  Cloud computing refers to maximizing efficiency by sharing computational and
storage resources, while data-parallel systems exploit the resources available
in the cloud to perform parallel transformations over large amounts of data. In
the same line, considerable emphasis has been recently given to two apparently
disjoint research topics: data-parallel, and eventually consistent, distributed
systems. Declarative networking has been recently proposed to ease the task of
programming in the cloud, by allowing the programmer to express only the
desired result and leave the implementation details to the responsibility of
the run-time system. In this context, we propose a study on a
logic-programming-based computational model for eventually consistent,
data-parallel systems, the keystone of which is provided by the recent finding
that the class of programs that can be computed in an eventually consistent,
coordination-free way is that of monotonic programs. This principle is called
CALM and has been proven by Ameloot et al. for distributed, asynchronous
settings. We advocate that CALM should be employed as a basic theoretical tool
also for data-parallel systems, wherein computation usually proceeds
synchronously in rounds and where communication is assumed to be reliable. It
is general opinion that coordination-freedom can be seen as a major
discriminant factor. In this work we make the case that the current form of
CALM does not hold in general for data-parallel systems, and show how, using
novel techniques, the satisfiability of the CALM principle can still be
obtained although just for the subclass of programs called connected monotonic
queries. We complete the study with considerations on the relationships between
our model and the one employed by Ameloot et al., showing that our techniques
subsume the latter when the synchronization constraints imposed on the system
are loosened.
