The effect of variability in the GI/G/s queue

We consider the stationary waiting time distribution under the FIFO discipline for multi-server queues with (possibly heterogeneous) phase type servers and renewal arrivals, and for queues which can be represented as a quasi-birth-and-death process. It is shown that the complementary distribution function of the stationary waiting time in these queues is of the form where θ is positive and the dj are probabilities. A complete characterization of θ and dj , which lends itself to interesting probabilistic interpretations, is given for all these cases. Except in the case of the multiserver GI/PH/c queue which is to be discussed in detail elsewhere, we provide an efficient algorithm to compute the waiting time distribution.

Mechanistic models for herbivore populations responding to rainfall-driven pasture are used to explore the effect of temporal variability in a primary resource on the abundance and distribution of a species. If the numerical response of the herbivore to pasture is a convex function, then gains made over time intervals with above average rainfall do not compensate for losses incurred when rainfall is below average. Populations therefore fare worse when rainfall is variable compared with when rainfall is reliable. It is demonstrated that this result is independent of the distribution of rainfall. Sensitivity of a species to variability, and hence the limit to its distribution in variable environments, is directly proportional to the difference between population growth rate under ideal conditions and the estimated rate of decline as the species' resource tends to zero. When density dependence is included in the numerical response, the average abundance of a species declines with increasing variability in its primary resource. However, a model for the dynamics of pasture and rabbits (Oryctolagus cuniculus) and red foxes (Vulpes vulpes) in southern Australia, is used to illustrate that trophic interactions can reverse the effect of variability: in the absence of foxes, the mean abundance of rabbits declines with variability as expected, but in the full model the mean abundance of rabbits increases.

We consider an m server system in which each server can service at most one update packet at a time. The system designer controls (1) scheduling - the order in which the packets get serviced, (2) routing - the server that an arriving update packet joins for service, and (3) the service time distribution with fixed service rate. Given a fixed update generation process, we prove a strong age-delay and age-delay variance tradeoff, wherein, as the average AoI approaches its minimum, the packet delay and its variance approach infinity. In order to prove this result, we consider two special cases of the m server system, namely, a single server system with last come first served with preemptive service and an infinite server system. In both these cases, we derive sufficient conditions to show that three heavy tailed service time distributions, namely Pareto, log-normal, and Weibull, asymptotically minimize the average AoI as their tail gets heavier, and establish the age-delay tradeoff results. We provide an intuitive explanation as to why such a seemingly counter intuitive age-delay tradeoff is natural, and that it should exist in many systems.

short-paper Share on Tail asymptotics of the stationary distribution for a two-node generalized Jackson network Author: Masakiyo Miyazawa Tokyo University of Science, Noda, Chiba, Japan Tokyo University of Science, Noda, Chiba, JapanView Profile Authors Info & Claims ACM SIGMETRICS Performance Evaluation ReviewVolume 42Issue 2September 2014pp 70–72https://doi.org/10.1145/2667522.2667545Published:04 September 2014Publication History 1citation26DownloadsMetricsTotal Citations1Total Downloads26Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Heuristic and evolutionary algorithms are proposed to solve challenging real-world optimization problems. In the evolutionary community, many benchmark problems for empirical evaluations of algorithms have been proposed. One of the most important classes of test problems is the class of convex functions, particularly the d-dimensional sphere function. However, the convex function type is somewhat limited. In principle, one can select a set of convex basis functions and use operations that preserve convexity to generate a family of convex functions. This method will inevitably introduce bias in favor of the basis functions. In this paper, the problem is solved by employing insights from computational geometry, which gives the first-ever general-purpose multi-dimensional convex landscape generator. The new proposed generator has the advantage of being generic, which means that it has no bias toward a specific analytical function. A set of N random d-dimensional points is generated for the construction of a d-dimensional convex hull. The upper part of the convex hull is removed by considering the normal of the polygons. The remaining part defines a convex function. It is shown that the complexity of constructing the function is O(Md3), where M is the number of polygons of the convex function. For the method to work as a benchmark function, queries of an arbitrary (d−1) dimensional input are generated, and the generator has to return the value of the convex function. The complexity of answering the query is O(Md). The convexity of the function from the generator is verified with a nonconvex ratio test. The performance of the generator is also evaluated using the Broyden–Fletcher–Goldfarb–Shanno (BFGS) gradient descent algorithm. The source code of the generator is available.

