Buffer losses vs. deadline violations for ABR traffic in an ATM switch: A computational approach

The B‐ISDN will carry a variety of traffic types: the Variable Bit Rate traffic (VBR), of which compressed video is an example, Continuous Bit Rate traffic (CBR), of which telemetry is an example, Data traffic, and Available Bit Rate traffic (ABR) that represents aggregate data traffic with very limited guarantees on quality. Of these, VBR and CBR have timing constraints and need synchronous bandwidth; data traffic is relatively delay insensitive. In this paper, we consider the VBR, Data and ABR traffic types and obtain the cumulative distribution function (cdf) of the queueing delay experienced by a burst of ABR traffic in the output buffer of an ATM switch. The cdf is used to trade off buffer loss probabilities against deadline violation probabilities through adjusting the buffer size and (delay) deadline values. Large buffers result in low losses but queueing delays can become excessive and cause a high level of deadline violations. Both losses and violations are detrimental and an operating point must be chosen to achieve a balance. In this paper we study the nature of the trade off. We develop a stochastic Petri net model assuming periodic burst arrivals for VBR and Poisson arrival processes for the Data and ABR traffic types at the burst level, and solve the model analytically (numerically) using a decomposition approach. This decomposition, along with the inherent decomposability of the tagged customer approach for obtaining the cdf opens up a possibility of carrying out fast computations using a parallel machine for selecting the operating point each time that a call is admitted. This revised version was published online in June 2006 with corrections to the Cover Date.     

Incorporating fault debugging activities into software reliability models: a simulation approach

A large number of software reliability growth models have been proposed to analyse the reliability of a software application based on the failure data collected during the testing phase of the application. To ensure analytical tractability, most of these models are based on simplifying assumptions of instantaneous & perfect debugging. As a result, the estimates of the residual number of faults, failure rate, reliability, and optimal software release time obtained from these models tend to be optimistic. To obtain realistic estimates, it is desirable that the assumptions of instantaneous & perfect debugging be amended. In this paper we discuss the various policies according to which debugging may be conducted. We then describe a rate-based simulation framework to incorporate explicit debugging activities, which may be conducted according to the different debugging policies, into software reliability growth models. The simulation framework can also consider the possibility of imperfect debugging in conjunction with any of the debugging policies. Further, we also present a technique to compute the failure rate, and the reliability of the software, taking into consideration explicit debugging. An economic cost model to determine the optimal software release time in the presence of debugging activities is also described. We illustrate the potential of the simulation framework using two case studies.     

A Best Practice Guide to Resource Forecasting for Computing Systems

Recently, measurement-based studies of software systems have proliferated, reflecting an increasingly empirical focus on system availability, reliability, aging, and fault tolerance. However, it is a nontrivial, error-prone, arduous, and time-consuming task even for experienced system administrators, and statistical analysts to know what a reasonable set of steps should include to model, and successfully predict performance variables, or system failures of a complex software system. Reported results are fragmented, and focus on applying statistical regression techniques to monitored numerical system data. In this paper, we propose a best practice guide for building empirical models based on our experience with forecasting Apache web server performance variables, and forecasting call availability of a real-world telecommunication system. To substantiate the presented guide, and to demonstrate our approach in a step by step manner, we model, and predict the response time, and the amount of free physical memory of an Apache web server system, as well as the call availability of an industrial telecommunication system. Additionally, we present concrete results for a) variable selection where we cross benchmark three procedures, b) empirical model building where we cross benchmark four techniques, and c) sensitivity analysis. This best practice guide intends to assist in configuring modeling approaches systematically for best estimation, and prediction results.     

Driver Behavior and Situation Aware Brake Assistance for Intelligent Vehicles

This paper deals with the development of Human-Centric Intelligent Driver Assistance Systems. Rear-end collisions account for a large portion of traffic accidents. To help mitigate this problem, predictive braking systems and adaptive cruise control systems have been developed. However, these types of systems usually rely solely on the vehicle and vehicle surround sensors, either ignoring the human component of driving or learning the driver's control behavior using only these sensors. As with all human-computer interfaces, this has the potential to work against the driver, distract the driver further, or even annoy the driver so that the driver ignores or disables the system. It is, therefore, important to directly take the driver's intended actions into account when designing a driver assistance system. By using a probabilistic model for the system, warnings and preventative measures can be constructed based on varying levels of situational severity and driver attentiveness and intent. The research is based upon carefully conducted experimental trials involving a human subjects driving in natural manner and on typical freeways in the USA. The experiments, designed by inputs from cognitive scientist, were conducted in a specially designed instrumented vehicle to record important cues associated with driver's behavior, vehicle state, and vehicle surround in a synchronized manner. Quantitative results and analysis of the experimental trials are presented to show the feasibility and promise of this framework to predict the driver's intent to brake, the need for braking given the current situation, and at what level the driver should be warned     

