Internet Resource Management and Pricing

Originally conceived and funded as a research project, the Internet has grown into a commercial, global and integrated service network. This has changed the nature of traffic on the Internet with the increasing use of things like video conferencing and time critical transactions. These forms of Internet usage place high demands on bandwidth. Added to this is the fact that the number of users is increasing at a dramatic rate and shows no signs of slowing. This is leading to a \u27tragedy of the commons\u27 where endemic congestion will reduce the value of the Internet to everyone. It also implies the introduction of some form of quality of service (QoS) to differentiate time critical traffic from less time critical traffic. Pricing usage has been shown to be effective in controlling congestion by promoting more effective resource allocation. To provide the necessary QoS, there is an argument that simply increasing the available bandwidth will achieve this, while at the same time maintaining the simple model of the current Internet. However, there is also an argument that a more complex model may be needed that provides various levels of QoS with an associated pricing scheme to manage usage of these levels of QoS. A major part of the debate on this subject surrounds the trade-off between efficiency, economics and complexity that exists in introducing QoS and pricing to the Internet. This document discusses some of these issues, presents some of the current proposals for pricing Internet usage and finally compares the presented pricing proposals

Performance modelling and the representation of large scale distributed system functions

This thesis presents a resource based approach to model generation for performance characterization and correctness checking of large scale telecommunications networks. A notion called the timed automaton is proposed and then developed to encapsulate behaviours of networking equipment, system control policies and non-deterministic user behaviours. The states of pooled network resources and the behaviours of resource consumers are represented as continually varying geometric patterns; these patterns form part of the data operated upon by the timed automata. Such a representation technique allows for great flexibility regarding the level of abstraction that can be chosen in the modelling of telecommunications systems. None the less, the notion of system functions is proposed to serve as a constraining framework for specifying bounded behaviours and features of telecommunications systems. Operational concepts are developed for the timed automata; these concepts are based on limit preserving relations. Relations over system states represent the evolution of system properties observable at various locations within the network under study. The declarative nature of such permutative state relations provides a direct framework for generating highly expressive models suitable for carrying out optimization experiments. The usefulness of the developed procedure is demonstrated by tackling a large scale case study, in particular the problem of congestion avoidance in networks; it is shown that there can be global coupling among local behaviours within a telecommunications network. The uncovering of such a phenomenon through a function oriented simulation is a contribution to the area of network modelling. The direct and faithful way of deriving performance metrics for loss in networks from resource utilization patterns is also a new contribution to the work area

Traffic engineering in dynamic optical networks

Traffic Engineering (TE) refers to all the techniques a Service Provider employs to improve the efficiency and reliability of network operations. In IP over Optical (IPO) networks, traffic coming from upper layers is carried over the logical topology defined by the set of established lightpaths. Within this framework then, TE techniques allow to optimize the configuration of optical resources with respect to an highly dynamic traffic demand. TE can be performed with two main methods: if the demand is known only in terms of an aggregated traffic matrix, the problem of automatically updating the configuration of an optical network to accommodate traffic changes is called Virtual Topology Reconfiguration (VTR). If instead the traffic demand is known in terms of data-level connection requests with sub-wavelength granularity, arriving dynamically from some source node to any destination node, the problem is called Dynamic Traffic Grooming (DTG). In this dissertation new VTR algorithms for load balancing in optical networks based on Local Search (LS) techniques are presented. The main advantage of using LS is the minimization of network disruption, since the reconfiguration involves only a small part of the network. A comparison between the proposed schemes and the optimal solutions found via an ILP solver shows calculation time savings for comparable results of network congestion. A similar load balancing technique has been applied to alleviate congestion in an MPLS network, based on the efficient rerouting of Label-Switched Paths (LSP) from the most congested links to allow a better usage of network resources. Many algorithms have been developed to deal with DTG in IPO networks, where most of the attention is focused on optimizing the physical resources utilization by considering specific constraints on the optical node architecture, while very few attention has been put so far on the Quality of Service (QoS) guarantees for the carried traffic. In this thesis a novel Traffic Engineering scheme is proposed to guarantee QoS from both the viewpoint of service differentiation and transmission quality. Another contribution in this thesis is a formal framework for the definition of dynamic grooming policies in IPO networks. The framework is then specialized for an overlay architecture, where the control plane of the IP and optical level are separated, and no information is shared between the two. A family of grooming policies based on constraints on the number of hops and on the bandwidth sharing degree at the IP level is defined, and its performance analyzed in both regular and irregular topologies. While most of the literature on DTG problem implicitly considers the grooming of low-speed connections onto optical channels using a TDM approach, the proposed grooming policies are evaluated here by considering a realistic traffic model which consider a Dynamic Statistical Multiplexing (DSM) approach, i.e. a single wavelength channel is shared between multiple IP elastic traffic flows

