Flow Control and Scheduling Algorithm of Air-Space-Ground Integrated Networks

Summary form only given. The problem of flow control in ATM networks is a difficult one due to the uncertainties about the flow rates of the data streams coming from various sources of traffic at different nodes of the network. The flow control problem consists of multiplexing the ATM traffic and it is a challenging statistical management strategy where different patterns are directed into the communication channels. During the multiplexing stage, some prescribed performance constraints on the communication parameters should be strictly satisfied. The real motivation for considering an unconventional control technique for flow control in the ATM networks is because with the growing demand for the traffic of the integrated services (including voice and data communications), reliable decisions should be taken in real time. We consider the problem of estimating the statistical parameters of an intelligent control scheme for smoothing the ATM networks based on a neural network architecture. The neural network considered for carrying out the task of flow control runs the statistical estimation as an iterated map. The reason for this architectural design is the need to have a robust estimation scheme, where the convergence to an attractor of the neural dynamics can be enforced to hold in a small number of iterations of the statistical mapping. The neural network is symmetrically connected and this choice reflects a kind of tradeoff between the speed of convergence and the stability of the states during the successive states of the dynamical relaxation. A numerical experiment describing the dynamic performances of the neural controller for the ATM flow control is presented.

We consider a noncooperative game framework for combined routing and flow control in a network of parallel links, where the number of users (players) is arbitrarily large. The utility function of each user is related to the power criterion, and is taken as the ratio of some positive power of the total throughput of that user to the average delay seen by the user. The utility function is nonconcave in the flow rates of the user, for which we introduce a scaling to make it well defined as the number of users, N, becomes arbitrarily large. In spite of the lack of concavity, we obtain explicit expressions for the flow rates of the users and their associated routing decisions, which are in O(1/N) Nash equilibrium. This O(1/N) equilibrium solution, which is symmetric across different users and could be multiple in some cases, exhibits a delay-equalizing feature among the links which carry positive flow. The paper also provides the complete optimal solution to the single-user case, and includes several numerical examples to illustrate different features of the solutions in the single- as well as N-user cases, as N becomes arbitrarily large.

Efficient implementation of globally-aware network flow control

Application of Lagrangian relaxation to computer network control

In this paper, we study a flow control problem considering network coding in wireless ad-hoc networks with multi-path routing. As a network coding scheme, we use XOR network coding, in which each node bitwise-XORs some packets received from different sessions, and then broadcasts this coded packet to multiple nodes in a single transmission. This process can reduce the number of required transmissions, and thus can improve network utilization, especially if it is used with appropriate network coding-aware protocols. Considering this XOR network coding, we formulate an optimization problem for flow control that aims at maximizing network utility. By solving the optimization problem in a distributed manner, we implement a distributed flow control algorithm that provides the optimal transmitting rate on each of multiple paths of each session. The simulation results show that our flow control algorithm performs well exploiting the advantages of network coding and provides significant performance improvement.

This paper studies the problem of energy efficiency (EE) maximization via user association, power, and backhaul (BH) flow control in the downlink of millimeter wave BH heterogeneous networks. This problem is mathematically formulated as a mixed-integer non-linear program, which is non-convex. To get a tractable solution, the initial problem is separated into two sub-problems and optimized sequentially. The first is a joint user association and power control sub-problem for the access network (AN) (AN sub-problem). The second is a joint flow and power control sub-problem for the BH network (BH sub-problem). While the BH sub-problem is a convex optimization problem and hence can be efficiently solved, the AN sub-problem assumes the form of a generalized assignment problem, which is known to be NP-hard. To that end, we utilize Lagrangian decomposition to propose two polynomial time solution techniques that obtain a high-quality solution for the AN sub-problem. The first, referred to as Technique A, uses dynamic programming, the subgradient method, and a heuristic. The second, named Technique B, uses the multiplier adjustment method, the sorting algorithm, and a heuristic. Simulation results are used to demonstrate the effectiveness of the proposed energy efficient user association, power, and BH flow control algorithms as compared with benchmark user association schemes that incorporate the BH sub-problem algorithm, in terms of the total AN power, BH power, and overall network (AN plus BH) EE. The computational complexity and practical implementation of the proposed algorithms are discussed.

