Rerouting Strategy Research Articles

공급 사슬 고장 기간에 따른 대응 전략의 효과성 연구

Today supply chains have become more complex and exposed to more diverse risks due to globalization. It is essential for the supply chain to choose and implement a response strategy that fits the characteristics of the risk. In particular, it is necessary to use appropriate response strategies according to the period of failure. Based on literature research, this study presented hypotheses on the effectiveness of the response strategy on the failure period and tested the hypotheses using simulation and regression analysis. As for the response strategy, we investigated surplus inventory strategy, multiple sourcing strategy, and contingent rerouting strategy. As a result, the longer the failure period of the supply chain, the lower the effect of the surplus inventory strategy, while the effect of the multiple sourcing strategy increased. The contingent rerouting strategy showed a lower effect than non-response when the failure period was short, but when the failure period was long, it showed the best effect among the three strategies. Furthermore, the effect of the surplus inventory strategy further decreased as the demand increased sharply, and the effect of the multiple sourcing strategy further increased. The contingent rerouting strategy has seen a dramatic increase in response effects as the failure period increases when market demand increases sharply.

Dynamic adaptive vehicle re-routing strategy for traffic congestion mitigation of grid network

This paper proposes a possible methodology for detecting and mitigating traffic congestion. This method is carried out using a custom-designed traffic scenario model. The model is fully developed in lieu of abundant data support from actual traffic events, which is applicable to localized traffic surveillance conditions, where massive data collection from surveilling devices is infeasible or unviable. This approach includes two parts: model construction and re-routing strategy. The model construction part focuses on the development of a traffic driving scenario, which takes various criteria such as traffic volume and traffic signal into consideration. The goal of this setup is to create a realistic-possible environment, where the proposed methods can be tested. The re-routing strategy is implemented based on the model simulation result of a medium-scale drive-able road map. The idea of the adaptive vehicle re-routing strategy is inspired by the k-shortest path algorithm, adapted with the dynamic congestion re-routing strategy. It will be shown that the model is able to automatically identify congestion patterns that are happening on any road segments, and then initiates a proper re-routing strategy to alleviate such congestion in a timely manner. Although the methodology is realized and validated within a simulated model, the concept is transparent to any transportation system under study without extra complexity. In addition, the proposed modeling and simulation technique can be used for real-time implementation in intelligent transportation management systems.

Rerouting Strategies for Highly Available Virtual Network Functions

The development of Virtual Network Functions (VNFs) migrates network functions from dedicated hardware to groups of commodity servers called network points of presence (N-PoPs). In this way, network services are redefined as interconnected VNFs called Service Function Chains (SFCs). The emerging of SFCs significantly reduces the cost of network services and improves scalability. However, the availability of SFCs brings new challenges since a failure of any N-PoP along an SFC affects its availability. In this paper, we propose two rerouting strategies to improve the availability of SFCs. First, we propose a local rerouting strategy to bypass the failed N-PoPs on SFCs using locally rerouted paths (LRPs). In the strategy, we formulate an optimization model to minimize the maximum load on links while deploying SFCs and LRPs to reduce the risk of congested links caused by local rerouting. We then propose an approximation algorithm to solve the optimization problem, preserving an approximation ratio of <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(\log (|V|))$</tex-math></inline-formula> , where <inline-formula><tex-math notation="LaTeX">$|V|$</tex-math></inline-formula> is the number of N-PoPs in the network. We also propose an alternative heuristic algorithm to improve efficiency. Second, we propose a supplementary rerouting strategy with an online algorithm to provide supplementary rerouted paths when original SFCs and corresponding LRPs fail at the same time in the local rerouting strategy. The online algorithm is proved to have an <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(\log (|V|))$</tex-math></inline-formula> competitive ratio to the offline optimum. Finally, our extensive simulation results show that the proposed algorithms can provide highly available SFCs with less congested links.

