Werner R. W. Scheinhardt

In-Degree and PageRank of web pages: why do they follow similar power laws?

PageRank is a popularity measure designed by Google to rank Web pages. Experiments confirm that PageRank values obey a power law with the same exponent as In-Degree values. This paper presents a novel mathematical model that explains this phenomenon. The relation between PageRank and In-Degree is modeled through a stochastic equation, which is inspired by the original definition of PageRank, and is analogous to the well-known distributional identity for the busy period in the M/G/1 queue. Further, we employ the theory of regular variation and Tauberian theorems to prove analytically that the tail distributions of PageRank and In-Degree differ only by a multiplicative constant, for which we derive a closed-form expression. Our analytical results are in good agreement with experimental data.

A feedback fluid queue with two congestion control thresholds

Feedback fluid queues play an important role in modeling congestion control mechanisms for packet networks. In this paper we present and analyze a fluid queue with a feedback-based traffic rate adaptation scheme which uses two thresholds. The higher threshold B1 is used to signal the beginning of congestion while the lower threshold B2 signals the end of congestion. These two parameters together allow to make the trade-off between maximizing throughput performance and minimizing delay. The difference between the two thresholds helps to control the amount of feedback signals sent to the traffic source. In our model the input source can behave like either of two Markov fluid processes. The first applies as long as the upper threshold B1 has not been hit from below. As soon as that happens, the traffic source adapts and switches to the second process, until B2 (smaller than B1) is hit from above. We analyze the model by setting up the Kolmogorov forward equations, then solving the corresponding balance equations using a spectral expansion, and finally identifying sufficient constraints to solve for the unknowns in the solution. In particular, our analysis yields expressions for the stationary distribution of the buffer occupancy, the buffer delay distribution, and the throughput.

A TANDEM FLUID QUEUE WITH GRADUAL INPUT

For a two-node tandem fluid model with gradual input, we compute the joint steady-state buffer-content distribution. Our proof exploits martingale methods developed by Kella \& Whitt. For the case of finite buffers, we use an insightful sample-path argument to find a proportionality result.

Probabilistic Relation between In-Degree and PageRank

This paper presents a novel stochastic model that explains the relation between power laws of In-Degree and PageRank. PageRank is a popularity measure designed by Google to rank Web pages. We model the relation between PageRank and In-Degree through a stochastic equation, which is inspired by the original definition of PageRank. Using the theory of regular variation and Tauberian theorems, we prove that the tail distributions of PageRank and In-Degree differ only by a multiplicative constant, for which we derive a closed-form expression. Our analytical results are in good agreement with Web data. Categories and Subject Descriptors H.3.3:[ Information Storage and Retrieval ]: Information Search and Retrieval--- Retrieval models; G.3:[ Mathematics of Computing ]: Probability and statistics --- Stochastic processes, Distribution functions

Characterization of Tail Dependence for In-Degree and PageRank

The dependencies between power law parameters such as in-degree and PageRank, can be characterized by the so-called angular measure, a notion used in extreme value theory to describe the dependency between very large values of coordinates of a random vector. Basing on an analytical stochastic model, we argue that the angular measure for in-degree and personalized PageRank is concentrated in two points. This corresponds to the two main factors for high ranking: large in-degree and a high rank of one of the ancestors. Furthermore, we can formally establish the relative importance of these two factors.

Hypothesis testing for rare-event simulation : limitations and possibilities

One of the main applications of probabilistic model checking is to decide whether the probability of a property of interest is above or below a threshold. Using statistical model checking (SMC), this is done using a combination of stochastic simulation and statistical hypothesis testing. When the probability of interest is very small, one may need to resort to rare-event simulation techniques, in particular importance sampling (IS). However, IS simulation does not yield 0/1-outcomes, as assumed by the hypothesis tests commonly used in SMC, but likelihood ratios that are typically close to zero, but which may also take large values. In this paper we consider two possible ways of combining IS and SMC. One involves a classical IS-scheme from the rare-event simulation literature that yields likelihood ratios with bounded support when applied to a certain (nontrivial) class of models. The other involves a particular hypothesis testing scheme that does not require a-priori knowledge about the samples, only that their variance is estimated well.

Tandem queue with server slow-down

We study how rare events happen in the standard two-node tandem Jackson queue and in a generalization, the socalled slow-down network, see [2]. In the latter model the service rate of the first server depends on the number of jobs in the second queue: the first server slows down if the amount of jobs in the second queue is above some threshold and returns to its normal speed when the number of jobs in the second queue is below the threshold. This property protects the second queue, which has a finite capacity B, from overflow. In fact this type of overflow is precisely the rare event we are interested in. More precisely, consider the probability of overflow in the second queue before the entire system becomes empty. The starting position of the two queues may be any state in which at least one job is present.

Path-ZVA: General, Efficient, and Automated Importance Sampling for Highly Reliable Markovian Systems

We introduce Path-ZVA: an efficient simulation technique for estimating the probability of reaching a rare goal state before a regeneration state in a (discrete-time) Markov chain. Standard Monte Carlo simulation techniques do not work well for rare events, so we use importance sampling; i.e., we change the probability measure governing the Markov chain such that transitions “towards” the goal state become more likely. To do this, we need an idea of distance to the goal state, so some level of knowledge of the Markov chain is required. In this article, we use graph analysis to obtain this knowledge. In particular, we focus on knowledge of the shortest paths (in terms of “rare” transitions) to the goal state. We show that only a subset of the (possibly huge) state space needs to be considered. This is effective when the high dependability of the system is primarily due to high component reliability, but less so when it is due to high redundancies. For several models, we compare our results to well-known importance sampling methods from the literature and demonstrate the large potential gains of our method.

Stationary distributions for a class of Markov-modulated tandem fluid queues

ABSTRACT We consider a model consisting of two fluid queues driven by the same background continuous-time Markov chain, such that the rates of change of the fluid in the second queue depend on whether the first queue is empty or not: when the first queue is nonempty, the content of the second queue increases, and when the first queue is empty, the content of the second queue decreases. We analyze the stationary distribution of this tandem model using operator-analytic methods. The various densities (or Laplace–Stieltjes transforms thereof) and probability masses involved in this stationary distribution are expressed in terms of the stationary distribution of some embedded process. To find the latter from the (known) transition kernel, we propose a numerical procedure based on discretization and truncation. For some examples we show the method works well, although its performance is clearly affected by the quality of these approximations, both in terms of accuracy and run time.

Shot-noise fluid queues and infinite-server systems with batch arrivals

Abstract We show how a shot-noise fluid queue can be considered as the limiting case of a sequence of infinite-server queues with batch arrivals. The shot-noise queue we consider receives fluid amounts at the arrival times of a (time-inhomogeneous) Poisson process, the sizes of which are governed by some probability distribution that may also depend on time. The continuous rate at which fluid leaves the queue is proportional to the current content of the queue. Thus, intuitively, one can think of drops of fluid arriving in batches, which are taken into service immediately upon arrival, at an exponential service rate. We show how to obtain the partial differential equation for (the Laplace–Stieltjes transform of) the queue content at time t , as well as its solution, from the corresponding infinite-server systems by taking appropriate limits. Also, for the special case of a time-homogeneous arrival process, we show that the scaled number of occupied servers in the infinite-server system converges as a process to the shot-noise queue content, implying that finite-dimensional distributions also converge.