Multilevel flow control in telecommunication networks

We study the problem of decentralization of flow control in packet-switching networks under the isarithmic scheme. An incoming packet enters the network only if there are permits available at the entry port when it arrives. The actions of the controllers refer to the routing of permits in the network and the control variables are the corresponding probabilities. We study the behavior of adaptive algorithms implemented at the controllers to update these probabilities and seek optimal performance. This problem can be stated as a routing problem in a closed queueing network. The centralized version of a learning automation is a general framework presented along with the proof of asymptotic optimality. Decentralization of the controller gives rise to non-uniqueness of the optimal control parameters. Non-uniqueness can be exploited to construct asymptotically optimal learning algorithms that exhibit different behavior. We implement two different algorithms for the parallel operation and discuss their differences. Convergence is established using the weak convergence methodology. In addition to our theoretical results, we illustrate the main results using the flow control problem as a model example and verify the predicted behavior of the two proposed algorithms through computer simulations, including an example of tracking.

This paper focuses on flow control in high-speed net-works. Each node in a network handles its local traffic flow only on the basis of the information it is aware of, but it is preferable that the decision-making of each node leads to high performance of the whole network. To this end, we investigate the relationship between the flow control mech-anism of each node and network performance. We consider the situation in which the capacity of a link in the network is changed but individual nodes are not aware of this. Then we investigate the stability and adaptability of the network performance when the capacity of a link is changed, and discuss an appropriate flow control model on the basis of simulation results. 1

A shared network is largely oversubscribed by independent users who make random demands on the network. Network flow control is required for the orderly operation of the network under all potential traffic loads. This paper presents a qualitative analysis of the theory of flow control to circuit-switched and packet-switched networks and pro- ,. poses 4 complementary controls and 1 subtended control to constitute "flow control." They are traffic control, routing and delay control, congestion control, network management control, and end-end flow control. This paper introduces the concept of the "R-T function" to illustrate the synthesis of delays and routing freedoms, and to demonstrate the continuum between overload and congestion phenomena. The application of the qualitative theory of flow control to the Canadian Telephone Network is reviewed and the agreement between the theory and the practice is demonstrated. The telephone network is a prime example of a circuit-switched network which has received the benefit of extensive simulation and analytical studies, as well as a long experience to validate the conclusions reached in this paper.

We present Predictive Capacity Consumption (PCC), a congestion modeling extension to graph-based routing protocols. This extension provides a solution to the problem of flow control in Delay-Tolerant Networks (DTNs) and other overlays that can neither synchronize physical link state across the network nor negotiate bandwidth consumption bridging heterogeneous link layers. PCC enables the construction of a distributed, predictive congestion model independent of the underlying link layer without requiring excessive broadcasts or other mechanisms unfeasible in DTNs. PCC examines information generated by routing protocols and adjusts local routing graphs to account for predicted message paths, correcting for downstream congestion and message retransmission. Unlike other mechanisms, the flow control provided by PCC can be implemented anywhere a graph-based routing methodology is used and the adoption of this method requires only minor modification to the in-situ routing framework. We describe the PCC algorithm, analyze its operation, and demonstrate its performance by simulating multiple data streams driving a set of constrained networks to saturation. The simulation results show that PCC improves the throughput of the network by 97% over table routing approaches and by 37% over graph routing approaches without congestion models.

The introduction of wormhole routing over a decade ago enabled the use of networks with large diameter, such as the 2-D and 3-D meshes. In direct networks with possibly large diameters, the distances between nodes vary significantly. This will affect communication latencies especially with varying traffic loads. In this paper, we propose a new, low-cost distance-based flow control for wormhole networks. Conventional virtual channel flow control does not take into account distances and the physical channel is scheduled in a strict round-robin manner. The proposed flow control is achieved through two mechanisms, a distance-based message priority mapping and an efficient deadlock-free prioritized physical link allocation strategy. This paper presents the motivation behind the proposed flow control, a description of the mechanisms, and its implementation. The distance-based flow control is evaluated and compared against conventional virtual channel flow control for a wide range of system parameters. Results based on thorough simulation show that the performance of local and long-distance communication can be significantly improved, latency can be improved by up to 45%. The results demonstrate significant potential for designing high performance highly parallel wormhole systems.

Study of active flow control for a simplified vehicle model using the PANS method

In this paper we present a model for the controlled flow of a fluid through a network of channels using a coupled System of St Venant equations. We generalize in a variety of ways recent results of Coron, d’Andrea-Novel and Bastin concerning the stabilizability around equilibrium of the flow through a single channel to serially connected channels and finally to networks of channels. The work presented here is entirely based on the theory of quasilinear hyperbolic Systems. We also consider open-loop optimal control problems and provide numerical schemes both for the simulation and the control of such Systems.

Optimization network flow control and price coordination with feedback: proposal of a new distributed algorithm