Age-Delay Tradeoffs in Queueing Systems

This paper investigates a queuing system with infinite number of servers where the arrival process is given by a Markov Arrival Process (MAP) and the service time follows a Phase-type (PH) distribution. They were chosen since they are simple enough to describe the model by exact methods. Moreover, highly correlated arrival processes and heavy-tailed service time distributions can be approximated by these tools on a wide range of time-scales. The transient behaviour of the system is analysed and the time-dependent moments of the queue length is computed explicitly by solving a set of differential equations. The results can be applied to models where performance of parallel processing is important. The applicability of the model is illustrated by dimensioning a WEB-based content provider.

This article deals with the actual problem of study the effectiveness of economic, information and social processes of train station of ticket sales system. In this paper we present the model of the queuing system at “St. Pölten Hauptbahnhof” and compare the waiting times at the ticket counters for one to six employees and the waiting times for one, four and six vending machines. We compare a four server system with one shared queue with four single server systems with separate queues. We concluded that four vending machines might be too less right now, but as there is a backwards trend for buying the tickets at vending machines and we expect the percentages to go down even more in the future as more and more people are able to buy their tickets online or on their phones. Therefore, in the future four vending machines will be enough.KeywordsEconomic processesSocial processesInformation processesTicket sales systemDigital transformationTechnology

The ever-growing demand for more computing power forces hardware vendors to put an increasing number of multiprocessors into a single server system, which usually exhibits a non-uniform memory access (NUMA). In-memory database systems running on NUMA platforms face several issues such as the increased latency and the decreased bandwidth when accessing remote main memory. To cope with these NUMA-related issues, a DBMS has to allow flexible data partitioning and data placement at runtime. In this demonstration, we present ERIS, our NUMA-aware in-memory storage engine. ERIS uses an adaptive partitioning approach that exploits the topology of the underlying NUMA platform and significantly reduces NUMA-related issues. We demonstrate throughput numbers and hardware performance counter evaluations of ERIS and a NUMA-unaware index for different workloads and configurations. All experiments are conducted on a standard server system as well as on a system consisting of 64 multiprocessors, 512 cores, and 8 TBs main memory.

In this paper we analyze a single removable and unreliable server in the N policy M/G/1 queueing system in which the server breaks down according to a Poisson process and the repair time obeys an arbitrary distribution. The method of maximum entropy is used to develop the approximate steady-state probability distributions of the queue length in the M/G(G)/1 queueing system, where the second and the third symbols denote service time and repair time distributions, respectively. A study of the derived approximate results, compared to the exact results for the M/M(M)/1, M/E2(E3)/1, M/H2(H3)/1 and M/D(D)/1 queueing systems, suggest that the maximum entropy principle provides a useful method for solving complex queueing systems. Based on the simulation results, we demonstrate that the N policy M/G(G)/1 queueing model is sufficiently robust to the variations of service time and repair time distributions.

This dissertation examines an M[x]/G/1 queueing system with a randomized vacation policy and at most J vacations. Whenever the system is empty, the server immediately takes a vacation. If there is at least one customer found waiting in the queue upon returning from a vacation, the server will be immediately activated for service. Otherwise, if no customers are waiting for service at the end of a vacation, the server either remains idle with probability p or leaves for another vacation with probability 1-p. This pattern continues until the number of vacations taken reaches J. If the system is empty by the end of the Jth vacation, the server becomes idle in the system until at least one customer waiting in the queue. In this dissertation, we investigate the following three queueing systems: Reliable server queueing system, un-reliable server queueing system and un-reliable server with a delayed repair queueing system. For the three systems considered in our dissertation, using the supplementary technique, we develop the system size distribution as well as other important system characteristics, such as the system size distribution at busy period initiation epoch, the queue size distribution at a departure epoch, and the distributions of busy period and idle period, etc. Further, for the un-reliable server we also develop main reliability indices of the presented model. Using the renewal reward theorem, a cost model is constructed to determine the optimal randomized vacation policy. Based on the cost model, a heuristic approach is provided to search the joint optimum values of p and J. Some numerical results are presented for illustrative purpose. Our study presents an extension of the existing vacation queueing model and the analysis of the proposed model will provide a useful performance evaluation tool for more general situations arising in real word.

