Closedown Time Research Articles

Modeling and performance evaluation of the sleep mode in the IEEE 802.16e wireless networks with self-similar traffic

IEEE 802.16e is currently the latest broadband wireless access standard designed to support mobility. In mobile networks, how to control energy consumption is one of the most important issues for battery-powered mobile stations. The standard proposes an energy saving mechanism named ‘sleep mode’ for conserving the power of mobile stations. According to the operation principle of the sleep mode for downlink traffic in the type I power saving class, considering the self-similar nature of massive multimedia data in wireless networks, a discrete-time batch arrival Geom X /G/1 queuing model with a close-down time and multiple vacations is built. The batch size is supposed to be Pareto distributed. By employing an embedded Markov chain method, the average queue length and the average sojourn time of the system model are derived. Correspondingly, the performance measures are obtained of the energy saving rate and the average packet delay time for the sleep mode in the IEEE 802.16e. The numerical results demonstrate the dependency relationships between the system performance measures and the system parameters with different offered loads and different self-similar degrees. Furthermore, a cost model is developed to determine the optimum length of the close-down time for minimizing the total system cost.

A Queueing Model with Start-Up/Close-Down Times and Retrial Customers

We consider a queueing system with Poisson arrivals and arbitrarily distributed service times, vacation times, and start-up and close-down times. The model accepts two types of customers—the ordinary and the retrial customers—and the server takes a single vacation each time he becomes free. For such a model the stability conditions and the system state probabilities are investigated both in a transient and in the steady state. Numerical results are finally obtained and used to observe system performance for various values of the parameters.

A Discrete-Time Single-Server Queueing System Under Multiple Vacations and Setup-Closedown Times

This article concerns a Geo/G/1/∞ queueing system under multiple vacations and setup-closedown times. Specifically, the operation of the system is as follows. After each departure leaving an empty system, the server is deactivated during a closedown time. At the end of each closedown time, if at least a customer is present in the system, the server begins the service of the customers (is reactivated) without setup; however, if the system is completely empty, the server takes a vacation. At the end of each vacation, if there is at least a customer in the system, the server requires a startup time (is reactivated) before beginning the service of the customers; nevertheless, if there are not customers waiting in the system, the server takes another vacation. By applying the supplementary variable technique, the joint generating function of the server state and the system length together with the main performance measures are derived. We also study the length of the different busy periods of the server. The stationary distributions of the time spent waiting in the queue and in the system under the FCFS discipline are analysed too. Finally, a cost model with some numerical results is presented.

Performance evaluation for the sleep mode in the IEEE 802.16e based on a queueing model with close-down time and multiple vacations

IEEE 802.16e is the latest broadband wireless access standard designed to support mobility. In mobile networks, how to control energy consumption is one of the most important issues for the battery-powered mobile stations. The standard proposes an energy saving mechanism that named for conserving the power of the mobile stations. According to the operation mechanism of the sleep mode for downlink traffic in the type I power saving class, a discrete-time Geom/G/1 queueing model with close-down time and multiple vacations is built. By employing an embedded Markov chain method and Little's law, the average queue length, the average sojourn time and the average busy cycle of the queueing model are derived. Correspondingly, we get the performance measures of the energy saving rate and the average packet delay time for the sleep mode in the IEEE 802.16e. Finally, numerical results are given to demonstrate the dependency relationships between the system performance measures and the system parameters. Furthermore, a cost model is developed to determine the optimum length of the close-down time for minimizing the total system cost.

Two thresholds of a batch arrival queueing system under modified T vacation policy with startup and closedown

AbstractThis paper studies the operating characteristics of the variant of an M[x]/G/1 vacation queue with startup and closedown times. After all the customers are served in the system exhaustively, the server shuts down (deactivates) by a closedown time, and then takes at most J vacations of constant time length T repeatedly until at least one customer is found waiting in the queue upon returning from a vacation. If at least one customer is present in the system when the server returns from a vacation, then the server reactivates and requires a startup time before providing the service. On the other hand, if no customers arrive by the end of the J th vacation, the server remains dormant in the system until at least one customer arrives. We will call the vacation policy modified T vacation policy. We derive the steady‐state probability distribution of the system size and the queue waiting time. Other system characteristics are also investigated. The long‐run average cost function per unit time is developed to determine the suitable thresholds of T and J that yield a minimum cost. Copyright © 2007 John Wiley & Sons, Ltd.

The discrete-time Geom/G/1 queue with multiple adaptive vacations and server Setup/Closedown times

In this paper, we discuss the discrete-time Geom/G/1 queue with multiple adaptive vacations and server setup/closedown times. We consider two types of closedown time policies which differ from each other in the completion instant of the closedown times and contrast the effects of them on the queueing system. We derive the probability generating functions (PGFs) and stochastic decomposition results of the steady-state queue length and waiting time distributions through the method of embedded Markov chain in both cases. Many discussed models related to Geom/G/1 queue are special cases of our model.

