A Tale of Two C’s: Convergence and Composability

I. Altintas
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Abstract

Cyberinfrastructure is everywhere in diverse forms in service of applications in science, business and society. From IoT to extreme-scale computing data and computing have never been so distributed with the potential for real-time integration into these applications. The common theme to these applications, mostly composed of (big) data-integrated workloads, is their need to run in specialized environments for reasons such as on-demand or 24x7 nature of the tasks they are performing, and difficulties regarding their portability, latency, privacy, and performance optimization. Moreover, in many data-driven scientific applications, there is a need for heterogeneous integration of tasks requiring specialized computing capabilities with traditional high-throughput computing or high-performance computing tasks. Although some key middleware technologies enabled demonstration of standalone heterogeneous applications, such integration requires expertise convergence from a large group of people in very specialized settings. There are still many challenges for streamlined, scalable, repeatable, responsible, and explainable integration of data-integrated applications. Key opportunities for further innovations include intelligent systems and automated workflow management software that can compose and steer dynamic applications that can adapt to changing conditions in a data-driven fashion while integrating many tools to explore, analyze and utilize data. This talk will discuss some examples for data-integrated applications, describe emerging systems that enabled these applications, and overview our recent research to enable composable applications including a convergence application development methodology, intelligent middleware, and workflow composition.
两个C的故事:收敛和可组合性
网络基础设施无处不在,以各种形式服务于科学、商业和社会的应用。从物联网到极端规模的计算,数据和计算从未如此分散,具有实时集成到这些应用程序中的潜力。这些应用程序主要由(大)数据集成工作负载组成,它们的共同主题是需要在专门的环境中运行,原因包括它们正在执行的任务的随需应变或24x7性质,以及它们的可移植性、延迟、隐私和性能优化方面的困难。此外,在许多数据驱动的科学应用程序中,需要将需要专门计算能力的任务与传统的高吞吐量计算或高性能计算任务进行异构集成。尽管一些关键的中间件技术支持独立的异构应用程序的演示,但是这种集成需要在非常专业的环境中汇集大量人员的专业知识。对于数据集成应用程序的流线型、可伸缩、可重复、负责任和可解释的集成,仍然存在许多挑战。进一步创新的关键机会包括智能系统和自动化工作流管理软件,它们可以组合和引导动态应用程序,这些应用程序可以以数据驱动的方式适应不断变化的条件,同时集成许多工具来探索、分析和利用数据。本讲座将讨论一些数据集成应用的例子,描述支持这些应用的新兴系统,并概述我们最近在支持可组合应用方面的研究,包括融合应用开发方法、智能中间件和工作流组合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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