This paper argues for the need to design a new processor scheduling algorithm that can handle the mix of applications we see today. We present a scheduling algorithm which we have implemented in the Solaris UNIX operating system [Eykholt et al. 1992], and demonstrate its improved performance over existing schedulers in research and practice on real applications. In particular, we have quantitatively compared against the popular weighted fair queueing and UNIX SVR4 schedulers in supporting multimedia applications in a realistic workstation environment...

. The satisfiability (SAT) problem is a core problem in mathematical logic and computing theory. In practice, SAT is fundamental in solving many problems in automated reasoning, computer-aided design, computeraided manufacturing, machine vision, database, robotics, integrated circuit design, computer architecture design, and computer network design. Traditional methods treat SAT as a discrete, constrained decision problem. In recent years, many optimization methods, parallel algorithms, and practical techniques have been developed for solving SAT. In this survey, we present a general framework (an algorithm space) that integrates existing SAT algorithms into a unified perspective. We describe sequential and parallel SAT algorithms including variable splitting, resolution, local search, global optimization, mathematical programming, and practical SAT algorithms. We give performance evaluation of some existing SAT algorithms. Finally, we provide a set of practical applications of the sat...

Important classical scheduling theory results for real-time computing are identified. Implications of these results from the perspective of a real-time systems designer are discussed. Uni-processor and multiprocessor results are addressed as well as important issues such as future release times, precedence constraints, shared resources, task value, overloads, static versus dynamic scheduling, preemption versus non-preemption, multiprocessing anomalies, and metrics. Examples of what scheduling algorithms are used in actual applications are given.

Most real-time computer-controlled systems are built in two separate steps, each in isolation: controller design and its digital implementation. Computational tasks that realize the control algorithms are usually scheduled by treating their execution times and periods as unchangeable parameters. Task scheduling therefore depends only on the limited computing resources available. On the other hand, controller design is primarily based on the continuous-time dynamics of the physical system being controlled. The set of tasks resulting from this controller design may not be schedulable with the limited computing resources available. Even if the given set of tasks is schedulable, the overall control performance may not be optimal in the sense that they do not make a full use of computing resource. In this paper, we propose an integrated approach to controller design and task scheduling. Specifically, task frequencies (or periods) are allowed to vary within a certain range as long as such a ...

This paper summarizes the state of the real-time field in the areas of scheduling and operating system kernels. Given the vast amount of work that has been done by both the operations research and computer science communities in the scheduling area, we discuss four paradigms underlying the scheduling approaches and present several exemplars of each. The four paradigms are: static table-driven scheduling, static priority preemptive scheduling, dynamic planning-based scheduling, and dynamic best efSort scheduling. In the operating system context, we argue that most of the proprietary commercial kernels as well as real-time extensions to time-sharing operating system kernels do not fit the needs of predictable real-time systems. We discuss several research kernels that are currently being built to explicitly meet the needs of real-time applications. I.

Quality of Service (QoS) control is considered an important user demand and therefore receives wide attention, especially in the areas of computer networks and real-time multimedia systems. In this paper we present an QoS management scheme that enables us to quantitatively measure QoS, and to analytically plan and allocate resource. In this model, available system resources are apportioned across multiple applications such that the net utility that accrues to the end-users of those applications is maximized. In [26, 27], we primarily work with “continuous ” QoS dimensions, and assumed that the ’utility ’ gained by improvements along a QoS dimension were always representable by concave functions. In this paper, we relax both assumptions. One, we deal with discrete set of QoS operating points. Two, we make no assumptions about the concavity of the utility functions. Using these as the basis, we tackle the problem of maximizing system utility by allocating a single finite resource to satisfy the QoS requirements of multiple applications along multiple QoS dimensions. We present two nearoptimal algorithms to solve this problem. The first yields an allocation within a known bounded distance from the optimal solution, and the second yields an allocation whose distance from the optimal solution can be explicitly controlled by the QoS manager. We compare the run-times of these near-optimal algorithms and their solution quality relative to the optimal allocation, which in turn is computed using dynamic programming. These detailed evaluations provide practical insight into which of these algorithms can be used online in real-time systems.

Many stream-based applications have sophisticated data processing requirements and real-time performance expectations that need to be met under asynchronous, time-varying data streams. In order to address these challenges, we propose novel operator scheduling approaches that specify (1) which operators to schedule (2) in which order to schedule the operators, and (3) how many tuples to process at each execution; and study them in the context of the Aurora data stream manager. We argue and provide experimental evidence that a fine-grained scheduling approach in combination with various scheduling techniques (such as batching of operators and tuples) can significantly improve the efficiency by reducing various system overheads. We also discuss application-aware extensions that address Quality of Service (QoS) issues by making scheduling decisions according to tuple processing delays and per-application QoS specifications. Finally, we present prototype-based experimental results that characterize the efficiency and effectiveness of our approaches under various stream workloads and processing scenarios. 1

With respect to on-line scheduling algorithms that must direct the serviceofsporadic task requests we quantify the benefit of clairvoyancy, i.e. the power of possessing knowledge of various task parameters of future events. Specifically, we consider the problem of preemptively scheduling sporadic task requests in both uni- and multi-processor environments. If a task request is successfully scheduledtocompletion, a value equal to the task's execution time is obtained# otherwise no value is obtained. We prove that no on-line scheduling algorithm can guarantee a cumulative value greater than 1/4th the value obtainable by a clairvoyant scheduler# i.e., we prove a 1/4th upper bound on the competitive factor of on-line real-time schedulers. We present an on-line uniprocessor scheduling algorithm TD 1 that actually has a competitive factor of 1/4# this bound is thus shown to be tight. We further consider the effect of restricting the amount of overloading permitted (the "loading factor"), and...

A real-time application is typically composed of a number of cooperating activities that must execute within specific time intervals. Since there are usually more activities to be executed than there are processors on which to execute them, several activities must share a single processor. Necessarily, satisfying the activities' timing constraints is a prime concern in making the scheduling decisions for that processor.

When hard periodic and f&-m aperiodic tasks are jointly scheduled in the same system, the processor workload can vary according to the arrival times of aperiodic requests. In order to guarantee the schedu-lability of the periodic task set, in overload conditions some aperiodic tasks must be rejected. In this paper we propose a technique that, in over-load conditions, adds robustness to the joint schedul-ing of periodic and aperiodic tasks in systems with dy-namic priorities. Our technique is based on an ape-riodic server, called Total Bandwidth server, already proven effective in a previous work. Here the algo-rithm is first extended to eficiently h,andle firm aperi-odic tasks and then integrated with a robust guarantee mechanism that allows to achieve graceful degradation in case of transient overloads. Extensive simulations show that the proposed new algorithm is effective in all workload conditions. 1