We apply supervised learning to a general problem in queueing theory: using a neural net, we develop a fast and accurate predictor of the stationary system-length distribution of a GI/GI/1 queue—a fundamental queueing model for which no analytical solutions are available. To this end, we must overcome three main challenges: (i) generating a large library of training instances that cover a wide range of arbitrary interarrival and service time distributions, (ii) labeling the training instances, and (iii) providing continuous arrival and service distributions as inputs to the neural net. To overcome (i), we develop an algorithm to sample phase-type interarrival and service time distributions with complex transition structures. We demonstrate that our distribution-generating algorithm indeed covers a wide range of possible positive-valued distributions. For (ii), we label our training instances via quasi-birth-and-death(QBD) that was used to approximate PH/PH/1 (with phase-type arrival and service process) as labels for the training data. For (iii), we find that using only the first five moments of both the interarrival and service times distribution as inputs is sufficient to train the neural net. Our empirical results show that our neural model can estimate the stationary behavior of the GI/GI/1—far exceeding other available methods in terms of both accuracy and runtimes. History: Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: O. Baron received financial support from the Natural Sciences and Engineering Research Council of Canada (NERC) [Grant 458051]. D. Krass received financial support from the NERC [Grant 458395]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0263 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0263 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

A discrete time single-line queueing system with independent geometric streams of regular and negative claims, infinite buffer, and geometric service is considered. A negative claim pushes a regular claim out of the buffer queue and moves it to a bunker of infinite capacity. If the buffer is empty, a negative claim leaves the system without any effect. After servicing a claim, the system receives the next claim from the buffer, if it is not empty, or from the bunker. We obtain relations for stationary waiting and sojourn time distributions of the regular claim.

The paper considers scheduling jobs online to minimize the objective $$\sum _{i \in [n]}w_ig(C_i-r_i)$$ , where $$w_i$$ is the weight of job i, $$r_i$$ is its release time, $$C_i$$ is its completion time and g is any non-decreasing convex function. It is known that the clairvoyant algorithm Highest-Density-First (HDF) is $$(2+\epsilon )$$ -speed O(1)-competitive for this objective on a single machine for any fixed $$ 0< \epsilon < 1$$ (Im et al., in: ACM-SIAM symposium on discrete algorithms, pp 1254–1265, 2012). In this paper, we give the first non-trivial results for this problem when g is a non-decreasing convex function and the algorithm must be non-clairvoyant. More specifically, our results include: The paper gives the first non-trivial upper-bound on multiple machines even if the algorithm is allowed to be clairvoyant. All performance guarantees above hold for all non-decreasing convex functions gsimultaneously. The positive results are supplemented by almost matching lower bounds. We show that any algorithm that is oblivious to g is not O(1)-competitive with speed augmentation less than 2 on a single machine. Further, any non-clairvoyent algorithm that knows the function g cannot be O(1)-competitive with speed augmentation less than $$\sqrt{2}$$ on a single machine or $$(2-\frac{1}{m})$$ on m identical machines.

A queue with Poisson arrivals is served by N identical exponential servers. When a server becomes free, a group of customers of size at most b can be served. The server is not allowed to process a group of size less than a, 1 ≤ a ≤ b. The actual size of each group served depends on the number of waiting customers. The queue is studied as a Markov process. In the stable case, the stationary probability vector of this process has a simple, readily computable form. Using this form, the stationary waiting time distribution, which is analytically intractable, may be expressed in an algorithmically useful form. Several questions, related to the optimal design of such a service system, may be algorithmically investigated. This model serves to illustrate the advantages of the algorithmic approach. The design criteria are not analytically tractable in general.

