Decentralized Network Flow Control

In this paper, the problem of finding the decentralized flow control of a BCMP network is investigated. The packets of each of the users correspond to different classes of customers. The servers in the network are exponential and serve packets with FIFO policy. Each network user operates with either a state-dependent arrival rate (i.e. an arrival rate which depends upon the number of the user\u27s packets that have not yet been acknowledged) or a state-dependent arrival rate (which the user chooses). The decentralized flow control problem is formulated udder two optimization criteria. Under the first optimization criterion, the decentralized flow control corresponding to each of the network users maximizes the throughput of the network, under the constraint that the expected time delay of the packets in the network does not exceed a preassigned upper bound. Under the second optimization criterion, the decentralized flow control corresponding to each of the network users maximizes the throughput of the network, under the constraint that the expected time delay of each particular class of packets does not exceed a preassigned (user dependent) upper bound. In this paper all the previous classes of problems are handled uniformly, using efficient nonlinear optimization techniques

Scalable laws for stable network congestion control

Discusses flow control in networks, in which sources control their rates based on feedback signals received from the network links, a feature present in current TCP protocols. We develop a congestion control system which is arbitrarily scalable, in the sense that its stability is maintained for arbitrary network topologies and arbitrary amounts of delay. Such a system can be implemented in a decentralized way with information currently available in networks plus a small amount of additional signaling

Selfish Optimization in Computer Networks

This paper describes two applications of decentralized (Pareto) optimization to problems of computer communication networks. The first application is to develop a generalized principle for optimality of multi-hop broadcast channel access schemes. The second application is to decentralized flow-control in fixed virtual-circuit networks (e.g., SNA) using power maximization as the performance index. The decentralized approach to optimum network behavior yields, among other results, characterization of fair global objective functions, and optimal decentralized greedy network control algorithms. The main conclusion of this paper is that Pareto-optimality methods can be successfully used to develop optimal decentralized behavior algorithms where a centralized approach is (sometimes provably) not applicable

Assessment of novel distributed control techniques to address network constraints with demand side management

