wavelength assignment for multiple multicasts in 3D ONoC, with the objective of reducing the number of wavelengths required. The main idea is to develop low-complexity routing policies to reduce the number of wavelengths required for routing multiple multicasts traffic by considering the distribution of multicast nodes in 3D ONoC. Extensive simulations with different traffic profiles reveal that our proposed scheme can reduce the number of wavelengths used by 33.1% compared to other schemes. Both theoretical and simulation results show that the proposed scheme has the advantages of low routing complexity, low wavelength requirement, and good scalability.

SYMMETRIC IMPACT OF EXCHANGE RATE VOLATILITY ON FOREIGN DIRECT INVESTMENT IN PAKISTAN: DO THE GLOBAL FINANCIAL CRISES AND POLITICAL REGIMES MATTER?

Understanding currency fluctuations carries a high level of risk. A currency can slide or spike as a response of an economic indicators revelation or an unexpected political event, both of which are unforeseeable. As foreign investors become more optimistic about the country’s future, a healthier economy usually requires a strong currency. Therefore, it is necessary to assess the exchange rate volatility risk in the context of direct investment in order to develop preventive policy routes that would encourage foreign investors into the country. To this purpose, the autoregressive distributive least square (ARDL) technique has been employed to investigate the symmetric effect of exchange rate volatility on foreign direct investment (FDI) during global financial crises and political regimes from January 2000 to December 2018. In order to eliminate arbitrariness, the study uses the most accredited estimate of volatility, GARCH (1 1), as part of the re-estimation process. Besides this, the interaction has been studied under various regimes to see how the political environment affects foreign market participants’ expectations for future gains. The findings suggest that exchange rate volatility is adversely related to FDI and is a source of decreased net foreign investment in Pakistan. It also illustrates that during times of crises and apolitical regimes, Pakistan’s average FDI falls dramatically. The findings are extended to the context of Pakistan that has seen significant rupee volatility for the past few years. Our study recommends that policymakers should pursue exchange rate measures that promote exchange rate stability, which might assist to minimize risk of exchange rate volatility and so attract higher FDI. It is also crucial to formulate more sustainable investment methods that allow for efficient capital allocation during times of economic uncertainty.

Efficient ordering policy for secret key assignment in quantum key distribution-secured optical networks

Quantum key distribution (QKD) is a promising solution to protect data transmission in optical networks against security breaches. Hence, several studies have paid attention on integration of QKD with the existing optical networks. Meanwhile, blocking is a challenging issue for QKD lightpath requests (QLRs) due to limited number of network resources (wavelengths and time-slots) in existing optical networks. In QKD-secured optical networks, the blocking increases with increase in the number of QLRs as well as with the modifications of secret keys for enhancing the security level of QLRs. Hence, the blocking affects the QLRs of different security levels, especially the QLRs of high and moderate security levels. Thus, the prioritization of QLRs based on the security level is essential for reducing the impact of blocking in such networks. In this paper, we propose a secret key assignment priority ordering policy (SKA-POP) for routing, wavelength and time-slot assignment (RWTA) to improve the success probability of QLRs. In the proposed SKA-POP, the resources during assignment and re-assignment are allocated based on the proposed priority criteria. The performance of the proposed SKA-POP is analyzed in terms of the success probability and the probability of secret key update failure (PSKUF). Simulations performed on two different network topologies, namely, NSFNET and UBN24, indicate that the proposed SKA-POP performs better than the non-priority based RWTA (NP-RWTA), priority order-based RWTA (POB-RWTA), partial-priority based RWTA (PP-RWTA), and a version of SKA-POP, i.e., SKA-POP with the longest route first (SKA-POP-LRF) schemes, when the number of QLRs (traffic load) increase in the network. .

Branch‐and‐price for energy optimization in multi‐hop wireless sensor networks

AbstractWireless sensor networks (WSNs) are widely deployed to support transmissions of the huge data emitted by heterogeneous connected devices. The quality of service (QoS) and energy requirements in WSNs are dramatically increasing and becoming challenging issues. Integrating network coding, in the data transmission process, leads to reduced energy consumption and improved QoS. In this work, we study the problem of energy efficiency in multi‐hop WSNs, namely wireless unsplittable multi‐commodity flow with network coding (wUMCFC). We propose mixed‐integer programming formulations of the wUMCFC problem, and we implement an open source solver wUMCFC based on the branch‐and‐price algorithm. To the best of our knowledge, this work is the first study of the wUMCFC problem by mathematical programming tools. We perform a computational study on realistic instances with an analysis of the performance of the proposed algorithm. The computational results show the efficiency of the network coding and provide important information for strategic decisions on routing policies and network management https://github.com/lidingxu/wUMCFC.

Optimal Open-Loop Routing and Threshold-Based Allocation in TWO Parallel QUEUEING Systems with Heterogeneous Servers

In this paper, we study the problem of optimal routing for the pair of two-server heterogeneous queues operating in parallel and subsequent optimal allocation of customers between the servers in each queue. Heterogeneity implies different servers in terms of speed of service. An open-loop control assumes the static resource allocation when a router has no information about the state of the system. We discuss here the algorithm to calculate the optimal routing policy based on specially constructed Markov-modulated Poisson processes. As an alternative static policy, we consider an optimal Bernoulli splitting which prescribes the optimal allocation probabilities. Then, we show that the optimal allocation policy between the servers within each queue is of threshold type with threshold levels depending on the queue length and phase of an arrival process. This dependence can be neglected by using a heuristic threshold policy. A number of illustrative examples show interesting properties of the systems operating under the introduced policies and their performance characteristics.