Policy issues in interconnecting networks

To support the activities of the Federal Research Coordinating Committee (FRICC) in creating an interconnected set of networks to serve the research community, two workshops were held to address the technical support of policy issues that arise when interconnecting such networks. The workshops addressed the required and feasible technologies and architectures that could be used to satisfy the desired policies for interconnection. The results of the workshop are documented

Congestion Control by Bandwidth-Delay Tradeoff in Very High-Speed Networks: The Case of Window-Based Control

Increasing bandwidth-delay product of high-speed wide-area networks is well-known to make conventional dynamic traffic control schemes sluggish . Still, most existing schemes employ dynamic control, among which TCP and ATM Forum\u27s rate-based flow control are prominent examples. So far, little has been investigated as to how the existing schemes will scale as bandwidth further increases up to gigabit speed and beyond. Our investigation in this paper is the first to show that dynamic control has a severe scalability problem with bandwidth increase, and to propose an entirely new approach to traffic control that overcomes the scalability problem. The essence of our approach is in exercising control in bandwidth domain rather than time domain, in order to avoid time delay in control. This requires more bandwidth than the timed counterpart, but achieves a much faster control. Furthermore, the bandwidth requirement is not excessively large because the bandwidth for smaller control delay and we call our approach Bandwidth-Latency Tradeoff (BLT). While the control in existing schemes are bound to delay, BLT is bound to bandwidth. As a fallout, BLT scales tied to bandwidth increase, rather than increasingly deteriorate as conventional schemes. Surprisingly, our approach begins to pay off much earlier than expected, even from a point where bandwidth-delay product is not so large. For instance, in a roughly AURORA-sized network, BLT far outperforms TCP on a shared 150Mbps link, where the bandwidth-delay product is around 60KB. In the other extreme where bandwidth-delay product is large, BLT outperforms TCP by as much as twenty times in terms of network power in a gigabit nationwide network. More importantly, BLT is designed to continue to scale with bandwidth increase and the performance gap is expected to widen further

Hybrid Communication Protocols and Control Algorithms for NextGen Aircraft Arrivals

Capacity constraints imposed by current air traffic management technologies and protocols could severely limit the performance of the Next Generation Air Transportation System (NextGen). A fundamental design decision in the development of this system is the level of decentralization that balances system safety and efficiency. A new surveillance technology called automatic dependent surveillance-broadcast (ADS-B) can be potentially used to shift air traffic control to a more distributed architecture; however, channel variations and interference with existing secondary radar replies can affect ADS-B systems. This paper presents a framework for managing arrivals at an airport by using a hybrid centralized/distributed algorithm for communication and control. The algorithm combines the centralized control that is used in congested regions with the distributed control that is used in lower traffic density regions. The hybrid algorithm is evaluated through realistic simulations of operations around a major airport. The proposed strategy is shown to significantly improve air traffic control performance under various operating conditions by adapting to the underlying communication, navigation, and surveillance systems. The performance of the proposed strategy is found to be comparable to fully centralized strategies, despite requiring significantly less ground infrastructure.National Science Foundation (U.S.) (Grant CNS-931843)United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N0014-08-0696)United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-09-1-1051)United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-12-1-0609)United States. Air Force Office of Scientific Research. Multidisciplinary University Research Initiative (Grant FA9550-10-1-0567

Performance evaluation of synergic operation of algorithms enabling opportunistic networks - D4.3

Deliverable D4.3 del projecte OneFITPreprin

Network delay control through adaptive queue management

Timeliness in delivering packets for delay-sensitive applications is an important QoS (Quality of Service) measure in many systems, notably those that need to provide real-time performance. In such systems, if delay-sensitive traffic is delivered to the destination beyond the deadline, then the packets will be rendered useless and dropped after received at the destination. Bandwidth that is already scarce and shared between network nodes is wasted in relaying these expired packets. This thesis proposes that a deterministic per-hop delay can be achieved by using a dynamic queue threshold concept to bound delay of each node. A deterministic per-hop delay is a key component in guaranteeing a deterministic end-to-end delay. The research aims to develop a generic approach that can constrain network delay of delay-sensitive traffic in a dynamic network. Two adaptive queue management schemes, namely, DTH (Dynamic THreshold) and ADTH (Adaptive DTH) are proposed to realize the claim. Both DTH and ADTH use the dynamic threshold concept to constrain queuing delay so that bounded average queuing delay can be achieved for the former and bounded maximum nodal delay can be achieved for the latter. DTH is an analytical approach, which uses queuing theory with superposition of N MMBP-2 (Markov Modulated Bernoulli Process) arrival processes to obtain a mapping relationship between average queuing delay and an appropriate queuing threshold, for queue management. While ADTH is an measurement-based algorithmic approach that can respond to the time-varying link quality and network dynamics in wireless ad hoc networks to constrain network delay. It manages a queue based on system performance measurements and feedback of error measured against a target delay requirement. Numerical analysis and Matlab simulation have been carried out for DTH for the purposes of validation and performance analysis. While ADTH has been evaluated in NS-2 simulation and implemented in a multi-hop wireless ad hoc network testbed for performance analysis. Results show that DTH and ADTH can constrain network delay based on the specified delay requirements, with higher packet loss as a trade-off

