Parallel Execution of Space-Aware Applications in a Cloud Environment

This paper analyzes and evaluates the strategies and implications related to the execution of parallel algorithms on a distributed Cloud infrastructure, with the focus on an important class of applications for which the execution is performed on spatial data, dislocated on a bidimensional territory. Applications of interest cover a wide spectrum ranging from Internet of Things to social sciences, geology, swarm-inspired computation etc. The territory is partitioned into regions, and regions are assigned to parallel computational nodes to speed up the execution. Parallel nodes are aligned through the exchange of messages in order to ensure a coherent and efficient execution. The paper offers an analysis of the parallelization cost in this context, especially in terms of communication overhead, which is essential to estimate the impact of porting the computation onto a Cloud environment. More in particular, the paper evaluates two different strategies for space partitioning, i.e., linear partitioning and bidimensional partitioning, with a specific focus on scalability analysis, and compares the two strategies when both options are exploitable.