Development of a novel Quality of Service and energy balancing control engine to reduce and measure the carbon emissions of distributed computing networks for high throughput computing.

Charity Engine (CE) 平台（由 WorldwideComputing Company Ltd 运营）是一种网格计算解决方案，利用剩余的“空闲时间”计算能力为全球企业和学术客户提供计算服务，以满足科学模拟或统计分析等高性能计算 (HPC) 需求。企最终用户的 QoS 约束。通过降低电力成本和二氧化碳影响，将与基础设施所有者分享利益，并通过减少运营中的二氧化碳影响与最终用户分享利益。为了实现这一目标，该项目将利用 Cognitive Networks Limited 研究取得的技术进步，该公司已在多个目标测试台中设计并实施了多个概念验证能源/QoS 平衡控制系统。该联盟将开发基于强化学习的强大 QoS 能源/平衡引擎，该引擎将集成到 CE 分布式网络中，基于实时QoS（包括网络延迟、服务器响应时间和服务器能耗）优化选择服务器节点。

The Charity Engine (CE) platform (ran by Worldwide Computing Company Ltd) is a grid computing solution that harnesses surplus 'idle time' computing power to provide computing services to both enterprise and academic customers worldwide to meet the high-performance computing (HPC) needs such as scientific simulations or statistical analyses. Corporate users pay for the service, while charitable or research institutions receive the excess computing power free of charge as a charitable contribution.There is a need for a grid computing solution that reduces, measures and reports both energy requirements and subsequent carbon emissions whilst maintaining an adequate Quality of Service (QoS) to meet customer compute demands and enables corporates to fulfil CSR reporting requirements.The aim of this project is to substantially reduce the energy consumption and CO2 emissions of the CE infrastructure by allocating tasks in a manner which minimizes the amount of energy consumed per task, while meeting the QoS constraints of the end users. The benefit will be shared with the owners of the infrastructure through reduced electricity costs and CO2 impact, and with the end users through reduced CO2 impact of their operations.To achieve this aim, this project will exploit the technical advancements achieved by research at Cognitive Networks Limited which has designed and implemented several proof-of-concept Energy/QoS balancing control systems in several target testbeds.The consortium will develop a robust QoS energy/balancing engine based on Reinforcement Learning that which will be integrated into the CE distributed network, to optimally select server nodes based on real-time QoS including network delay, server response time and server energy consumption.