An Improved Traffic Rerouting Strategy Using Real-Time Traffic Information and Decisive Weights

To alleviate possible traffic congestion during rush hours, an improved traffic rerouting strategy designed for (or interplayed with) the vehicular ad hoc networks (VANETs) <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> is proposed in this paper. The proposed strategy considers not only the real-time traffic information but also the maximization of road utilization to reach an optimized solution. Furthermore, an alternative path selection is incorporated so that an efficient rerouting path is determined according to the destination of the vehicle, the density and the capacity of the road, and the speed. Via simulations, we can successfully show that our proposed strategy significantly outperforms the closely related strategies in the literature in terms of the travel time, CO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula> emission, and fuel consumption, in particular, when heavy traffic is involved. Specifically, our proposed strategy can gain up to 20% of reduction in the average travel time in a realistic scenario to be observed later. Additionally, our proposed strategy gains 19% at least of improvement in the case with different road networks and 28.6% at least of improvement in the case with different percentages of reduction of traffic lights as compared to the closely related strategies as shown in the Appendices given in the supplementary downloadable material.

Design of robust programmable networks with bandwidth-optimal failure recovery scheme

More than ever, data networks have demonstrated their central role in the world economy, but also in the well-being of humanity that needs fast and reliable networks. In parallel, with the emergence of Network Function Virtualization (NFV) and Software Defined Networking (SDN), efficient network algorithms considered too hard to be put in practice in the past now have a second chance to be considered again. In this context, as new networks will be deployed and current ones get significant upgrades, it is thus time to rethink the network dimensioning problem with protection against failures. In this paper, we consider a path-based protection scheme with the global rerouting strategy in which, for each failure situation, there may be a new routing of all the demands. Our optimization task is to minimize the needed amount of bandwidth. After discussing the hardness of the problem, we develop two scalable mathematical models that we handle using both Column Generation and Benders Decomposition techniques. Through extensive simulations on real-world IP network topologies and on randomly generated instances, we show the effectiveness of our methods: they lead to savings of 40 to 48% of the bandwidth to be installed in a network to protect against failures compared to traditional schemes. Finally, our implementation in OpenDaylight demonstrates the feasibility of the approach. Its evaluation with Mininet shows that our solution provides sub-second recovery times, but the way it is implemented may greatly impact the amount of signaling traffic exchanged. In our evaluations, the recovery phase requires only a few tens of milliseconds for the fastest implementation, compared to a few hundreds of milliseconds for the slowest one.

Pre-flight rerouting combining A∗ algorithm and AHP under severe weather

Delays due to bad weather have become common in recent years.When the delay occurs, in most cases, flights will choose ground-holding which results in the waste of airspace resources and reduces the operating efficiency of the entire air route network. In order to deepen the reform of the ATC operations, local rerouting strategy before flight is imperative. In this paper, based on the influence degree and scope of severe weather, historical meteorological data and flight plan data, the damage situation of the air route is comprehensively analyzed, and the segment that pilots need to avoid in route selection is defined. Taking the priority of different city pairs into consideration, the OD pair allocation order model based on AHP (analytic hierarchy process) was established. In addition, this paper establishes the evaluation index of navigation modification combined with the actual operation, and makes a comparative analysis with an example.

A Runtime Optimization Approach for FPGA Routing

In this paper, we present a new field-programmable gate array (FPGA) routing approach on the basis of the PathFinder routing algorithm. During each routing iteration, our approach applies a novel timing-based rerouting strategy to only reroute the illegal paths. At a lower level, each maze expansion is started from the relatively close part of current routing tree to search for the target sink on the routing resource graph. Experimental results demonstrate that on average the proposed approach reduces the routing runtime by 68.5% compared with the timing-driven router in versatile place and route FPGA placement and routing framework, with reduction of 2.5% and 1.4% in critical path delay and wirelength, respectively.

