1,011 research outputs found
A Greedy Link Scheduler for Wireless Networks with Fading Channels
We consider the problem of link scheduling for wireless networks with fading
channels, where the link rates are varying with time. Due to the high
computational complexity of the throughput optimal scheduler, we provide a low
complexity greedy link scheduler GFS, with provable performance guarantees. We
show that the performance of our greedy scheduler can be analyzed using the
Local Pooling Factor (LPF) of a network graph, which has been previously used
to characterize the stability of the Greedy Maximal Scheduling (GMS) policy for
networks with static channels. We conjecture that the performance of GFS is a
lower bound on the performance of GMS for wireless networks with fading
channel
Minimizing the Age of Information in Wireless Networks with Stochastic Arrivals
We consider a wireless network with a base station serving multiple traffic
streams to different destinations. Packets from each stream arrive to the base
station according to a stochastic process and are enqueued in a separate (per
stream) queue. The queueing discipline controls which packet within each queue
is available for transmission. The base station decides, at every time t, which
stream to serve to the corresponding destination. The goal of scheduling
decisions is to keep the information at the destinations fresh. Information
freshness is captured by the Age of Information (AoI) metric.
In this paper, we derive a lower bound on the AoI performance achievable by
any given network operating under any queueing discipline. Then, we consider
three common queueing disciplines and develop both an Optimal Stationary
Randomized policy and a Max-Weight policy under each discipline. Our approach
allows us to evaluate the combined impact of the stochastic arrivals, queueing
discipline and scheduling policy on AoI. We evaluate the AoI performance both
analytically and using simulations. Numerical results show that the performance
of the Max-Weight policy is close to the analytical lower bound
On deciding stability of multiclass queueing networks under buffer priority scheduling policies
One of the basic properties of a queueing network is stability. Roughly
speaking, it is the property that the total number of jobs in the network
remains bounded as a function of time. One of the key questions related to the
stability issue is how to determine the exact conditions under which a given
queueing network operating under a given scheduling policy remains stable.
While there was much initial progress in addressing this question, most of the
results obtained were partial at best and so the complete characterization of
stable queueing networks is still lacking. In this paper, we resolve this open
problem, albeit in a somewhat unexpected way. We show that characterizing
stable queueing networks is an algorithmically undecidable problem for the case
of nonpreemptive static buffer priority scheduling policies and deterministic
interarrival and service times. Thus, no constructive characterization of
stable queueing networks operating under this class of policies is possible.
The result is established for queueing networks with finite and infinite buffer
sizes and possibly zero service times, although we conjecture that it also
holds in the case of models with only infinite buffers and nonzero service
times. Our approach extends an earlier related work [Math. Oper. Res. 27 (2002)
272--293] and uses the so-called counter machine device as a reduction tool.Comment: Published in at http://dx.doi.org/10.1214/09-AAP597 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Probabilistic Inference in Queueing Networks
Although queueing models have long been used to model the performance of computer systems, they are out of favor with practitioners, because they have a reputation for requiring unrealistic distributional assumptions. In fact, these distributional assumptions are used mainly to facilitate analytic approximations such as asymptotics and large-deviations bounds. In this paper, we analyze queueing networks from the probabilistic modeling perspective, applying inference methods from graphical models that afford significantly more modeling flexibility. In particular, we present a Gibbs sampler and stochastic EM algorithm for networks of M/M/1 FIFO queues. As an application of this technique, we localize performance problems in distributed systems from incomplete system trace data. On both synthetic networks and an actual distributed Web application, the model accurately recovers the systemâs service time using 1 % of the available trace data.
Delay Jitter Bounds and Packet Scale Rate Guarantee for Expedited Forwarding
We consider the definition of the Expedited Forwarding Per-Hop Behaviour (EF PHB) as given in RFC 2598, and its impact on worst case end-to-end delay jitter. On one hand, the definition in RFC 2598 can be used to predict extremely low end-to-end delay jitter, independent of the network scale. On the other hand, we find that the worst case delay jitter can be made arbitrarily large, while each flow traverses at most a specified number of hops, if we allow networks to become arbitrarily large, this is in contradiction with the previous statement. We analyze where the contradiction originates, and find the explanation. It resides in the fact that the definition in RFC 2598 is not easily implementable in schedulers we know of, mainly because it is not formal enough, and also because it does not contain an error term. We propose a new definition for the EF PHB, called ``Packet Scale Rate Guarante
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