634 research outputs found
Is Tail-Optimal Scheduling Possible?
This paper focuses on the competitive analysis of scheduling disciplines in a large deviations setting. Although there are policies that are known to optimize the sojourn time tail under a large class of heavy-tailed job sizes (e.g., processor sharing and shortest remaining processing time) and there are policies known to optimize the sojourn time tail in the case of light-tailed job sizes (e.g., first come first served), no policies are known that can optimize the sojourn time tail across both light- and heavy-tailed job size distributions. We prove that no such work-conserving, nonanticipatory, nonlearning policy exists, and thus that a policy must learn (or know) the job size distribution in order to optimize the sojourn time tail
Regenerative Simulation for Queueing Networks with Exponential or Heavier Tail Arrival Distributions
Multiclass open queueing networks find wide applications in communication,
computer and fabrication networks. Often one is interested in steady-state
performance measures associated with these networks. Conceptually, under mild
conditions, a regenerative structure exists in multiclass networks, making them
amenable to regenerative simulation for estimating the steady-state performance
measures. However, typically, identification of a regenerative structure in
these networks is difficult. A well known exception is when all the
interarrival times are exponentially distributed, where the instants
corresponding to customer arrivals to an empty network constitute a
regenerative structure. In this paper, we consider networks where the
interarrival times are generally distributed but have exponential or heavier
tails. We show that these distributions can be decomposed into a mixture of
sums of independent random variables such that at least one of the components
is exponentially distributed. This allows an easily implementable embedded
regenerative structure in the Markov process. We show that under mild
conditions on the network primitives, the regenerative mean and standard
deviation estimators are consistent and satisfy a joint central limit theorem
useful for constructing asymptotically valid confidence intervals. We also show
that amongst all such interarrival time decompositions, the one with the
largest mean exponential component minimizes the asymptotic variance of the
standard deviation estimator.Comment: A preliminary version of this paper will appear in Proceedings of
Winter Simulation Conference, Washington, DC, 201
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Optimal Scheduling in a Queue with Differentiated Impatient Users
We consider a M/M/1 queue in which the average reward for servicing a job is an exponentially decaying function of the jobâs sojourn time. The maximum reward and mean service times of a job are i.i.d. and chosen from arbitrary distributions. The scheduler is assumed to know the maximum reward, service rate, and age of each job. We prove that the scheduling policy that maximizes average reward serves the customer with the highest product of potential reward and service rate
On the optimal admission schedule of a finite population to a queue
Includes bibliographical references (p. 16-17).Supported by the NSF. ECS-8552419Daniel C. Lee, John N. Tsitsiklis
Multiclass queueing systems in heavy traffic: an asymptotic approach based on distributional and conservation laws
We propose a new approach to analyze multiclass queueing systems in heavy traffic based on what we consider as fundamental laws in queueing systems, namely distributional and conservation laws. Methodologically, we extend the distributional laws from single class queueing systems to multiple classes and combine them with conservation laws to find the heavy traffic behavior of the following systems: a)EGI/G/1 queue under FIFO, b) EGI/G/1 queue with priorities, c) Polling systems with general arrival distributions. Compared with traditional heavy traffic analysis via Brownian processes, our approach gives more insight to the asymptotics used, solves systems that traditional heavy traffic theory has not fully addressed, and more importantly leads to closed form answers, which compared to simulation are very accurate even for moderate traffic
Heavy traffic limit for a processor sharing queue with soft deadlines
This paper considers a GI/GI/1 processor sharing queue in which jobs have
soft deadlines. At each point in time, the collection of residual service times
and deadlines is modeled using a random counting measure on the right
half-plane. The limit of this measure valued process is obtained under
diffusion scaling and heavy traffic conditions and is characterized as a
deterministic function of the limiting queue length process. As special cases,
one obtains diffusion approximations for the lead time profile and the profile
of times in queue. One also obtains a snapshot principle for sojourn times.Comment: Published at http://dx.doi.org/10.1214/105051607000000014 in the
Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute
of Mathematical Statistics (http://www.imstat.org
Asymptotic buffer overflow probabilities in multiclass multiplexers : part II : the GLQF policy
Cover title.Includes bibliographical references (p. 33-35).Supported by a Presidential Young Investigator Award. DDM-9158118 Matching funds from Draper Laboratory. Supported by ARO. DAAL-03-92-G-0115Dimitris Bertsimas, Ioannis Ch. Paschalidis, John N. Tsitsiklis
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