A Paradigm for Scalable Open Real-Time Computing Under Uncertainty

Abdelzaher CCR-0208769”A Paradigm for Scalable Open Real-Time Computing Under Uncertainty“嵌入式实时计算的一个关键挑战是提供时间性能保证。不幸的是，丰富的知识开发到目前为止，在该领域的性能保证在embeddedsystems已被限制到一个有点限制性的应用领域，其中详细的知识是假设在系统中的可用资源容量和个别任务的资源要求。这些限制阻止了许多以前的研究成果被应用到更广泛的主流应用和服务的QoS保证是必需的，但负载和资源模型是不可用的。本研究旨在解决在没有详细的负载和资源知识的情况下提供性能保证的问题。 我们的目标是建立细粒度的保证，即使在没有细粒度的系统负载和资源容量模型的实时系统性能是可以实现的。这是接近通过新的基础，在不确定性下运行的嵌入式实时系统的性能保证。该研究集中在一个新的演算，旨在解决目前的性能保证方法中对鲁棒性和可扩展性的基本限制。 有两个主要要素：1）基于可行域的鲁棒可重构性分析理论：可行域是保证满足所有时序约束的聚合系统状态的集合。本研究正在开发用于在连续状态空间中导出多维可行区域的方法，其中维度表示聚合的可测量量，例如不同系统资源的总体利用率。 在可行区域边界内保持系统仅基于聚集来保证时间正确性。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)中间件组件，强制运行时系统符合其可行域。正在开发的理论框架被纳入一个基于控制理论的中间件框架，它执行运行时的性能监控和反馈控制机制，以确保系统状态收敛到一个可行的区域。这两个元素通过将应用程序与中间件连接起来，指定所需的QoS保证，并利用运行时可行区域执行机制来提供正确的时间行为，从而保持对实时行为的保证。 这增加了嵌入式计算的范围，从主要是封闭的定制设计系统到大型分布式开放系统，包括商业现成组件，如Web服务器，主流操作系统和标准协议，如TCP/IP，其中准确的负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，预计会产生很大的影响，这些系统包括大型Web服务器群和互联网路由器，以及无处不在的计算系统和智能空间。

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.