In 1969 H. and D. Stoyan showed that the stationary waiting-time distribution in a GI/G/1 queue increases in the ordering determined by the expected value of all non-decreasing convex functions when the interarrival-time and service-time distributions become more variable, as expressed in the ordering determined by the expected value of all convex functions. Ross (1978) and Wolff (1977) showed by counterexample that this conclusion does not extend to all GI/G/s queues. Here it is shown that this conclusion does hold for all GI/G/s queues for several other measures of congestion which coincide with the waiting time in single-server systems. One such alternate measure of congestion is the clearing time, the time required after the arrival epoch of the nth customer for the system to serve all customers in the system at that time, excluding the nth customer. The stochastic comparisons also imply an ordering for the expected waiting times in M/G/s queues.

For monitoring network traffic, there is an enormous cost in collecting, storing, and analyzing network traffic datasets. Data mining based network traffic analysis has a growing interest in the cyber security community, but is computationally expensive for finding correlations between attributes in massive network traffic datasets. To lower the cost and reduce computational complexity, it is desirable to perform feasible statistical processing on effective reduced datasets instead of on the original full datasets. Because of the dynamic behavior of network traffic, traffic traces exhibit mixtures of heavy tailed statistical distributions or overdispersion. Heavy tailed network traffic characterization and visualization are important and essential tasks to measure network performance for the Quality of Services. However, heavy tailed distributions are limited in their ability to characterize real-time network traffic due to the difficulty of parameter estimation. The Entropy-Based Heavy Tailed Distribution Transformation (EHTDT) was developed to convert the heavy tailed distribution into a transformed distribution to find the linear approximation. The EHTDT linearization has the advantage of being amenable to characterize and aggregate overdispersion of network traffic in realtime. Results of applying the EHTDT for innovative visual analytics to real network traffic data are presented.

On the M/G/1 queueing system with multiclass customers and fixed feedback

It is well known that the stationary distribution of the number of busy servers in the Erlang blocking system (M/G/c/c) depends on the service-time distribution only through its mean. This insensitivity property is shared by several other queueing systems. In this paper, we give simple sufficient conditions for determining if this insensitivity property holds for general queueing systems and related stochastic models. The conditions involve determining whether the solution of the stationary Markovian flow equations also solves certain restricted flow equations. The proof that these conditions are sufficient is direct and elementary.

It is well known that the stationary distribution of the number of busy servers in the Erlang blocking system (M/G/c/c) depends on the service-time distribution only through its mean. This insensitivity property is shared by several other queueing systems. In this paper, we give simple sufficient conditions for determining if this insensitivity property holds for general queueing systems and related stochastic models. The conditions involve determining whether the solution of the stationary Markovian flow equations also solves certain restricted flow equations. The proof that these conditions are sufficient is direct and elementary.

We analyzed an M/M/R queueing system with finite capacity N where customers have multiple classes with no priorities under steady-state conditions. It is assumed that any one class of arriving customer is serviced by one or more servers, and that each customer class has equal probability of service. Analytic explicit solutions are obtained with the assumption of exponential distributions for arrival times and service times. A cost model is developed to determine the optimal system capacity and the optimal number of servers. The minimum expected cost, the optimal system capacity, the optimal number of servers, and various system performance measures are obtained for three customer classes and four customer classes, based on assumed numerical values given to the designated system parameters.

This paper investigates the near-memoryless behavior of the service time for IEEE 802.11 saturated single-hop ad hoc networks. We show that the number of packets successfully transmitted by any node over a time interval follows a general distribution, which is close to a Poisson distribution with an upper bounded distribution distance. We also show that the service time distribution can be approximated by a geometric distribution. We illustrate that the usage of discrete-time queuing analysis (M/Geo/1) near network saturation greatly simplifies the queuing analysis and leads to sufficiently accurate results for both the first order statistics and the probability distribution of the number of packets in the queuing system.

The paper aims at busy period analysis of non-Markovian queuing system GI/G/1 starting initially with i0 customers, through lattice path approach. Both interarrival and service time distributions are approximated by two-phase Cox distributions, C2, that have Markovian property, amenable to the application of lattice paths combinatorial analysis. Distributions having rational Laplace?Stieltjes transform and square coefficient of variation lying in [1/2∞) form a very wide class of distributions. As any distribution of this class can be approximated by C2, therefore, the use of C2 has led us to achieve results applicable to almost any real life queuing system GI/G/1 occurring in computer systems, communication systems, manufacturing systems, etc. Numerical computations have been performed for different sets of values of the parameters involved using software package Mathematica and represented graphically.

The paper presents some numerical results on modelling a M/G/1/∞ queuing system using Markov chain Monte Carlo (MCMC). This particular technique was chosen in order to draw samples from the service time distribution from which it is assumed complicated to sample random numbers. The software for experiments was created using C++ Builder development environment.

Sensitivity analysis for output performance measures in long-range dependent queueing system