Quality of service (QoS) has been receiving wide attention in recent years in many research communities including networking, multimedia systems, real-time systems and distributed systems. In large distributed systems such as those used in defense systems, on-demand service and inter-networked systems, applications contending for system resources must satisfy timing, reliability and security constraints as well as application-specific quality requirements. Allocating sufficient resources to different applications in order to satisfy various requirements is a fundamental problem in these situations. A basic yet flexible model for performance-driven resourceallocations can thereforebe useful in making appropriate tradeoffs. In this paper, we present an analytical model for QoS management in systems which must satisfy application needs along multiple dimensions such as timeliness, reliable delivery schemes, cryptographic security and data quality. We refer to this model as Q-RAM (QoS-based Res...

AbstractÐReal-time middleware services must guarantee predictable performance under specified load and failure conditions, and ensure graceful degradation when these conditions are violated. Guaranteed predictable performance typically entails reservation of resources and use of admission control. Graceful degradation, on the other hand, requires dynamic reallocation of resources to maximize the application-perceived system utility while coping with unanticipated overload and failures. We propose a model for quality-of-service (QoS) negotiation in building real-time services to meet both of the above requirements. QoS negotiation is shown to 1) outperform ªbinaryº admission control schemes (either guaranteeing the required QoS or rejecting the service request), 2) achieve higher application-perceived system utility, and 3) deal with violations of the load and failure hypotheses. We incorporated the proposed QoS-negotiation model into an example real-time middleware service, called RTPOOL, which manages a distributed pool of shared computing resources (processors) to guarantee timeliness QoS for real-time applications. In order to guarantee timeliness QoS, the resource pool is encapsulated with its own schedulability analysis, admission control, and load-sharing support. This support differs from others in that it adheres to the proposed QoS-negotiation model. The efficacy and power of QoS negotiation are demonstrated for an automated flight control system implemented on a network of PCs running RTPOOL. This system is used to fly an F-16 fighter aircraft modeled using the Aerial Combat (ACM) F-16 Flight Simulator. Experimental results indicate that QoS negotiation, while maintaining real-time guarantees, enables graceful QoS degradation under conditions in which traditional schedulability analysis and admission control schemes fail. Index TermsÐQuality-of-service (QoS), QoS negotiation, QoS levels and rewards, schedulability analysis and admission control, automated flight systems. 1

RAM) proposed in [20] presented an analytical approach for satisfying multiple quality-of-service dimensions in a resource-constrained environment. Using this model, available system resources can be apportioned across multiple applications such that the net utility that accrues to the end-users of those applications is maximized. In this paper, we present several practical solutions to allocation problems that were beyond the limited scope of [20]. First, we show that the Q-RAM problem of finding the optimal resource allocation to satisfy multiple QoS dimensions (at least one of which is dependent on another) is NP-hard. We then present a polynomial solution for this resource allocation problem which yields a solution within a provably fixed and short distance from the optimal allocation. Secondly, [20] dealt mainly with the problem of apportioning a single resource to satisfy multiple QoS dimensions. In this paper, we study the converse problem of apportioning multiple resources to satisfy a single QoS dimension. In practice, this problem becomes complicated, since a single QoS dimension perceived by the user can be satisfied using different combinations of available resources. We show that this problem can be formulated as a mixed integer programming problem that can be solved efficiently to yield an optimal resource allocation. Finally, we also present the run-times of these optimizations to illustrate how these solutions can be applied in practice. We expect that a good understanding of these solutions will yield insights into the general problem of apportioning multiple resources to satisfy simultaneously multiple QoS dimensions of multiple concurrent applications. 1.

An increasing number of real-time applications, related to multimedia and adaptive control systems, require greater flexibility than classical real-time theory usually permits. In this paper we present a novel periodic task model, in which tasks' periods are treated as springs, with given elastic coefficients. Under this framework, periodic tasks can intentionally change their execution rate to provide different quality of service, and the other tasks can automatically adapt their periods to keep the system underloaded. The proposed model can also be used to handle overload conditions in a more flexible way, and provide a simple and efficient mechanism for controlling the quality of service of the system as a function of the current load.