Satellite imagery based adaptive background models and shadow suppression

Accurate segmentation of foreground objects in video scenes is critical for assuring reliable performance of vision systems for object tracking and situational awareness in outdoor scenes. Most existing techniques for background modeling and shadow suppression require that a number of parameters be “hand-tuned” based on environmental conditions. This paper presents two contributions to overcome such limitations. First, we develop and demonstrate a satellite imagery based approach for selecting appropriate background and shadow models. It is shown that the illumination conditions (i.e. cloud cover) of a scene can be reliably inferred from visible satellite images in the local region of the camera. The second contribution presented in the paper is introduction and evaluation of a Hybrid Cone-Cylinder Codebook (HC3) model which combines an adaptive efficient background model with HSV-color space shadow suppression into a single coherent framework. The structure of the HC3 model allows for seamless fusion of the satellite data. We are thereby able to exploit the fact that, for example, shadows are more pronounced on sunny days than cloudy days, allowing for more sensitive detection. The paper presents a set of experiments using day long sequences of videos from an operational surveillance system testbed. Results of these experimental analyses quantitatively illustrate the benefits of using satellite imagery to inform and adaptively adjust background and shadow modeling.     

Performance analysis of OFDM system with transmit antenna selection using delayed feedback

We consider orthogonal frequency division multiplexing (OFDM) in a multiple input single output (MISO) system. In the presence of spatially uncorrelated time-varying frequency selective channel, we use subcarrier by subcarrier antenna selection using delayed feedback. We derive closed-form expressions for the pdf of the received SNR and BER for MQAM constellation. The expressions have been obtained as a function of the correlation (ρ) between perfect channel state information (CSI) and delayed CSI, where 0 ≤ ρ ≤ 1. We have verified our analytical expressions by comparing them with simulation results. We have also reduced the BER expression for some special cases and compared them with the results available in the literature. We conclude that the diversity gain of the considered system is reduced to one for ρ < 1, i.e. not having perfect antenna selection for each subcarrier. However, we get some coding gain compared to single input single output system, the coding gain reduces with decreasing the correlation.     

Computing steady-state mean time to failure for non-coherent repairable systems

Mean time to failure (MTTF) is an important reliability measure. Previous research is mainly concerned with the MTTF computation of coherent systems. In this paper, we derive equations to calculate the steady-state MTTF for noncoherent systems. Based on the equations, we extend the BDD by adding an intersection edge in each BDD node to efficiently store additional information for MTTF computation of noncoherent systems. A recursive algorithm is developed for MTTF computation using the extended BDD. To accelerate building the extended BDD, a method is proposed to avoid calculating the intersection edge for some nodes by keeping node monotonicity during the BDD construction. We show the efficiency of our algorithm by applying it to some example fault trees, real-life applications, and large fault tree benchmarks.     

B-GWO based multi-UAV deployment and power allocation in NOMA assisted wireless networks

Wireless Networks - Unmanned Aerial Vehicles (UAVs) deployed as flying base stations is a promising technology for enhancing the quality of service (QoS) and quick recovery from unexpected damages...     

Availability modeling of energy management systems

Energy management system (EMS) computer architectures have changed significantly over the recent past increasing the difficulty and the need for a priori assessment of system performance and dependability. The old practice based on measurements is no longer acceptable because of the flexibility accrued with the deployment of the new distributed computer-based systems. The number of “what if” questions increased since EMS systems are now implemented using multiple workstations that can be interconnected in various different ways.In this paper we show how alternative configurations can be modeled and analyzed, before proposing and purchasing any equipment, with the assistance of Markov reward models. We review the concept of Markov reward models and show how they can be applied in the availability analysis of SCADA/EMS computer systems. The paper also presents a software tool that facilitates automatic generation and solution of large Markov reward models. The input language of this modeling tool uses a variation of stochastic Petri nets called stochastic reward nets, which are also reviewed. We believe this is the first time a detailed quantitative model of a SCADA/EMS computer system is proposed and solved in the general literature.