Flow Management Mechanism in Software-Defined Network

Software-defined networking(SDN)is a paradigm shift in modern networking.However,centralised controller architecture in SDNimposed flow setup overhead issue as the control plane handles all flows regardless of size and priority.Existing frameworks strictly reduce control plane overhead and it does not focus on rule placement of the flows itself.Furthermore,existing frameworks do not focus on managing elephant flows like RTSP.Thus,the proposed mechanism will use the flow statistics gathering method such as random packet sampling to determine elephant flow and microflow via a predefined threshold.This mechanism will ensure that the control plane works at an optimum workload because the controller only manages elephant flows via reactive routing and rule placement respectively.Reactive routing has reduced link bandwidth usage below the pre-defined threshold.Furthermore,rule placement has increased average throughput and total transfer to 238%.Meanwhile,the data plane switches will be able to forward microflows via multipath wildcard routing without invoking controller in greater responding time by 85 ms faster in two Transmission Control Protocol(TCP)traffic and achieved 11%and 12%higher total transfer size and throughput respectively.Hence,the controller’s workload reduced significantly to 48%in two TCP traffic.

Review on artificial intelligence techniques for improving representative air traffic management capability

The use of artificial intelligence(AI)has increased since the middle of the 20th century,as evidenced by its applications to a wide range of engineering and science problems.Air traffic management(ATM)is becoming increasingly automated and autonomous,making it lucrative for AI applications.This paper presents a systematic review of studies that employ AI techniques for improving ATM capability.A brief account of the history,structure,and advantages of these methods is provided,followed by the description of their applications to several representative ATM tasks,such as air traffic services(ATS),airspace management(AM),air traffic flow management(ATFM),and flight operations(FO).The major contribution of the current review is the professional survey of the AI application to ATM alongside with the description of their specific advantages:(i)these methods provide alternative approaches to conventional physical modeling techniques,(ii)these methods do not require knowing relevant internal system parameters,(iii)these methods are computationally more efficient,and(iv)these methods offer compact solutions to multivariable problems.In addition,this review offers a fresh outlook on future research.One is providing a clear rationale for the model type and structure selection for a given ATM mission.Another is to understand what makes a specific architecture or algorithm effective for a given ATM mission.These are among the most important issues that will continue to attract the attention of the AI research community and ATM work teams in the future.

Stochastic Air Traffic Flow Management for Demand and Capacity Balancing Under Capacity Uncertainty

This paper introduces an innovative approach to the synchronized demand-capacity balance with special focus on sector capacity uncertainty within a centrally controlled collaborative air traffic flow management(ATFM)framework.Further with previous study,the uncertainty in capacity is considered as a non-negligible issue regarding multiple reasons,like the impact of weather,the strike of air traffic controllers(ATCOs),the military use of airspace and the spatiotemporal distribution of nonscheduled flights,etc.These recessive factors affect the outcome of traffic flow optimization.In this research,the focus is placed on the impact of sector capacity uncertainty on demand and capacity balancing(DCB)optimization and ATFM,and multiple options,such as delay assignment and rerouting,are intended for regulating the traffic flow.A scenario optimization method for sector capacity in the presence of uncertainties is used to find the approximately optimal solution.The results show that the proposed approach can achieve better demand and capacity balancing and determine perfect integer solutions to ATFM problems,solving large-scale instances(24 h on seven capacity scenarios,with 6255 flights and 8949 trajectories)in 5-15 min.To the best of our knowledge,our experiment is the first to tackle large-scale instances of stochastic ATFM problems within the collaborative ATFM framework.

OPTIMAL FLOW CONTROL PROBLEM IN AIR TRAFFIC CONTROL:PARTⅠ

The fundamental case is considered in which flights from many destinations must be scheduled for arrival at a single congested airport having limited capacities.An air traffic control(ATC)model is developed in this case.A new and efficient algorithm for the optimal solution of ground holding strategy problem(GHSP)is put forward and verified by a numerical example.

