New Algorithms for Capacity Allocation and Scheduling of Multiplexed Variable Bit Rate Video Sources

This study presents simple and accurate heuristics for determining the equivalent bandwidth for multiplexed variable bit rate (VBR) video sources. The results are based on empirical studies of measurement data of various classes of VBR video sources. They are also validated through extensive simulation. The principal result is that the equivalent bandwidth per source for n independent and identically distributed VBR video sources may be approximated by a hyperbolic function of the form: a coth -1n + b where a and b are independent of n. Further, assuming ∈ is the acceptable loss tolerance, statistical regression shows that b is a linear function of mean and log ( ∈ ), while a is a polynomial in log( ∈ ). The capacity assignment problem is further augmented with a scheduling algorithm that is an extension of the Virtual Clock Algorithm. The new algorithm belongs to a class of algorithms which we refer to as Generalized Virtual Clock (GVC) algorithms. The particular GVC algorithm investigated in this paper estimates the instantaneous rate of transmission of each source, and uses the estimate instead of the static average rates, for prioritizing packets. In so doing, it attempts to synchronize the switch scheduling rates and the packet arrival rates of each source, and improves upon the spatial loss distribution characteristics of Virtual Clock. The combined allocation and scheduling algorithms are proposed as means for guaranteeing Quality of Service in high speed networks

A Taxonomy of Communications Demand

Demand forecasts are an essential tool for planning capacity and formulating policy. Traffic estimates are becoming increasingly unreliable, however, as accelerating rates of use and new communications applications invalidate conventional forecasting assumptions. This paper presents an alternative approach to the study of telecommunications demand: build aggregate estimates for demand based on the elasticity of demand for bandwidth. We argue that price elasticity models are necessary to grasp the interaction between Moore-type technological progress and non-linear demand functions. Traditional marketing models are premised on existing or, at best, foreseeable services. But in a period of sustained price declines, applications-based forecasts will be unreliable. Dramatically lower prices can cause fundamental changes in the mix of applications and, hence, the nature of demand. We consider the option of posing demand theoretically in terms of service attributes. Our conclusion is that the positive feedback loop of technology-driven price decreases and high-elasticity demand will quickly make it possible to base forecasts on bandwidth elasticity alone. Industry analysts and policymakers need models of consumer demand applicable under dynamic conditions. We conclude by drawing implications of our demand model for network planning, universal service policies, and the commoditization of communications carriage

Some aspects of traffic control and performance evaluation of ATM networks

The emerging high-speed Asynchronous Transfer Mode (ATM) networks are expected to integrate through statistical multiplexing large numbers of traffic sources having a broad range of statistical characteristics and different Quality of Service (QOS) requirements. To achieve high utilisation of network resources while maintaining the QOS, efficient traffic management strategies have to be developed. This thesis considers the problem of traffic control for ATM networks. The thesis studies the application of neural networks to various ATM traffic control issues such as feedback congestion control, traffic characterization, bandwidth estimation, and Call Admission Control (CAC). A novel adaptive congestion control approach based on a neural network that uses reinforcement learning is developed. It is shown that the neural controller is very effective in providing general QOS control. A Finite Impulse Response (FIR) neural network is proposed to adaptively predict the traffic arrival process by learning the relationship between the past and future traffic variations. On the basis of this prediction, a feedback flow control scheme at input access nodes of the network is presented. Simulation results demonstrate significant performance improvement over conventional control mechanisms. In addition, an accurate yet computationally efficient approach to effective bandwidth estimation for multiplexed connections is investigated. In this method, a feed forward neural network is employed to model the nonlinear relationship between the effective bandwidth and the traffic situations and a QOS measure. Applications of this approach to admission control, bandwidth allocation and dynamic routing are also discussed. A detailed investigation has indicated that CAC schemes based on effective bandwidth approximation can be very conservative and prevent optimal use of network resources. A modified effective bandwidth CAC approach is therefore proposed to overcome the drawback of conventional methods. Considering statistical multiplexing between traffic sources, we directly calculate the effective bandwidth of the aggregate traffic which is modelled by a two-state Markov modulated Poisson process via matching four important statistics. We use the theory of large deviations to provide a unified description of effective bandwidths for various traffic sources and the associated ATM multiplexer queueing performance approximations, illustrating their strengths and limitations. In addition, a more accurate estimation method for ATM QOS parameters based on the Bahadur-Rao theorem is proposed, which is a refinement of the original effective bandwidth approximation and can lead to higher link utilisation

