  Modern large-scale date centres, such as those used for cloud computing
service provision, are becoming ever-larger as the operators of those data
centres seek to maximise the benefits from economies of scale. With these
increases in size comes a growth in system complexity, which is usually
problematic. There is an increased desire for automated "self-star"
configuration, management, and failure-recovery of the data-centre
infrastructure, but many traditional techniques scale much worse than linearly
as the number of nodes to be managed increases. As the number of nodes in a
median-sized data-centre looks set to increase by two or three orders of
magnitude in coming decades, it seems reasonable to attempt to explore and
understand the scaling properties of the data-centre middleware before such
data-centres are constructed. In [1] we presented SPECI, a simulator that
predicts aspects of large-scale data-centre middleware performance,
concentrating on the influence of status changes such as policy updates or
routine node failures. [...]. In [1] we used a first-approximation assumption
that such subscriptions are distributed wholly at random across the data
centre. In this present paper, we explore the effects of introducing more
realistic constraints to the structure of the internal network of
subscriptions. We contrast the original results [...] exploring the effects of
making the data-centre's subscription network have a regular lattice-like
structure, and also semi-random network structures resulting from parameterised
network generation functions that create "small-world" and "scale-free"
networks. We show that for distributed middleware topologies, the structure and
distribution of tasks carried out in the data centre can significantly
influence the performance overhead imposed by the middleware.

Distributed, Parallel, and Cluster Computing