The development of sustainable generation, a reliable electricity supply and affordable tariffs are the primary requirements to address the uncertainties in different future energy scenarios. Due to the predicted increase in Distributed Generation (DG) and load profile changes in future scenarios, there are significant operational and planning challenges facing netwrok operators. These changes in the power system distribution network require a new Active Network Management (ANM) control system to manage distribution constraint issues such as thermal rating, voltage, and fault levels. The future smart grid focuses on harnessing the control potential from demand side via bidirectional power flow, transparent information communication, and contractual customer participation. Demand Side Management (DSM) is considered as one of the effective solutions to defer network capacity reinforcement, increase energy efficiency, facilitate renewable access, and implement low carbon energy strategy. From the Distribution Network Operator's (DNO) perspective, the control opportunity from Demand Response (DR) and Decentralized Energy Resource (DER) contributes on capacity investment reduction, energy efficiency, and enable low carbon technologies. This thesis develops a new decentralized control system for dealing effectively with the constraint issues in the Medium Voltage (MV) distribution network. In the decentralized control system, two novel control approaches are proposed to autonomously relieve the network thermal constraint via DNO's direct control of the real power in network components during the operation period. The first approach, Demand Response for Power Flow Management (DR-PFM), implements the DSM peak clipping control of Active Demand (AD), whilst the second approach, Hybrid Control for Power Flow Management (HC-PFM), implements the hybrid control of both AD and DER. The novelty of these two new control algorithms consists in the application of a Constraint Satisfaction Problem (CSP) based programming model on decision making of the real power curtailment to relieve the network thermal overload. In the Constraint Programming (CP) model, three constraints are identified: a preference constraint, and a network constraint. The control approaches effectively solve the above constraint problem in the CSP model within 5 seconds' time response. The control performance is influenced by the pre-determined variable, domain and constraint settings. These novel control approaches take advantages on flexible control, fast response and demand participation enabling in the future smart grid.The development of sustainable generation, a reliable electricity supply and affordable tariffs are the primary requirements to address the uncertainties in different future energy scenarios. Due to the predicted increase in Distributed Generation (DG) and load profile changes in future scenarios, there are significant operational and planning challenges facing netwrok operators. These changes in the power system distribution network require a new Active Network Management (ANM) control system to manage distribution constraint issues such as thermal rating, voltage, and fault levels. The future smart grid focuses on harnessing the control potential from demand side via bidirectional power flow, transparent information communication, and contractual customer participation. Demand Side Management (DSM) is considered as one of the effective solutions to defer network capacity reinforcement, increase energy efficiency, facilitate renewable access, and implement low carbon energy strategy. From the Distribution Network Operator's (DNO) perspective, the control opportunity from Demand Response (DR) and Decentralized Energy Resource (DER) contributes on capacity investment reduction, energy efficiency, and enable low carbon technologies. This thesis develops a new decentralized control system for dealing effectively with the constraint issues in the Medium Voltage (MV) distribution network. In the decentralized control system, two novel control approaches are proposed to autonomously relieve the network thermal constraint via DNO's direct control of the real power in network components during the operation period. The first approach, Demand Response for Power Flow Management (DR-PFM), implements the DSM peak clipping control of Active Demand (AD), whilst the second approach, Hybrid Control for Power Flow Management (HC-PFM), implements the hybrid control of both AD and DER. The novelty of these two new control algorithms consists in the application of a Constraint Satisfaction Problem (CSP) based programming model on decision making of the real power curtailment to relieve the network thermal overload. In the Constraint Programming (CP) model, three constraints are identified: a preference constraint, and a network constraint. The control approaches effectively solve the above constraint problem in the CSP model within 5 seconds' time response. The control performance is influenced by the pre-determined variable, domain and constraint settings. These novel control approaches take advantages on flexible control, fast response and demand participation enabling in the future smart grid

Asynchronous Algorithms for Optimal Flow Control of BCMP Networks

The decentralized flow control problem for an open multiclass BCMP network is studied. The power based optimization criterion is employed for the derivation of the optimal flow control for each of the network\u27s users. It is shown that that optimal arrival rates correspond to the unique Nash equilibrium point of a noncooperative game problem. Asynchronous algorithms are presented for the computation of the Nash equilibrium point of the network. Among them, the nonlinear Gauss-Seidel algorithms is distinguished for its robustness and speed of convergence

The interconnection in active distribution networks

The active distribution network (AN) has been mentioned recently to adapt with a large-scale implementation of distributed generators. One of its enhancements is increasing interconnections to provide more than one power flow path among local control areas. These parallel physical connections might cause several problems for the network such as congestion and loop flow. Considering the characteristics of the AN, this paper proposes a decentralized approach to control power flow which has some analogies to the telephone networks. The implementation of this control mechanism is based on a multi-agent system (MAS) technology. A simulation of the power system and MAS is created to illustrate the possibility of the proposed method

Analyzing Linear Communication Networks using the Ribosome Flow Model

The Ribosome Flow Model (RFM) describes the unidirectional movement of interacting particles along a one-dimensional chain of sites. As a site becomes fuller, the effective entry rate into this site decreases. The RFM has been used to model and analyze mRNA translation, a biological process in which ribosomes (the particles) move along the mRNA molecule (the chain), and decode the genetic information into proteins. Here we propose the RFM as an analytical framework for modeling and analyzing linear communication networks. In this context, the moving particles are data-packets, the chain of sites is a one dimensional set of ordered buffers, and the decreasing entry rate to a fuller buffer represents a kind of decentralized backpressure flow control. For an RFM with homogeneous link capacities, we provide closed-form expressions for important network metrics including the throughput and end-to-end delay. We use these results to analyze the hop length and the transmission probability (in a contention access mode) that minimize the end-to-end delay in a multihop linear network, and provide closed-form expressions for the optimal parameter values