A Unified Framework for Vehicle Rerouting and Traffic Light Control to Reduce Traffic Congestion

As the number of vehicles grows rapidly each year, more and more traffic congestion occurs, becoming a big issue for civil engineers in almost all metropolitan cities. In this paper, we propose a novel pheromone-based traffic management framework for reducing traffic congestion, which unifies the strategies of both dynamic vehicle rerouting and traffic light control. Specifically, each vehicle, represented as an agent, deposits digital pheromones over its route, while roadside infrastructure agents collect the pheromones and fuse them to evaluate real-time traffic conditions as well as to predict expected road congestion levels in near future. Once road congestion is predicted, a proactive vehicle rerouting strategy based on global distance and local pheromone is employed to assign alternative routes to selected vehicles before they enter congested roads. In the meanwhile, traffic light control agents take online strategies to further alleviate traffic congestion levels. We propose and evaluate two traffic light control strategies, depending on whether or not to consider downstream traffic conditions. The unified pheromone-based traffic management framework is compared with seven other approaches in simulation environments. Experimental results show that the proposed framework outperforms other approaches in terms of traffic congestion levels and several other transportation metrics, such as air pollution and fuel consumption. Moreover, experiments over various compliance and penetration rates show the robustness of the proposed framework.

Traffic management model for vehicle re-routing and traffic light control based on Multi-Objective Particle Swarm Optimization

In this paper, a novel traffic management model is presented, which simultaneously optimizes vehicle re-routing and traffic light control to alleviate traffic congestion and limit the effects of incidents on traffic flow based on Multi-Objective Particle Swarm Optimization (MOPSO) method. Once a co ngested road is predicted, our proposed Multi-Objective Traffic Light Control is then applied to optimize signal timing which takes the maximization of traffic flow on the edge where the incident takes place and the minimization of the average junction waiting time as two objectives. To improve the performance and sensitivity of MOPSO algorithm, we used Q-Learning algorithm to grant to each agent of the swarm the ability of selecting appropriate MOPSO parameters adapted to the structure of the problem. At the same time, when the situation of the traffic flow starts to become more serious, we adopt a novel Multi-Objective Vehicle Re-routing strategy for assigning alternatives routes to cars before entering the congested road, in order to perform dynamic load balancing. Vehicle re-routing is also optimized by MOPSO to simultaneously find the shortest and least popular path. The obtained results from the simulation using SUMO, a well-known microscopic traffic simulator, confirm the efficiency of the proposed system.

A multiple colonies artificial bee colony algorithm for a capacitated vehicle routing problem and re-routing strategies under time-dependent traffic congestion

An Online Vehicle Routing Problem is a formation of Capacitated Vehicle Routing Problem with re-routing strategy to resolve the problem of inefficient vehicle routing caused by traffic congestion. A flexible delivery rerouting strategy is proposed, which aims at reducing the risk of late delivery. The method of terminating an exploration in a solution by the original ABC algorithm, when the solution is trapped in local optima, is to abandon the solution after specific tolerance limits are set. The phenomenon of local optimal traps will be repeated rapidly after a lengthy recursive process and will eventually result in a low quality solution, with a more complex combinatorial problem when the capability of the exploration is restricted by an inflexible termination criterion. Therefore, this paper proposes a novel scheme using a Multiple Colonies Artificial Bee Colony algorithm. The designs of the outstanding bee selection for colony communication show it to be superior in exploitation. The performance of the proposed algorithm is examined through by Capacitated Vehicle Routing instances and a case study, and the results indicate the potential of using real time information for data-driven vehicle scheduling.

Adaptive RSU re-routing under delay constraint in multi-hop vehicular networks

Vehicular wireless networks VWNs offer wireless multi-hop communications between vehicles and roadside units RSUs. A multi-hop wireless network with carry-and-forward routing may suffer from longer delay in packet delivery. However, the applications may demand stringent delay requirement in multi-hop VWNs. Therefore, this paper proposes an adaptive RSU re-routing strategy to alter the current associated RSU to the best RSU under delay or throughput constraints for applications in multi-hop VWNs. Performance results show that the proposed strategy is able to adaptively select a suitable packet size for the trade-off between delay and throughput to satisfy the diverse requirements of applications in multi-hop VWNs.

