327 research outputs found
EUROPEAN CONFERENCE ON QUEUEING THEORY 2016
International audienceThis booklet contains the proceedings of the second European Conference in Queueing Theory (ECQT) that was held from the 18th to the 20th of July 2016 at the engineering school ENSEEIHT, Toulouse, France. ECQT is a biannual event where scientists and technicians in queueing theory and related areas get together to promote research, encourage interaction and exchange ideas. The spirit of the conference is to be a queueing event organized from within Europe, but open to participants from all over the world. The technical program of the 2016 edition consisted of 112 presentations organized in 29 sessions covering all trends in queueing theory, including the development of the theory, methodology advances, computational aspects and applications. Another exciting feature of ECQT2016 was the institution of the Takács Award for outstanding PhD thesis on "Queueing Theory and its Applications"
Performance modelling with adaptive hidden Markov models and discriminatory processor sharing queues
In modern computer systems, workload varies at different times and locations. It is important to model the performance of such systems via workload models that are both representative and efficient. For example, model-generated workloads represent realistic system behaviour, especially during peak times, when it is crucial to predict and address performance bottlenecks. In this thesis, we model performance, namely throughput and delay, using adaptive models and discrete queues. Hidden Markov models (HMMs) parsimoniously capture the correlation and burstiness of workloads with spatiotemporal characteristics. By adapting the batch training of standard HMMs to incremental learning, online HMMs act as benchmarks on workloads obtained from live systems (i.e. storage systems and financial markets) and reduce time complexity of the Baum-Welch algorithm. Similarly, by extending HMM capabilities to train on multiple traces simultaneously it follows that workloads of different types are modelled in parallel by a multi-input HMM. Typically, the HMM-generated traces verify the throughput and burstiness of the real data. Applications of adaptive HMMs include predicting user behaviour in social networks and performance-energy measurements in smartphone applications. Equally important is measuring system delay through response times. For example, workloads such as Internet traffic arriving at routers are affected by queueing delays. To meet quality of service needs, queueing delays must be minimised and, hence, it is important to model and predict such queueing delays in an efficient and cost-effective manner. Therefore, we propose a class of discrete, processor-sharing queues for approximating queueing delay as response time distributions, which represent service level agreements at specific spatiotemporal levels. We adapt discrete queues to model job arrivals with distributions given by a Markov-modulated Poisson process (MMPP) and served under discriminatory processor-sharing scheduling. Further, we propose a dynamic strategy of service allocation to minimise delays in UDP traffic flows whilst maximising a utility function.Open Acces
Sojourn time asymptotics in processor sharing queues
This paper addresses the sojourn time asymptotics for a GI/GI/• queue operating under the
Processor Sharing (PS) discipline with stochastically varying service rate. Our focus is on the
logarithmic estimates of the tail of sojourn-time distribution, under the assumption that the jobsize
distribution has a light tail. Whereas upper bounds on the decay rate can be derived under
fairly general conditions, the establishment of the corresponding lower bounds requires that the
service process satisfies a samplepath large-deviation principle. We show that the class of
allowed service processes includes the case where the service rate is modulated by a Markov
process. Finally, we extend our results to a similar system operation under the Discriminatory
Processor Sharing (DPS) discipline. Our analysis relies predominantly on large-deviations
techniques
Single-server queues under overdispersion in the heavy-traffic regime
This paper addresses the analysis of the queue-length process of
single-server queues under overdispersion, i.e., queues fed by an arrival
process for which the variance of the number of arrivals in a given time window
exceeds the corresponding mean. Several variants are considered, using concepts
as mixing and Markov modulation, resulting in different models with either
endogenously triggered or exogenously triggered random environments. Only in
special cases explicit expressions can be obtained, e.g. when the random
arrival and/or service rate can attain just finitely many values. While for
more general model variants exact analysis is challenging, one
derive limit theorems in the heavy-traffic regime. In some of our derivations
we rely on evaluating the relevant Laplace transform in the heavy-traffic
scaling using Taylor expansions, whereas other results are obtained by applying
the continuous mapping theorem
Queueing Systems with Heavy Tails
VI+227hlm.;24c
Upstream traffic capacity of a WDM EPON under online GATE-driven scheduling
Passive optical networks are increasingly used for access to the Internet and
it is important to understand the performance of future long-reach,
multi-channel variants. In this paper we discuss requirements on the dynamic
bandwidth allocation (DBA) algorithm used to manage the upstream resource in a
WDM EPON and propose a simple novel DBA algorithm that is considerably more
efficient than classical approaches. We demonstrate that the algorithm emulates
a multi-server polling system and derive capacity formulas that are valid for
general traffic processes. We evaluate delay performance by simulation
demonstrating the superiority of the proposed scheduler. The proposed scheduler
offers considerable flexibility and is particularly efficient in long-reach
access networks where propagation times are high
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