Congestion control and QoS provisioning in IP networks.

Hua Cunqing.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 53-56).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Congestion Control in the IP Network --- p.1Chapter 1.2 --- Quality of Service in the IP network --- p.2Chapter 1.3 --- Structure of Thesis --- p.3Chapter 2 --- Background --- p.4Chapter 2.1 --- TCP and Congestion Control --- p.4Chapter 2.1.1 --- Slow Start --- p.4Chapter 2.1.2 --- Congestion Avoidance --- p.5Chapter 2.1.3 --- "Fast Retransmit, Fast Recovery and Timeout" --- p.5Chapter 2.2 --- Active Queue Management --- p.7Chapter 2.3 --- Integrated Services and Differentiated Services --- p.8Chapter 3 --- The Fairness of TCP Vegas in Networks with Multiple Congested Gate- ways --- p.10Chapter 3.1 --- Introduction --- p.10Chapter 3.2 --- TCP Vegas and related works --- p.11Chapter 3.3 --- Analysis --- p.13Chapter 3.4 --- Simulation Results --- p.15Chapter 3.4.1 --- Throughput for different number of active cross connections --- p.16Chapter 3.4.2 --- Throughput for different number of flows in each connection --- p.17Chapter 3.4.3 --- Multiple congestion vs Single congestion --- p.17Chapter 3.5 --- Summary --- p.19Chapter 4 --- The Joint Congestion Control for TCP/IP Networks --- p.21Chapter 4.1 --- Background --- p.21Chapter 4.2 --- The Joint Congestion Control --- p.23Chapter 4.2.1 --- Path Load Reduction Factor --- p.23Chapter 4.2.2 --- The Congestion Control Algorithm --- p.24Chapter 4.2.3 --- Probing Interval --- p.26Chapter 4.2.4 --- Parameter Setting --- p.26Chapter 4.2.5 --- Encoding of R --- p.27Chapter 4.3 --- Simulation Results --- p.28Chapter 4.3.1 --- Congestion Window Behavior --- p.28Chapter 4.3.2 --- Throughput Stability --- p.31Chapter 4.3.3 --- Packet Loss Ratio --- p.31Chapter 4.3.4 --- Fairness Index --- p.32Chapter 4.3.5 --- Fairness in Multiple-hop Network --- p.32Chapter 4.3.6 --- Parameter Sensitivity --- p.33Chapter 4.3.7 --- Interaction between JCC and Reno flows --- p.35Chapter 4.4 --- Summary --- p.35Chapter 5 --- S-WTP : Shifted Waiting Time Priority Scheduling for Delay Differ- entiated Services --- p.37Chapter 5.1 --- Introduction --- p.37Chapter 5.2 --- Scheduling Algorithms for Delay Differentiated Services --- p.38Chapter 5.3 --- Shifted Waiting Time Priority Scheduling --- p.41Chapter 5.3.1 --- Local Update --- p.42Chapter 5.3.2 --- Global Update --- p.42Chapter 5.3.3 --- Computational overhead --- p.42Chapter 5.4 --- Simulation Results --- p.43Chapter 5.4.1 --- Microscopic View of Individual Packet Delay of S-WTP and WTP --- p.43Chapter 5.4.2 --- Delay Ratios in Different Timescales --- p.44Chapter 5.4.3 --- Effects of aggregate traffic and class load distribution on delay ratio --- p.44Chapter 5.4.4 --- Delay Ratios with More Classes --- p.48Chapter 5.5 --- Summary --- p.48Chapter 6 --- Conclusions --- p.50Chapter 6.1 --- Congestion Control --- p.50Chapter 6.2 --- Quality of Service Provision --- p.51Chapter 6.3 --- Final Remarks --- p.5