FLIGHT CONFLICT DETECTION AND RESOLUTION

The air traffic control (ATC) systems are facing more and more serious congestive because of the increasing of air traffic flow in China. One of the most available ways to solve the problem is 'free flight' that the pilots may choose the air route and flight speed suitable for them. But this will lead to the difficulties for the controllers. This paper presents how ATC genetic algorithms can be used to detect and to solve air traffic control conflicts in free flight. And it also shows that this algorithm perfectly suits for solving flight conflicts resolution because of its short computing time.

Role-Based Modeling in Enterprise Dynamic Alliance

In enterprise dynamic alliance towards agile manufacturing systems, any group needs to know which activities it must take part in, in what order those activities take place and how many other groups it must interact with. The traditional procedural models can’t meet the requirement of the modeling for an enterprise dynamic alliance. In this paper, we use role based models for an enterprise modeling. Role based models group activities into roles, which describe the desired behavior of individual enterprise. We have developed a graphical modeling tool built on Web/DCOM platform according to Petri net theory and role based methodology.

A Functional Framework for Integrating eCRM with Workflow Management Based on Customer Value

eCRM ties customer relationship management with e-business. Very often, eCRM is interfaced with other information systems to form a seamless integration and interchange of information both inside and outside an organization--a work flow management system. This integration of business partners, suppliers, and customers is essential in this global competitive market environment. An effective infrastructure and hence an appropriate framework are required to provide the information exchange and data analysis between eCRM and work flow management. This paper proposes a functional framework of eCRM based on customer value to realize the win-win strategy for both the companies and their customers. Moreover, a workflow management system also forms an integral part of this total solution to facilitate the implementation of a supply chain or extended enterprise.

Metropolitan Emblems or Symbols in Globalization？ Both for the Future of Metropolises and Their Citizens

This research concerns on ＂metropolis and metropolisation＂： what is new with the symbols of city local life and the emblems of global competition？ From New York, Rome, Barcelone, Paris and Bordeaux, the same question demands specific answers： can we live and appreciate urban and metropolitan symbols and emblems together or on their own？ That is the question for towns and the future of their citizens. Confrontation of urban sites and observed places with the plural points of view of their establishment, their designers and urban skills, and their inhabitants and citizens. The combination of these several points of view always make a richer discovery than their disciplinary partition and monopoly.

Dynamic Air Route Open-Close Problem for Airspace Management

Dynamic airspace management plans and assigns airspace resources to airspace users on demand to increase airspace capacity. Although many studies of air traffic flow management （ATFM） have sought to optimally allocate air traffic to get the best use of given airspace resources, few studies have focused on how to build an efficient air traffic network or how to adjust the current network in real time. This paper presents an integer program model named the dynamic air route open-close problem （DROP）. DROP has a cost-based objective function which takes into account constraints such as the shortest occupancy time of routes, which are not considered in ATFM models. The aim of DROP is to determine which routes will be opened to a certain user during a given time period. Simulation results show that DROP can facilitate utilization of air routes. DROP, a simplified version of an air traffic network constructing problem, is the first step towards realizing dynamic airspace management. The combination of ATFM and DROP can facilitate decisions toward more reasonable, efficient use of limited airspace resources.

Study on Optimization of Passenger Flow at a Metro Station Based on AnyLogic-Case Study of Youfangqiao Station of Nanjing Metro Line 2

In this study,simulation software AnyLogic was used to establish a station simulation model for a metro line.First,a basic model of the environment of the metro station was drawn,and accordingly,reasonable assumptions and simplifications were proposed.Then,a diagram of the passenger walking path was created and the simulation variables and functions for passenger flow management were designed.Considering Youfangqiao Station of Nanjing Metro Line 2 in China as an example,the real passenger flow data of this station were statistically analyzed.To simulate the station passenger flow management,input parameters such as the passenger space diameter,passenger flow generation rate,delay rate of automatic fare collection equipment and security check machine,and the number of gates were considered.Passenger flow management was optimized for the morning and evening peak periods,and reasonable suggestions were proposed based on the optimization results,providing a theoretical basis for the construction planning and pre-evaluation of station operation capacities of urban rail transit systems.