An increasing number of distributed real-time systems face the critical challenge of providing end-to-end Quality of Service (QoS) guarantees in open and unpredictable environments. In particular, such systems often need to guarantee the CPU utilization on multiple processors in order to achieve overload protection and meet end-to-end deadlines while task execution times are unpredictable. While the recently developed feedback control real-time scheduling algorithms have shown promise, they cannot handle the common end-to-end task model in distributed systems where each task is comprised of a chain of subtasks distributed on multiple processors. This paper presents the End-to-end Utilization CONtrol (EUCON) algorithm that features a distributed feedback loop that dynamically enforces desired CPU utilization bounds on multiple processors based on online performance measurements EUCON is based on a model predictive control approach that models the utilization control problem on a distributed platform as a multi-variable constrained optimization problem. A multi-input-multi-output model predictive controller is designed based on a difference equation model that describes the dynamic behavior of distributed real-time systems. Both control theoretic analysis and simulations demonstrate that EUCON can provide robust utilization guarantees even when task execution times deviate from the estimation or vary significantly at run-time. Index terms—real-time and embedded systems, feedback control real-time scheduling, distributed systems, end-to-end task, Quality of Service

In this paper, we present a general scheduling methodology for managing overruns in a real-time environment, where tasks may have different criticality and flexible timing constraints. The proposed method achieves isolation among tasks through a resource reservation mechanism which bounds the effects of task interference, but also performs efficient reclaiming of the unused computation times to relax the utilization constraints imposed by isolation. The enhancements achieved by the proposed approach resulted to be very effective with respect to classical reservation schemes. The performance has been evaluated by implementing the algorithm on a real-time kernel. The runtime overhead introduced by the scheduling mechanism has also been investigated with specific experiments, in order to be taken into account in the schedulability analysis. However, it resulted to be negligible in most practical cases.

The paper presents a feedback scheduling mechanism in the context of co-design of the scheduler and the control tasks. We are particularly interested in controllers where the execution time may change abruptly between different modes, such as in hybrid controllers. The proposed solution attempts to keep the CPU utilization at a high level, avoid overload, and distribute the computing resources evenly among the tasks. The feedback scheduler is implemented as a periodic or sporadic task that assigns sampling periods to the controllers based on execution-time measurements. The controllers may also communicate feedforward mode-change information to the scheduler. As an example, we consider hybrid control of a set of double-tank processes. The system is evaluated, from both scheduling and control performance perspectives, by co-simulation of controllers, scheduler, and tanks. 1.

We present a translucent QoS management optimization framework for systems that must satisfy application needs along multiple dimensions such as timeliness, reliability, cryptographic security and other application-specific quality requirements. The architecture of the system consists of a semantically rich (in terms of customizable and expressiveness) QoS specification interface for multidimensional QoS provisioning, a quality-of-service index model to help the user make the quality trade-o# decision, and a unified QoS-based admission control and resource planning system.The semantically rich QoS specification interface allows the user or system administrator to define finegrained service requests in terms of quality or rate of service. The QoS index model is designed to be flexible and policy driven. The unified QoS-based admission and resource control facilitates the deployment of various QoS policies to meet performance objectives for specific service optimizations. Finally, the ov...

In many application areas, including control systems, careful management of system resources is key to providing the best application performance. Most traditional resource management techniques for real-time systems with multiple control loops are based on open-loop strategies that statically allocate a constant CPU share to each controller, independent of their current resource needs. This provides average control performance with minimal overhead but in general fails to provide the best performance possible within the available resources. We show that by using feedback to dynamically allocate resources to controllers as a function of the current state of their controlled systems, control performance can be significantly improved. We present an optimal resource allocation policy that maximizes control performance within the available resources and provide experimental results showing that the optimal policy 1) significantly increases control performance compared to traditional control system implementations (by more than 20% in our experiments), 2) maximizes control performance over other feedback-based policies, 3) saves resources when perturbations occur infrequently, and 4) incurs negligible overhead.

This paper presents a control theoretic approach to optimizing end-to-end timing constraints subject to the performance requirements and the schedulability constraint of a real-time control system. The control performance is specified in terms of control output responses such as steady state error, maximum overshoot, settling time, and rise time; and the end-to-end timing constraints include loop processing periods and input-to-output latency. Our approach includes a generic real-time controller model on which our analysis is performed, and a heuristic optimization algorithm which derives end-to-end timing constraints. We apply the approach to the design of an embedded real-time controller, and validate it through an experimental study using simulation. Our approach contributes to both the control and realtime areas: (1) it allows control engineers to take into consideration the effect of scheduling latency and sampling periods at the early stage of system design; and (2) it makes it ...