A Comparative Analysis of Adaptive Solutions for Grid Environments

Grid computing environments are distributed systems composed by heterogeneous and geographically distributed resources. This type of systems mainly emerged to satisfy the increasing computing power demand within the scientific community. Despite the advantages of such paradigm, there are still several challenges related to the discovery, monitoring and selection of grid resources. Moreover, the dynamic nature and changing characteristics of such environments worsen the applications performance. Thus, improving their efficiency is a fundamental issue. The present contribution analyses two self-adaptive solutions focused on enhancing the grid resource selection process by using resources in an efficient way. On the one hand, the Efficient Resources Selection model which is defined from the user’s point of view (it avoids controlling or modifying the infrastructure) and it is based on the Scatter Search method for achieving a suitable selection of resources. On the other hand, Montera2, a framework designed for addressing an efficient execution of distributed applications on the grid; it defines and employs a dynamic scheduling algorithm to determine the size and number of tasks to be executed. Both approaches have been tested on a real European infrastructure belonging to the well-known European Grid Infrastructure (EGI) project. The study also compares both solutions with the standard scheduling technique that governs this infrastructure, the gLiteWMS scheduler, showing a much better performance by reducing the final makespan by a factor of 20 if compared to the gLiteWMS scheduler. An analysis of task and time overheads for both approaches is also included. Furthermore, comparisons with many other solutions proposed in the literature are presented, showing the advantages of our approaches.