Assigning a single server to inhomogeneous queues with switching costs

AbstractIn this paper we study the preemptive assignment of a single server to two queues. Customers arrive at both queues according to Poisson processes, and all service times are exponential, but with rates depending on the queues. The costs to be minimized consist of both holding costs and switching costs. The limiting behavior of the switching curve is studied, resulting in a good threshold policy. Numerical results are included to illustrate the complexity of the optimal policy and to compare the optimal policy with the threshold policy

The effect of service time variability on maximum queue lengths in M^X/G/1 queues

We study the impact of service-time distributions on the distribution of the maximum queue length during a busy period for the M^X/G/1 queue. The maximum queue length is an important random variable to understand when designing the buffer size for finite buffer (M/G/1/n) systems. We show the somewhat surprising result that for three variations of the preemptive LCFS discipline, the maximum queue length during a busy period is smaller when service times are more variable (in the convex sense).Comment: 12 page

Resource allocation in grid computing

Grid computing, in which a network of computers is integrated to create a very fast virtual computer, is becoming ever more prevalent. Examples include the TeraGrid and Planet-lab.org, as well as applications on the existing Internet that take advantage of unused computing and storage capacity of idle desktop machines, such as Kazaa, SETI@home, Climateprediction.net, and Einstein@home. Grid computing permits a network of computers to act as a very fast virtual computer. With many alternative computers available, each with varying extra capacity, and each of which may connect or disconnect from the grid at any time, it may make sense to send the same task to more than one computer. The application can then use the output of whichever computer finishes the task first. Thus, the important issue of the dynamic assignment of tasks to individual computers is complicated in grid computing by the option of assigning multiple copies of the same task to different computers. We show that under fairly mild and often reasonable conditions, maximizing task replication stochastically maximizes the number of task completions by any time. That is, it is better to do the same task on as many computers as possible, rather than assigning different tasks to individual computers. We show maximal task replication is optimal when tasks have identical size and processing times have a NWU (New Worse than Used; defined later) distribution. Computers may be heterogeneous and their speeds may vary randomly, as is the case in grid computing environments. We also show that maximal task replication, along with a c μ rule, stochastically maximizes the successful task completion process when task processing times are exponential and depend on both the task and computer, and tasks have different probabilities of completing successfully

Stochastic scheduling games with Markov Decision Arrival Processes

AbstractIn Hordijk and Koole [1,2], a new type of arrival process, the Markov Decision Arrival Process (MDAP), was introduced, which can be used to model certain dependencies between arrival streams and the system at which the arrivals occur. This arrival process was used to solve control problems with several controllers having a common objective, where the output from one controlled node is fed into a second one, as in tandems of multi-server queues. In the case that objectives of the controllers are different, one may choose a min-max (worst case) approach where typically a controller tries to obtain the best performance under the worst possible (unknown) strategies of the other controllers. We use the MDAP to model such situations, or situations of control in an unknown environment. We apply this approach to several scheduling problems, including scheduling of customers and scheduling of servers. We consider different information patterns including delayed information. For all these models, we obtain several structural results of the optimal policies

On the pathwise optimal Bernoulli routing policy for homogeneous parallel servers

A long-standing conjecture on the optimal Bernoulli routing policy is proven to be true. For the case of equal exponential service times it is shown that splitting equally among the queues minimizes the departure times in a stochastic pathwise sense. A new technique is used, showing that certain distributional properties related to Schur convexity propagate forward in time

An adaptive priority policy for radiotherapy scheduling

In radiotherapy, treatment needs to be delivered in time. Long waiting times can result in patient anxiety and growth of tumors. They are often caused by inefficient use of radiotherapy equipment, the linear accelerators (LINACs). However, making an efficient schedule is very challenging, especially when we have multiple types of patients, having different service requirements and waiting time constraints. Moreover, in radiotherapy a patient needs to go through a LINAC multiple times over multiple days, to complete the treatment. In this paper we model the radiotherapy treatment process as a queueing system with multiple queues, and we propose a new class of scheduling policies that are simple, flexible and fair to patients. Numerical experiments show that our new policy outperforms the commonly used policies. We also extend the policy to an adaptive one to deal with unknown and fluctuating arrival rates. Our adaptive policy turns out to be quite efficient in absorbing the effects caused by these changes. Due to the complexity of our problem, we select the parameters of the policies through simulation-based optimization heuristics. Our work may also have important implications for managers in other service systems such as call centers