The growing number of scientific computation-intensive applications calls for an efficient utilization of large-scale, potentially interoperable distributed infrastructures. Parameter sweep applications represent a large body of workflows. While the principle of workflows is easy to conceive, their execution is very complex and no universally accepted solution exists. In this paper we focus on the resource allocation challenges of parameter study jobs in distributed computing infrastructures. To cope with this NP-hard problem and the high uncertainty present in these systems, we propose a series of job allocation models that helps refining and simplifying the problem complexity. In this way we present some special cases that are polynomial and show how more complex scenarios can be reduced to these models. It is known from practice that a small number of job sizes improves the result of job allocation, therefore we state a hypothesis relying on this fact in one of our models. Unfortunately, the reduction of the general problem (using K-means clustering) did not help, and thus the hypothesis has proved to be false. In the future, we shall look for clustering techniques which fit this goal better.
In an earlier joint work by Bacso et al. REF we examined the resource allocation challenges of parameter study jobs in distributed computing infrastructures, and proposed a series of job allocation models that helps refining and simplifying the problem complexity.
A Set of Successive Job Allocation Models in Distributed Computing Infrastructures