Dynamic bandwidth allocation in ATM networks

Includes bibliographical references.This thesis investigates bandwidth allocation methodologies to transport new emerging bursty traffic types in ATM networks. However, existing ATM traffic management solutions are not readily able to handle the inevitable problem of congestion as result of the bursty traffic from the new emerging services. This research basically addresses bandwidth allocation issues for bursty traffic by proposing and exploring the concept of dynamic bandwidth allocation and comparing it to the traditional static bandwidth allocation schemes

Video traffic modeling and delivery

Video is becoming a major component of the network traffic, and thus there has been a great interest to model video traffic. It is known that video traffic possesses short range dependence (SRD) and long range dependence (LRD) properties, which can drastically affect network performance. By decomposing a video sequence into three parts, according to its motion activity, Markov-modulated self-similar process model is first proposed to capture autocorrelation function (ACF) characteristics of MPEG video traffic. Furthermore, generalized Beta distribution is proposed to model the probability density functions (PDFs) of MPEG video traffic. It is observed that the ACF of MPEG video traffic fluctuates around three envelopes, reflecting the fact that different coding methods reduce the data dependency by different amount. This observation has led to a more accurate model, structurally modulated self-similar process model, which captures the ACF of the traffic, both SRD and LRD, by exploiting the MPEG structure. This model is subsequently simplified by simply modulating three self-similar processes, resulting in a much simpler model having the same accuracy as the structurally modulated self-similar process model. To justify the validity of the proposed models for video transmission, the cell loss ratios (CLRs) of a server with a limited buffer size driven by the empirical trace are compared to those driven by the proposed models. The differences are within one order, which are hardly achievable by other models, even for the case of JPEG video traffic. In the second part of this dissertation, two dynamic bandwidth allocation algorithms are proposed for pre-recorded and real-time video delivery, respectively. One is based on scene change identification, and the other is based on frame differences. The proposed algorithms can increase the bandwidth utilization by a factor of two to five, as compared to the constant bit rate (CBR) service using peak rate assignment

Statistical multiplexing and connection admission control in ATM networks

Asynchronous Transfer Mode (ATM) technology is widely employed for the transport of network traffic, and has the potential to be the base technology for the next generation of global communications. Connection Admission Control (CAC) is the effective traffic control mechanism which is necessary in ATM networks in order to avoid possible congestion at each network node and to achieve the Quality-of-Service (QoS) requested by each connection. CAC determines whether or not the network should accept a new connection. A new connection will only be accepted if the network has sufficient resources to meet its QoS requirements without affecting the QoS commitments already made by the network for existing connections. The design of a high-performance CAC is based on an in-depth understanding of the statistical characteristics of the traffic sources

Analysis of topology aggregation techniques for QoS routing

We study and compare topology aggregation techniques used in QoS routing. Topology Aggregation (TA) is defined as a set of techniques that abstract or summarize the state information about the network topology to be exchanged, processed, and maintained by network nodes for routing purposes. Due to scalability, aggregation techniques have been an integral part of some routing protocols. However, TA has not been studied extensively except in a rather limited context. With the continuing growth of the Internet, scalability issues of QoS routing have been gaining importance. Therefore, we survey the current TA techniques, provide methodology to classify, evaluate, and compare their complexities and efficiencies. ©2007 ACM.postprin

Global Distribution of Water Vapor and Cloud Cover--Sites for High Performance THz Applications

Absorption of terahertz radiation by atmospheric water vapor is a serious impediment for radio astronomy and for long-distance communications. Transmission in the THz regime is dependent almost exclusively on atmospheric precipitable water vapor (PWV). Though much of the Earth has PWV that is too high for good transmission above 200 GHz, there are a number of dry sites with very low attenuation. We performed a global analysis of PWV with high-resolution measurements from the Moderate Resolution Imaging Spectrometer (MODIS) on two NASA Earth Observing System (EOS) satellites over the year of 2011. We determined PWV and cloud cover distributions and then developed a model to find transmission and atmospheric radiance as well as necessary integration times in the various windows. We produced global maps over the common THz windows for astronomical and satellite communications scenarios. Notably, we show that up through 1 THz, systems could be built in excellent sites of Chile, Greenland and the Tibetan Plateau, while Antarctic performance is good to 1.6 THz. For a ground-to-space communication link up through 847 GHz, we found several sites in the Continental United States where mean atmospheric attenuation is less than 40 dB; not an insurmountable challenge for a link.Comment: 15 pages, 23 figure