In the world of embedded benchmarking and performance analysis, there has been very little agreement on standards for measuring performance of microprocessors, microcontrollers, and digital signal processors (DSPs). The EDN Embedded Microprocessor Benchmark Consortium (EEMBC-pronounced "embassy") (http://www.eembc.org) mission is to bring standardization to embedded benchmarking in the areas of automotive/industrial, consumer, networking, telecommunications, and office automation. This paper highlights practical uses of the benchmarks, shares some of the background in their development, and provides guidelines for avoiding some of the pitfalls associated with embedded benchmarking.

The main contributions of this paper are two-fold. First, we prove fundamental, similarly behaving lower and upper bounds, and give an approximation based on the bounds, which is effective for analyzing ATM multiplexers, even when the traffic has many, possibly heterogeneous, sources and their models are of high dimension. Second, we apply our analytic approximation to statistical models of video teleconference traffic, obtain the multiplexing system's capacity as determined by the number of admissible sources for given cell loss probability, buffer size and trunk bandwidth, and, finally, compare with results from simulations, which are driven by actual data from coders. The results are surprisingly close. Our bounds are based on Large Deviations theory. Our approximation has two easily calculated parameters, one is from Chernoff's theorem and the other is the system's dominant eigenvalue. A broad range of systems are analyzed and the time for analysis in each case is a fraction of a second.

The main contributions of this paper are two-fold. First, we prove fundamental, similarly behaving lower and upper bounds, and give an approximation based on the bounds, which is effective for analyzing ATM multiplexers, even when the traffic has many, possibly heterogeneous, sources and their models are of high dimension. Second, we apply our analytic approximation to statistical models of video teleconference traffic, obtain the multiplexing system's capacity as determined by the number of admissible sources for given cell-loss probability, buffer size and trunk bandwidth, and, finally, compare with results from simulations, which are driven by actual data from coders. The results are surprisingly close. Our bounds are based on large deviations theory. The main assumption is that the sources are Markovian and time-reversible. Our approximation to the steady-state buffer distribution is called Chenoff-dominant eigenvalue since one parameter is obtained from Chernoffs theorem and the other is the system's dominant eigenvalue. Fast, effective techniques are given for their computation. In our application we process the output of variable bit rate coders to obtain DAR(1) source models which, while of high dimension, require only knowledge of the mean, variance, and correlation. We require cell-loss probability not to exceed 10/sup -6/, trunk bandwidth ranges from 45 to 150 Mb/s, buffer sizes are such that maximum delays range from 1 to 60 ms, and the number of coder-sources ranges from 15 to 150. Even for the largest systems, the time for analysis is a fraction of a second, while each simulation takes many hours. Thus, the real-time administration of admission control based on our analytic techniques is feasible.>

This paper is an introduction to some large deviations techniques that have been used for analyzing models of communication networks. Starting from the beginning (sequences of i.i.d. random variables), it progresses to some Markov processes in discrete or continuous time. It illustrates the meaning of most theorems by applying them to a common example. Then the AMS model of buffering in ATM is analyzed in a variety of ways. Finally, a few other common models are discussed.>

In this paper we analyze a class of delayed feedback schemes that achieves the dual goal of keeping buffers small and utilizations high, despite propagation delays and regardless of network rates. We analyze delayed feedback schemes as a system of delay-differential equations, in which we model the queue-length process and the rate at which a source transmits data as fluids. We assume that a stream of acknowledgements carries information about the state of a bottleneck queue back to the source, which adapts its transmission rate according to any monotone function of that state. We show stability for this class of schemes, in that their rate of transmission and queue length rapidly converge to a small neighborhood of the designed operating point. We identify the appropriate scaling of the model's parameters for the system to perform optimally.

This paper gives a simple, accurate first order asymptotic analysis of the transient and steady state behavior of a network which is closed, not product-form and has multiple classes. One of the two nodes of the network is an infinite server and the discipline in the other node is discriminatory processor-sharing. Specifically, if there are nj jobs of class j at the latter node, then each class j job receives a fraction wj/(Σwini) of the processor capacity. This work has applications to data networks. For the asymptotic regime of high loading of the processor and high processing capacity, we derive the explicit first order transient behavior of the means of queue lengths. We also give explicit expressions for the steady state mean values and a simple procedure for finding the time constants (eigenvalues) that govern the approach to steady state. The results are based on an extension of Kurtz's theorem concerning the fluid limit of Markov processes. Some numerical experiments show that the analysis is quite accurate.