Agent-based Multi-Service Routing for Polar-orbit LEO broadband satellite networks

There exist essential requirements for providing service differentiation in the next-generation networks. In this paper, AMSR (Agent-based Multi-Service Routing) is proposed and evaluated for Polar-orbit LEO broadband satellite networks. AMSR forwards best-effort traffic in a distribute manner using three factors: unfair traffic distribution, link utilization level and constellation geometry characteristics. Meanwhile, AMSR employs an on-demand source routing method, called agent-based inter-satellite link QoS routing (AIQR), to implement QoS routing in space. In AIQR, agent migrates based on best-effort forwarding to gather QoS routing information, and determines QoS path considering both call duration and the periodic changes in network topology. Moreover, with protecting ongoing communication in mind, a gradual rerouting strategy is introduced to deal with user-to-satellite and satellite-to-satellite handover. Through simulations on an Iridium-like system, AMSR is shown to achieve better QoS guarantee and more effective service differentiation as well as lower overhead compared with the previously proposed methods. Especially, simulation results thoroughly reveal AMSR’s handover performance in terms of latency and hop count, rerouting frequency and overhead. Additionally, the credits and further research of this study are pointed out.

A hybrid rerouting scheme

In this work we study a routing scheme combined with an end-to-end rerouting procedure. We focus in particular on a new rerouting strategy called Shared Robust Rerouting (ShRR). This strategy combi...

SVNE: Survivable Virtual Network Embedding Algorithms for Network Virtualization

Network virtualization can offer more flexibility and better manageability for the future Internet by allowing multiple heterogeneous virtual networks (VN) to coexist on a shared infrastructure provider (InP) network. A major challenge in this respect is the VN embedding problem that deals with the efficient mapping of virtual resources on InP network resources. Previous research focused on heuristic algorithms for the VN embedding problem assuming that the InP network remains operational at all times. In this paper, we remove this assumption by formulating the survivable virtual network embedding (SVNE) problem. We then develop a pro-active, and a hybrid policy heuristic to solve it, and a baseline policy heuristic to compare to. The hybrid policy is based on a fast re-routing strategy and utilizes a pre-reserved quota for backup on each physical link. Our evaluation results show that our proposed heuristics for SVNE outperform the baseline heuristic in terms of long term business profit for the InP, acceptance ratio, bandwidth efficiency, and response time.

Traffic Rerouting Strategy against Jamming Attacks in WSNs for Microgrid

In a microgrid as an energy infrastructure, the vulnerability against jamming attacks is fatal. Thus, the ability to deal with jamming attacks and maintain an acceptable level of service degradation in presence of the attacks is needed. To solve the problem, we propose a traffic rerouting scheme in wireless communication infrastructure for islanded microgrid. We determine disjoint multiple paths as candidates of a detour path and then select the detour path among the candidates in order to reduce the effect of jamming attack and distribute traffic flows on different detour paths. Through performance comparison, we show that our scheme outperforms a conventional scheme in terms of packet delivery ratio and end-to-end delay.

Enhancement of blocking performance in all-optical WDM networks via wavelength reassignment and route deviation

This paper deals with the problem of improving the performance of all-optical Wavelength Division Multiplexing (WDM) networks in terms of blocking probability. Blocking in such network architecture is caused mainly by the so-called wavelength continuity constraint (wcc), which requires a provisioned lightpath to occupy the same wavelength on all the links along its route. To alleviate the effect of wcc on the blocking performance, a rerouting strategy is proposed. The main idea behind this strategy lies in ensuring the creation of wavelength-continuous routes for the connections that are blocked due to wcc. Particularly, the proposed rerouting strategy is built upon two main pillars, namely wavelength reassignment and route deviation. The former strives to keep the physical route followed by the lightpath intact, while the latter changes the route of the lightpath when wavelength reassignment proves to be ineffective. The performance of the rerouting strategy is studied through extensive simulations in the context of networks employing the least congested path (LCP) routing algorithm and the first-fit (FF) wavelength assignment strategy. The reported results show that the proposed rerouting strategy can guarantee significant enhancement in terms of blocking probability while achieving near-optimal performance for networks utilizing a small number of wavelengths per fiber.