In this paper, a single-server queue with negative customers is considered. The arrival of a negative customer will remove one positive customer that is being served, if any is present. An alternative approach will be introduced to derive a set of equations which will be solved to obtain the stationary queue length distribution. We assume that the service time distribution tends to a constant asymptotic rate when time t goes to infinity. This assumption will allow for finding the stationary queue length of queueing systems with non-exponential service time distributions. Numerical examples for gamma distributed service time with fractional value of shape parameter will be presented in which the steady-state distribution of queue length with such service time distributions may not be easily computed by most of the existing analytical methods.

We present in this paper a mathematical method based on stochastic comparisons of Markov chains in order to compute in a mobile network performance measures that are important Q parameters for users: the dropping handover of voice and the blocking probability of a new voice call. The key idea of this methodology is that given a complex system represented by a multi-dimensional Markov chain which is too large to be solved, we propose to reduce the state space, and so to define a new Markov chain which is a simplified version of the original one. This reduced Markov chain is defined as an aggregated one, which represents a stochastic bound for performance measures written as increasing reward function on the stationary distribution. The main steps of the construction of the aggregated Markov chain applied into mobile networks are presented in this paper. As the number of mobile users with different kinds of applications increases, the associated model is more complex and it can be represented by multi-dimensional continuous time Markov chain with a very large size. Thus, we define an aggregated Markov chain represented by a multi-dimensional birth and death process which is very easy to solve. We have proved that there is a weak ordering between the original Markov chain and the aggregated one using increasing set formalism. We have computed upper bounds of dropping handover and blocking probability for different values of input parameters. Numerical results prove that upper bounds give good results and so the stochastic methodology is an interesting mathematical tool for the performance evaluation of complex systems. Keywords: Mobile Networks, Quality of Service, Stochastic ordering, stochastic comparisons, Continuous Time Markov Chains.

A convex function defined on an open convex set is known to be continuous at every&#13;\npoint of this set. In actuality, a convex function has a strengthened continuity property.&#13;\nIn this paper, we introduce the notion of strong continuity and demonstrate that a convex&#13;\nfunction possesses this property. The proof is based only on the definition of convexity and&#13;\nthe Jensen’s inequality. A distinct constant (constant of strong continuity) is included in&#13;\nthe definition of strong continuity. In the article, we give an unimprovable value for this&#13;\nconstant in the case of convex functions. The constant of strong continuity depends, in&#13;\nparticular, on the form of the norm introduced in the space of the arguments of a convex&#13;\nfunction. Polyhedral norm is of particular interest. With its use the constant of strong&#13;\ncontinuity can be easily calculated. This requires a finite number of values of the convex&#13;\nfunction.

This paper is devoted to perturbation analysis of the stationary distribution of waiting times in the G/G/1 queue with a parameter-dependent service time distribution. We provide sufficient conditions under which the stationary distribution is Lipschitz continuous and we explicitly compute the Lipschitz constant. Thereby, we provide bounds on the effect of a (finite) perturbation of the service time distribution on the stationary waiting time. The case of infinitesimal perturbations (read, derivatives) is treated as well.

We consider a multiserver queue in the Halfin-Whitt regime: as the number of serversngrows without a bound, the utilization approaches 1 from below at the rateAssuming that the service time distribution is lattice valued with a finite support, we characterize the limiting scaled stationary queue length distribution in terms of the stationary distribution of an explicitly constructed Markov chain. Furthermore, we obtain an explicit expression for the critical exponent for the moment generating function of a limiting stationary queue length. This exponent has a compact representation in terms of three parameters: the amount of spare capacity and the coefficients of variation of interarrival and service times. Interestingly, it matches an analogous exponent corresponding to a single-server queue in the conventional heavy-traffic regime.

Insensitive light traffic in a cellular system with mobile subscribers