The steady-state behavior of open queuing systems with Poisson arrival processes in light traffic, that is, as the arrival rate tends to zero, is considered. Expressions are provided for the derivatives with respect to the arrival rate of various quantities of interest (such as moments of steady-state sojourn times and queue lengths), evaluated at an arrival rate of zero. These expressions are obtained using the regenerative structure of the queuing system and a change-of-measure formula based on likelihood ratios. The derivatives, which can be used in interpolation approximations, can be evaluated analytically in simple cases and by simulation in general.>

Analyzes the random delay experienced by a message traversing a buffered, multistage packet-switching banyan network. The authors find the generating function for the distribution of waiting time at the first stage of the network for a very general class of traffic, assuming messages have discrete sizes. For example, traffic can be uniform or nonuniform, messages can have different sizes, and messages can arrive in batches. For light-to-moderate loads, the authors conjecture that delays experienced at the various stages of the network are nearly the same and are nearly independent. This allows us to approximate the total delay distribution. Better approximations for the distribution of waiting times at later stages of the network are attained by assuming that in the limit a sort of spatial steady state is achieved. Extensive simulations confirm the formulas and conjectures.>

When using MIMD (multiple instruction, multiple data) parallel computers, one is often confronted with solving a task composed of many independent subtasks where it is necessary to synchronize the processors after all the subtasks have been completed. This paper studies how the subtasks should be allocated to the processors in order to minimize the expected time it takes to finish all the subtasks (sometimes called the makespan). We assume that the running times of the subtasks are independent, identically distributed, increasing failure rate random variables, and that assigning one or more subtasks to a processor entails some overhead, or communication time, that is independent of the number of subtasks allocated. Our analyses, which use ideas from renewal theory, reliability theory, order statistics, and the theory of large deviations, are valid for a wide class of distributions. We show that allocating an equal number of subtasks to each processor all at once has good efficiency. This appears as a consequence of a rather general theorem which shows how some consequences of the central limit theorem hold even when we cannot prove that the central limit theorem applies.

Freivalds recently reported a construction of a 2-way probabilistic finite automaton M that recognizes the set {a/sup m/b /sup m/ : m /spl ges/ 1} with arbitrarily small probability of error. This result implies that probabilistic machines of this type are more powerful than their deterministic, nondeterministic, and alternating counterparts. Freivalds' construction has a negative feature: the automation M runs in /spl Omega/ (2/sup n/2/n) expected time in the worst case on inputs of length n. We show that it is impossible to do significantly better. Specifically, no 2-way probabilistic finite automaton that runs in n/sup O (1)/ expected time recognizes {a/sup m/b/sup m/ : m /spl ges/ 1} with probability of error bounded away from 1/2. In passing we derive results on the densities of regular sets, the fine structure of Freivalds' construction, and the behavior of random walks controlled by Markov chains.

A variety of theoretical results are derived for a well-known class of discrete-time adaptive filters. First the following idealized identification problem is considered: a discrete-time system has vector inputx(t)and scalar outputz(t)= h \' x(t)wherehis an unknown time-invariant coefficient vector. The filter considered adjusts an estimate vector\hat{h}(t)in a control loop according to\hat{h}(t + \Delta t) = \hat{h}(t) + K[z(t) - \hat{z} (t)]x(t), where\hat{z}( t)= \hat{h}( t) \' x( t)andKis the control loop gain. The effectiveness of the filter is determined by the convergence properties of the misalignment vectorr(t) = h - \hat{h}(t). It is shown that a certain nondegeneracy "mixing" condition on the Input { x(t)} is necessary and sufficient for the exponential convergence of the misalignment. Qualitatively identical upper and lower bounds are derived for the rate of convergence. Situations where noise is present inz(t)andx(t)and the coefficient vectorhis time-varying are analyzed. Nonmixing inputs are also considered, and it is shown that in the idealized model the above stability results apply with only minor modifications. However, nonmixing input in conjunction with certain types of noise lead to bounded input - unbounded output, i.e., instability.

Recently, we have shown that a necessary and sufficient condition for uniform convergence in a well-known class of digital adaptive filters is that the inputs satisfy a 'mixing condition'. In the case of mixing inputs, system noise, or errors incurred in the digital implementation, with bounded size, can produce only bounded behavior of the filter. We show here that, in many applications, inputs may be expected not to mix. We analyze the effects of two specific types of implementation errors in both the mixing and nonmixing cases. We show that arbitrarily small bounds on each type of error leads to unbounded behavior of the filter if the input is nonmixing and both types of errors are present. However, if only one type of error is present, then unbounded behavior does not occur if the error is small.