This paper discusses strategies to the grid resource allocation in order to reduce energy consumption in a global perspective. We have implemented three green strategies: HGreen, GGreen and BOTEN. The former two strategies aim at reducing energy and were evaluated by simulation, whilst the latter intends simultaneously decreasing both energy and time.

Grid computing represents the main solution to integrate distributed and heterogeneous resources in global scale. However, the infrastructure necessary for maintaining a global grid in production is huge. Such fact has led to excessive power consumption. On the other hand, most green strategies for data centers are DVS (Dynamic Voltage Scaling)-based and become difficult to implement them in global grids. This paper proposes the HGreen heuristic (Heavier Tasks on Maximum Green Resource) and defines a workflow scheduling algorithm in order to implement it on global grids. HGreen algorithm aims to prioritize energy-efficient resources and explores workflow application profiles. Simulation results have shown that the proposed algorithm can significantly reduce the power consumption in global grids.

SUMMARY One of the main advantages of using a scientific workflow management system (SWfMS) is to orchestrate data flows among scientific activities and register provenance of the whole workflow execution. Nevertheless, the execution control of distributed activities in high performance computing environments by SWfMS presents challenges such as steering control and provenance gathering. Such challenges may become a complex task to be accomplished in bioinformatics experiments, particularly in Many Task Computing scenarios. This paper presents a data parallelism solution for a bioinformatics experiment supported by Hydra, a middleware that bridges SWfMS and high performance computing to enable workflow parallelization with provenance gathering. Hydra Many Task Computing parallelization strategies can be registered and reused. Using Hydra, provenance may also be uniformly gathered. We have evaluated Hydra using an Orthologous Gene Identification workflow. Experimental results show that a systematic approach for distributing parallel activities is viable, sparing scientist time and diminishing operational errors, with the additional benefits of distributed provenance support. Copyright © 2011 John Wiley & Sons, Ltd.

Large scale bioinformatics experiments are usually composed by a set of data flows generated by a chain of activities (programs or services) that may be modeled as scientific workflows. Current Scientific Workflow Management Systems (SWfMS) are used to orchestrate these workflows to control and monitor the whole execution. It is very common in bioinformatics experiments to process very large datasets. In this way, data parallelism is a common approach used to increase performance and reduce overall execution time. However, most of current SWfMS still lack on supporting parallel executions in high performance computing (HPC) environments. Additionally keeping track of provenance data in distributed environments is still an open, yet important problem. Recently, Hydra middleware was proposed to bridge the gap between the SWfMS and the HPC environment, by providing a transparent way for scientists to parallelize workflow executions while capturing distributed provenance. This paper analyzes data parallelism scenarios in bioinformatics domain and presents an extension to Hydra middleware through a specific cartridge that promotes data parallelism in bioinformatics workflows. Experimental results using workflows with BLAST show performance gains with the additional benefits of distributed provenance support.