Science Gateways provide portals for experiments execution, regardless of the users' computational background. Nowadays its construction and performance need enhancement in terms of resource provision and task scheduling. We present the Modular Distributed Architecture to support the Protein Structure Prediction (MDAPSP), a Service‐Oriented Architecture for management and construction of Science Gateways, with resource provisioning on a heterogeneous environment. The Decision Maker, central module of MDAPSP, defines the best computational environment according to experiment parameters. The proof of concept for MDAPSP is presented in WorkflowSim, with two novel schedulers. Our results demonstrate good Quality of Service (QoS), capable of correctly distributing the workload, fair response times, providing load balance, and overall system improvement. The study case relies on PSP algorithms and the Galaxy framework, with monitoring experiments to show the bottlenecks and critical aspects.

中文翻译：

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基于面向服务范式的科学网关生态系统计算资源管理优化

无论用户的计算背景如何，科学网关都为实验执行提供了门户。现在它的构建和性能需要在资源提供和任务调度方面进行提升。我们提出了模块化分布式架构来支持蛋白质结构预测 (MDAPSP)，这是一种面向服务的架构，用于管理和构建科学网关，在异构环境中提供资源。MDAPSP的核心模块Decision Maker根据实验参数定义最佳计算环境。MDAPSP 的概念证明在 WorkflowSim 中提供，带有两个新颖的调度程序。我们的结果证明了良好的服务质量 (QoS)，能够正确分配工作负载，公平的响应时间，提供负载平衡，和整体系统改进。研究案例依赖于 PSP 算法和 Galaxy 框架，并通过监控实验来显示瓶颈和关键方面。