No abstract

SUMMARY Complex scientific experiments have a growing demand for computational resources, which are expensive to be acquired and maintained. Grid computing has emerged as the mainstream technology to solve this issue. Grids are also adequate for the execution of scientific workflows because they allow the use of heterogeneous and distributed resources. In spite of the progress in grid technology, there are challenges to overcome in workflow scheduling. For instance, centralized scheduling solutions may lead to performance degradation and to problems with scalability. Some scheduling approaches are partially distributed, keeping a few centralized components that may become bottlenecks. Other distributed solutions have a lack of flexibility in the definition of workflows, in which only the use of tasks as steps in the workflow is permitted not high level services. In this work, we present P2PScheMe, a scheduling mechanism for peer‐to‐peer execution of workflows based on the invocation of grid services. The proposal considers information regarding grid execution environment in order to allow workflow scheduling adaptation. This adaptation is performed according to user requirements of quality of service. In this paper, we describe how P2PScheMe works and provides a comparative analysis with existing solutions. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, we propose a novel architecture to allow the implementation of a cyber environment composed of different High Performance Computing (HPC) infrastructures (i.e., clusters, grids and clouds). To access this cyber environment, scientific researchers do not have to become computer experts. In particular, we assume that scientific researchers provide a description of the problem as an input to the cyber environment and then get their results without being responsible for managing the computational resources. We provide a prototype of the architecture and introduce an evaluation which studies a real workload of scientific applications executions. The results show the advantages of the proposed architecture. Besides, we highlight this work provides guidelines for developing cyber environments focused on e-Science.

Cloud computing offers virtually unlimited set of resources and flexibility to allocate them through elasticity. But cloud limitations, such as the complexity of configuration and environment dynamicity, may jeopardizes the assurance of QoS requirements. HPC Shelf is a cloud of HPC services that employs a component-oriented architecture to describe hardware and software resources of parallel computing systems. We design a framework for HPC Shelf that employ cloud elasticity concepts for keeping the values of QoS metrics of parallel computing systems inside an acceptable range, enabling adaptations to fulfill the QoS contract restrictions. In our evaluation, using a linear algebra application, we show how HPC Shelf takes advantage of cloud elasticity to reinforce QoS requirements, rectifying assumptions from ill-defined QoS models.