A Paradigm for Scalable Open Real-Time Computing Under Uncertainty

Abdelzaher CCR-0208769《不确定性下可扩展开放实时计算的范例》嵌入式实时计算的一个关键挑战是提供时间性能保证。遗憾的是，迄今在嵌入式系统的性能保证领域发展起来的丰富知识仅限于一个有一定限制的应用领域，在这个领域中，假定有关于系统可用资源能力和个别任务的资源需求的详细知识。这些限制阻止了许多以前的研究结果被应用到更广泛的主流应用和服务中，在这些主流应用和服务中，需要Qos保证，但负载和资源模型不可用。本研究寻求解决在缺乏详细的负载和资源知识的情况下提供性能保证的问题。目标是确定即使在没有系统负载和资源容量的细粒度模型的情况下，也可以通过实时系统性能来实现细粒度的保证。这是通过在不确定性下运行的嵌入式实时系统中的性能保证的新基础来实现的。这项研究的中心是一种新的演算，旨在应对当前性能保证方法在健壮性和可伸缩性方面的根本限制。1)基于可行域的鲁棒可调度性分析理论：可行域是保证满足所有时间约束的系统状态的集合。这项研究正在开发在一个连续的状态空间中获得多维可行域的方法，其中的维度表示诸如不同系统资源的总体利用率等聚合的可测量量。将系统维持在可行区域边界内可确保仅基于聚集的时间正确性。这些机制将更具可伸缩性，并且适用于无法获得有关负载和资源的详细信息的系统。2)强制运行时系统符合其可行域的中间件组件。正在开发的理论框架被整合到一个基于控制理论的中间件框架中，该框架执行运行时性能监控和反馈控制机制，以确保系统状态收敛到可行域。这两个元素通过将应用程序与中间件相关联、指定所需的服务质量保证以及利用运行时可行区域实施机制来在开放实时系统中提供正确的时间行为来维护对实时行为的保证。这将嵌入式计算的范围从主要封闭的定制设计系统扩大到由商业现成组件(如Web服务器、主流操作系统和标准协议如TCP/IP)组成的大型分布式开放系统，在这些系统中，准确的加载和资源知识是不可用的。通过在许多重要系统中实现可预测行为的能力，从大型Web服务器场和互联网路由器到无处不在的计算系统和智能空间，预计将产生巨大影响。

Abdelzaher CCR-0208769" A Paradigm for Scalable Open Real-Time Computing Under Uncertainty"A key challenge for embedded real-time computing is that of providing temporal performance guarantees. Unfortunately, the wealth of knowledge developed to date in the area of performance assurances in embeddedsystems has been confined to a somewhat restrictive application domain where detailed knowledge is assumed of both the available resource capacity in the system and the resource requirements of individual tasks. These restrictions prevent many previous research results from being applied to a wider scope of mainstream applications and services where QoS guarantees are required, yet load and resource models are unavailable. This research seeks a solution to the problem of providing performance guarantees in the absence of detailed load and resource knowledge. The goal is to establish that fine-grained guarantees are achievable with real-time system performance even in the absence of fine-grained models of system load and resource capacity. This is approached through new foundations for performance guarantees in embedded real-time systems operating under uncertainty. The research centers on a new calculus aimed to counter fundamental limitations on robustness and scalability in current approaches for performance guarantees. There are two main elements: 1) A theory for robust schedulability analysis based on feasible regions: A feasible region is a set of aggregate system states in which all timing constraints are guaranteed to be met. This research is developing methods for deriving multi-dimensional feasible regions in a continuous state space, where the dimensions represent aggregate measurable quantities such as the overall utilization of different system resources. Maintaining a system within feasible region boundaries guarantees temporal correctness based on aggregates only. These mechanisms will be more scalable and suitable for systems where detailed information about the load and resources is unavailable. 2) Middleware components that enforce conformance of a run-time system to its feasible region. The theoretical framework being developed is incorporated into a middleware framework based on control theory, which executes run-time performance monitoring and feedback control mechanisms to ensure that system state converges to a feasible region. This condition is enforced using admission control and QoS adaptation.These two elements maintain guarantees on real-time behavior by linking applications with the middleware, specifying desired QoS guarantees, and leveraging run-time feasible region enforcement mechanisms to provide correct temporal behavior in in open real-time systems. This increases the scope of embedded computing from predominantly closed custom-designed systems to large distributed open systems composed of commercial off-the-shelf components such as web servers, mainstream operating systems, and standard protocols such as TCP/IP, where accurate load and resource knowledge is unavailable. High impact is expected through the ability to achieve predictable behavior in many important systems ranging from large Web server farms and Internet routers to ubiquitous computing systems, and smart spaces.