John P. Lehoczky

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AbstmctA direct application of commonly used synchronization primitives such as semaphores, monitors, or the Ada rendezvous can lead to uncontrolled priority inversion, a situation in which a higher priority job is blocked by lower priority jobs for an indefinite period of time. In this paper, we investigate two protocols belonging to the class of priority(More)
deadlines all of the time if the rate monotonic algorithm is used and the total utilization is not greater than .693. This paper presents an exact characterization of the ability of the rate monotonic scheduling algorithm to meet the Liu and Layland [3] also found that the optimal dynamic deadlines of a periodic task set. In addition, a stochastic(More)
A real-time system consists of both aperiodic and periodic tasks. Periodic tasks have regular arrival times and hard deadlines. Aperiodic tasks have irregular arrival times and either soft or hard deadlines. In this article, we present a new algorithm, the Sporadic Server algorithm, which greatly improves response times for soft deadline aperiodic tasks and(More)
The potential speedup of applications has motivated the widespread use of multiprocessors in recent years. Several mechanisms exist to synchronize tasks that execute on different processors, but share dota and resources. In a hard real-time context, however, these synchronization mechanisms need to have bounded the blocking duration of a task waiting for a(More)
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(More)
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(More)
We present a QoS management framework that enables us to quantitatively measure QoS, and to analytically plan and allocate resources. In this model, end users’ quality preferences are considered when system resources are apportioned across multiple applicationssuch that the net utility that accrues to the end-users is maximized. In [23][24], we primarily(More)