Strategic flight assignment approach based on multi-objective parallel evolution algorithm with dynamic migration interval

The continuous growth of air traffic has led to acute airspace congestion and severe delays, which threatens operation safety and cause enormous economic loss. Flight assignment is an economical and effective strategic plan to reduce the flight delay and airspace congestion by rea- sonably regulating the air traffic flow of China. However, it is a large-scale combinatorial optimiza- tion problem which is difficult to solve. In order to improve the quality of solutions, an effective multi-objective parallel evolution algorithm （MPEA） framework with dynamic migration interval strategy is presented in this work. Firstly, multiple evolution populations are constructed to solve the problem simultaneously to enhance the optimization capability. Then a new strategy is pro- posed to dynamically change the migration interval among different evolution populations to improve the efficiency of the cooperation of populations. Finally, the cooperative co-evolution （CC） algorithm combined with non-dominated sorting genetic algorithm II （NSGA-II） is intro- duced for each population. Empirical studies using the real air traffic data of the Chinese air route network and daily flight plans show that our method outperforms the existing approaches, multi- objective genetic algorithm （MOGA）, multi-objective evolutionary algorithm based on decom- position （MOEA/D）, CC-based multi-objective algorithm （CCMA） as well as other two MPEAs with different migration interval strategies.

Locally generalised multi-agent reinforcement learning for demand and capacity balancing with customised neural networks

Reinforcement Learning(RL)techniques are being studied to solve the Demand and Capacity Balancing(DCB)problems to fully exploit their computational performance.A locally gen-eralised Multi-Agent Reinforcement Learning(MARL)for real-world DCB problems is proposed.The proposed method can deploy trained agents directly to unseen scenarios in a specific Air Traffic Flow Management(ATFM)region to quickly obtain a satisfactory solution.In this method,agents of all flights in a scenario form a multi-agent decision-making system based on partial observation.The trained agent with the customised neural network can be deployed directly on the corresponding flight,allowing it to solve the DCB problem jointly.A cooperation coefficient is introduced in the reward function,which is used to adjust the agent’s cooperation preference in a multi-agent system,thereby controlling the distribution of flight delay time allocation.A multi-iteration mechanism is designed for the DCB decision-making framework to deal with problems arising from non-stationarity in MARL and to ensure that all hotspots are eliminated.Experiments based on large-scale high-complexity real-world scenarios are conducted to verify the effectiveness and efficiency of the method.From a statis-tical point of view,it is proven that the proposed method is generalised within the scope of the flights and sectors of interest,and its optimisation performance outperforms the standard computer-assisted slot allocation and state-of-the-art RL-based DCB methods.The sensitivity analysis preliminarily reveals the effect of the cooperation coefficient on delay time allocation.

A multi-objective multi-memetic algorithm for network-wide conflict-free 4D flight trajectories planning

Under the demand of strategic air traffic flow management and the concept of trajectory based operations（TBO）,the network-wide 4D flight trajectories planning（N4DFTP） problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories（4DTs）（3D position and time） for all the flights in the whole airway network.Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity,an efficient model for strategiclevel conflict management is developed in this paper.Specifically,a bi-objective N4 DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated.In consideration of the large-scale,high-complexity,and multi-objective characteristics of the N4DFTP problem,a multi-objective multi-memetic algorithm（MOMMA） that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented.It is capable of rapidly and effectively allocating 4DTs via rerouting,target time controlling,and flight level changing.Additionally,to balance the ability of exploitation and exploration of the algorithm,a special hybridization scheme is adopted for the integration of local and global search.Empirical studies using real air traffic data in China with different network complexities show that the proposed MOMMA is effective to solve the N4 DFTP problem.The solutions achieved are competitive for elaborate decision support under a TBO environment.