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Cloud computing refers to maximizing efficiency by sharing computational andstorage resources, while data-parallel systems exploit the resources availablein the cloud to perform parallel transformations over large amounts of data. Inthe same line, considerable emphasis has been recently given to two apparentlydisjoint research topics: data-parallel, and eventually consistent, distributedsystems. Declarative networking has been recently proposed to ease the task ofprogramming in the cloud, by allowing the programmer to express only thedesired result and leave the implementation details to the responsibility ofthe run-time system. In this context, we propose a study on alogic-programming-based computational model for eventually consistent,data-parallel systems, the keystone of which is provided by the recent findingthat the class of programs that can be computed in an eventually consistent,coordination-free way is that of monotonic programs. This principle is calledCALM and has been proven by Ameloot et al. for distributed, asynchronoussettings. We advocate that CALM should be employed as a basic theoretical toolalso for data-parallel systems, wherein computation usually proceedssynchronously in rounds and where communication is assumed to be reliable. Itis general opinion that coordination-freedom can be seen as a majordiscriminant factor. In this work we make the case that the current form ofCALM does not hold in general for data-parallel systems, and show how, usingnovel techniques, the satisfiability of the CALM principle can still beobtained although just for the subclass of programs called connected monotonicqueries. We complete the study with considerations on the relationships betweenour model and the one employed by Ameloot et al., showing that our techniquessubsume the latter when the synchronization constraints imposed on the systemare loosened.

A Datalog-based Computational Model for Coordination-free, Data-Parallel Systems