4 research outputs found
QD-AMVA: Evaluating Systems with Queue-Dependent Service Requirements
AbstractWorkload measurements in enterprise systems often lead to observe a dependence between the number of requests running at a resource and their mean service requirements. However, multiclass performance models that feature these dependences are challenging to analyze, a fact that discourages practitioners from characterizing workload dependences. We here focus on closed multiclass queueing networks and introduce QD-AMVA, the first approximate mean-value analysis (AMVA) algorithm that can efficiently and robustly analyze queue-dependent service times in a multiclass setting. A key feature of QD-AMVA is that it operates on mean values, avoiding the computation of state probabilities. This property is an innovative result for state-dependent models, which increases the computational efficiency and numerical robustness of their evaluation. Extensive validation on random examples, a cloud load-balancing case study and comparison with a fluid method and an existing AMVA approximation prove that QD-AMVA is efficient, robust and easy to apply, thus enhancing the tractability of queue-dependent models
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Generalised analytic queueing network models. The need, creation, development and validation of mathematical and computational tools for the construction of analytic queueing network models capturing more critical system behaviour.
Modelling is an important technique in the comprehension and
management of complex systems. Queueing network models capture
most relevant information from computer system and network
behaviour. The construction and resolution of these models is
constrained by many factors. Approximations contain detail lost
for exact solution and/or provide results at lower cost than
simulation.
Information at the resource and interactive command level is
gathered with monitors under ULTRIX'. Validation studies indicate
central processor service times are highly variable on the
system. More pessimistic predictions assuming this variability
are in part verified by observation.
The utility of the Generalised Exponential (GE) as a
distribution parameterised by mean and variance is explored.
Small networks of GE service centres can be solved exactly using
methods proposed for Generalised Stochastic Petri Nets. For two
centre. systems of GE type a new technique simplifying the balance equations is developed. A very efficient "building bglloocbka"l.
is presented for exactly solving two centre systems with service
or transfer blocking, Bernoulli feedback and load dependent rate,
multiple GE servers. In the tandem finite buffer algorithm the
building block illustrates problems encountered modelling high
variability in blocking networks. ':
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A parametric validation study is made of approximations for
single class closed networks of First-Come-First-Served (FCFS)
centres with general service times. The multiserver extension
using the building block is validated. Finally the Maximum
Entropy approximation is extended to FCFS centres with multiple
chains and implemented with computationally efficient
convolution