低熵云计算系统

Low-entropy cloud computing systems

当前的云计算系统,不论是虚拟化云还是分区云,难以同时满足用户体验和系统效率需求,产业界和学术界都开始研究下一代云计算系统以应对这个难题.本文指出,这个难题的一个重要原因是计算系统熵(无序、干扰和不确定性)居高不下,并归纳了云计算系统中存在的4类无序现象.本文提出了低熵云计算系统的学术概念,刻画了其主要特点,比较了低熵云计算系统与虚拟化云和分区云在用户体验、开发效率、运行效率、资源适配方面的区别,并讨论了低熵云的新概念和新技术:(1)不同于图灵可计算性和算法可计算性的实用可计算性概念,形式化地刻画了云计算行业的"用户体验差的功能是不存在的功能"的实践经验;(2)刻画云计算系统能够实现实用可计算性的充分必要条件,即DIP猜想;(3)支持DIP猜想,即能够区分、隔离、优先化计算任务相空间,从而降低干扰,有潜力同时满足用户体验和系统效率需求的标签化von Neumann体系结构;(4)适配深度学习负载与神经网络处理器的云计算协同设计技术.

Current cloud computing systems, whether virtualization clouds or partitioned clouds, face the challenge of simultaneously satisfying user experience and system efficiency requirements. Both the industry and the academia are investigating next-generation cloud computing systems to address this problem. This paper points out a main cause of this problem： existing cloud systems have high computing system entropy（i.e., disorder and uncertainty）, which manifest as four classes of disorders. We propose a new concept of ＂low-entropy cloud computing systems＂, and contrast them to virtualization clouds and partitioned clouds, in terms of user experience,application development efficiency, execution efficiency, and resource matching. We discuss four new features and techniques of low-entropy clouds：（1） a notion of production computability that, unlike Turing computability and algorithmic tractability, formalizes the user experience requirements of cloud computing practices;（2） a conjecture, named the DIP（differentiation, isolation, prioritization） conjecture, that tries to capture the necessary and sufficient conditions for a cloud computing system to realize production computability;（3） the labeled von Neumann architecture that has the potential to support the DIP capabilities and thus simultaneously satisfy user experience and system efficiency requirements; and（4） a co-design technique allowing a cloud computing system to adaptively match deep-learning workloads to neural network accelerator hardware.