Batch arrival queues under vacation policies with server breakdowns and startup/closedown times

This paper studies the operating characteristics of an M [x] /G/1 queueing system under vacation policies with startup/closedown times, where the vacation time, the startup time, and the closedown time are generally distributed. When all the customers are served in the system exhaustively, the server shuts down (deactivates) by a closedown time. After shutdown, the server operates one of (1) multiple vacation policy and (2) single vacation policy. When the server reactivates since shutdown, he needs a startup time before providing the service. If a customer arrives during a closedown time, the service is immediately started without a startup time. The server may break down according to a Poisson process while working and his repair time has a general distribution. We analyze the system characteristics for the vacation models.

On M/G/1 system under NT policies with breakdowns, startup and closedown

This paper studies the vacation policies of an M/G/1 queueing system with server breakdowns, startup and closedown times, in which the length of the vacation period is controlled either by the number of arrivals during the vacation period, or by a timer. After all the customers are served in the queue exhaustively, the server is shutdown (deactivates) by a closedown time. At the end of the shutdown time, the server immediately takes a vacation and operates two different policies: (i) The server reactivates as soon as the number of arrivals in the queue reaches to a predetermined threshold N or the waiting time of the leading customer reaches T units; and (ii) The server reactivates as soon as the number of arrivals in the queue reaches to a predetermined threshold N or T time units have elapsed since the end of the closedown time. If the timer expires or the number of arrivals exceeds the threshold N, then the server reactivates and requires a startup time before providing the service until the system is empty. If some customers arrive during this closedown time, the service is immediately started without leaving for a vacation and without a startup time. We analyze the system characteristics for each scheme.

Steady state analysis of a bulk queue with multiple vacations, setup times with N-policy and closedown times

In this paper a M X / G ( a, b)/1 queueing system with multiple vacations, setup time with N-policy and closedown times is considered. On completion of a service, if the queue length is ξ, where ξ < a, then the server performs closedown work. Following closedown the server leaves for multiple vacations of random length irrespective of queue length. When the server returns from a vacation and if the queue length is still less than ‘ N’, he leaves for another vacation and so on, until he finds ‘ N’ ( N > b) customers in the queue. That is, if the server finds at least ‘ N’ customers waiting for service, then he requires a setup time ‘ R’ to start the service. After the setup he serves a batch of ‘ b’ customers, where b ⩾ a. Various characteristics of the queueing system and a cost model with the numerical solution for a particular case of the model are presented.

A vacation queue with setup and close-down times and batch Markovian arrival processes

We consider a finite-capacity single-server vacation queue with close-down/setup times and batch Markovian arrival process (BMAP), where both the service time, the vacation time, the setup time, and the close-down time are generally distributed. The queueing model has potential applications in SVC (switched virtual connection)-based IP-over-ATM networks and multiple protocol label switched (MPLS) networks. By applying the supplementary variable technique, we develop a unified solution to both the single-vacation and multiple-vacation models and for either the PBAS ( partial batch acceptance strategy) or the WBAS ( whole batch acceptance strategy) service disciplines. For both models, we obtain the queue length distribution at batch arrival epochs and that at an arbitrary time instant, the loss probability of a whole batch or an arbitrary customer in a batch, server setup rate, server utilization ratio, and the LST of the waiting time distribution. Through the numerical examples, we find that: (1) there is a trade-off between the user’s quality-of-service (e.g., loss probabilities, wanting times) and the system performance (e.g., server setup rate, server utilization ratio); (2) the system performance is closely related not only to the first and second order moments of the arrival process but also the pattern (distribution) of the customer arrivals; (3) mean batch size is a much more critical factor to influence the queueing system’s performance than the type of batch size distribution. These conclusions are of instructive meanings in the design of IP-over-ATM or more generally MPLS-based networks.

A finite-capacity queue with exhaustive vacation/close-down/setup times and Markovian arrival processes

We consider a finite-capacity single-server vacation model with close-down/setup times and Markovian arrival processes (MAP). The queueing model has potential applications in classical IP over ATM or IP switching systems, where the close-down time corresponds to an inactive timer and the setup time to the time delay to set up a switched virtual connection (SVC) by the signaling protocol. The vacation time may be considered as the time period required to release an SVC or as the time during which the server goes to set up other SVCs. By using the supplementary variable technique, we obtain the queue length distribution at an arbitrary instant, the loss probability, the setup rate, as well as the Laplace–Stieltjes transforms of both the virtual and actual waiting time distributions.