AI 中文总结
本文提出认知连续体数字孪生（CCDS）的数学基础，通过状态空间表示与多阶段随机规划结合，实现跨设施工作流在作业启动前的部署优化，并量化成本、时间和能耗权衡。
AI 中文摘要
我们提出了认知连续体数字孪生（CCDS）的数学基础，这是一个位于用户与跨设施基础设施（包括仪器、网络、数据存储和计算中心）之间的决策支持层，用于百亿亿次及后百亿亿次科学工作流。CCDS将连续体的状态空间表示与多阶段随机规划相结合，使得在作业启动之前就能探索和优化部署场景。这使得操作员和用户能够在资源可用性不确定的情况下量化工作流的成本、完工时间和能耗权衡，并据此对冲他们的决策。我们将底层优化问题建模为一个多模式、资源受限的随机供应链网络设计问题，并在一个跨异构HPC和数据中心资源调度的真实基因组学工作流上进行了演示。这是三篇论文中的第一篇；第二篇讨论底层软件架构，第三篇报告大规模用例。
英文摘要
We present the mathematical foundations of a \emph{Cognitive Continuum Digital Shadow} (CCDS), a decision-support layer between users and the cross-facility infrastructure---instruments, networks, data stores and compute centers---of exascale and post-exascale scientific workflows. The CCDS couples a state-space representation of the continuum with multistage stochastic programming, so that deployment scenarios can be explored and optimized \emph{before} jobs are launched. This allows operators and users to quantify the cost, makespan and energy trade-offs of a workflow under uncertain resource availability, and hedge their decisions accordingly. We formulate the underlying optimization as a multimode, resource-constrained, stochastic supply-chain network design problem and demonstrate it on a realistic genomics workflow scheduled across heterogeneous HPC and data-center resources. This is the first of three papers; the second treats the underlying software architecture and the third reports large-scale use-cases.