GigaManP2P: an overlay network for distributed QoS management and resilient routing

Management of long-distance, high-speed optical backbones spanning multiple administrative domains requires new solutions for challenging tasks. In particular, it is not trivial to negotiate, monitor and continuously enforce the required quality of service (QoS) for applications that span multiple domains. This paper proposes GigaManP2P: a novel peer-to-peer (P2P) management architecture for high-speed QoS-aware backbones. GigaManP2P peers provide management services in a ubiquitous fashion through modules that interface with both the communication infrastructure and network users. In particular, we describe management services for on inter-domain QoS monitoring and resilient routing. After detecting a QoS constraint violation trend, a proactive rerouting strategy is triggered based on redundant virtual circuits, allowing both full and partial rerouting. The P2P overlay implementation is the basis for allowing transparent communication across autonomous systems. Experimental results showing the overhead of the P2P infrastructure in comparison to raw Simple Network Management Protocol, and the performance of the rerouting strategy, are presented. Copyright © 2011 John Wiley & Sons, Ltd.

Rerouting in advance for preempted IR calls in QoS-enabled networks

When network resources are shared between Instantaneous Request (IR) and Book- Ahead (BA) connections, activation of future BA connections may cause preemption of on-going IR connections due to resource scarcity. Rerouting of preempted calls via alternative feasible paths is often considered as the final option to restore and maintain service continuity. Existing rerouting techniques, however, do not ensure acceptably low service disruption time and suffer from high failure rate and low network utilization. In this work, a new rerouting strategy is proposed that estimates the future resource scarcity, identifies the probable candidate connections for preemption and initiates the rerouting process in advance for those connections. Simulations on a widely used network topology suggest that the proposed rerouting scheme achieves a higher successful rerouting rate with lower service disruption time, while not compromising other network performance metrics like utilization and call blocking rate.

Accelerating reconfiguration of degradable VLSI arrays

The reconfiguration process within fault tolerant systems must be accelerated for the rapid generation of degradable VLSI arrays. Given an m x n mesh-connected VLSI array with faulty density p, finding an m' x n' fault-free sub-array under the row and column rerouting such that m' ≥ r and n' ≥ c for integers r and c, has been shown to be NP-complete. In this paper, we propose efficient heuristic techniques based on partial rerouting strategy. We show that by enhancing popular logical row exclusion and the greedy column rerouting techniques, we reduce the routing time from O(max(m - r, n - c) (1 - ρ) · m n) to O(β · (1 - ρ) · m · n), where β<<max (m, n), without loss of harvest. Simulations based on a range of square arrays and fault densities conclusively show that the computation time can be improved by more than 70% for a fault density of 10%. Moreover, performance improvement of up to 98% is noted when the fault density is reduced to 0.1%.

A real-time methodology for minimizing mean flowtime in FMSs with machine breakdowns: threshold-based selective rerouting

This paper presents the threshold-based selective rerouting (TSR) heuristic to minimize mean flowtime of parts in a flexible manufacturing system (FMS) with machine failures. Most of the available methodologies considering machine failures are either off-line methods or propose different versions of rerouting all the jobs to alternate machines. The objective of this methodology is to develop an efficient real-time rerouting strategy, which can be easily applicable in case of machine failures and is able to adapt to the dynamic features of an FMS. Parts in the queue of a failed machine are selectively rerouted to alternate machines when the benefit in terms of waiting time obtained from rerouting exceeds a specific pre-determined threshold value. The threshold concept and the performance of TSR in minimizing mean flowtime are tested with extensive simulation experiments. It has been shown that TSR provides significant improvements in system performance measures compared to other real-time rerouting methods and that the threshold value is dependent on system parameters. The relationship between the threshold value and system parameters has also been determined.