PERTS: A Prototyping Environment for Real-Time Systems

PERTS is a prototyping environment for real-time systems. It is being built incrementally and will contain basic building blocks of operating systems for time-critical applications, tools, and performance models for the analysis, evaluation and measurement of real-time systems and a simulation/emulation environment. It is designed to support the use and evaluation of new design approaches, experimentations with alternative system building blocks, and the analysis and performance profiling of prototype real-time systems

Scheduling real-time, periodic jobs using imprecise results

A process is called a monotone process if the accuracy of its intermediate results is non-decreasing as more time is spent to obtain the result. The result produced by a monotone process upon its normal termination is the desired result; the error in this result is zero. External events such as timeouts or crashes may cause the process to terminate prematurely. If the intermediate result produced by the process upon its premature termination is saved and made available, the application may still find the result unusable and, hence, acceptable; such a result is said to be an imprecise one. The error in an imprecise result is nonzero. The problem of scheduling periodic jobs to meet deadlines on a system that provides the necessary programming language primitives and run-time support for processes to return imprecise results is discussed. This problem differs from the traditional scheduling problems since the scheduler may choose to terminate a task before it is completed, causing it to produce an acceptable but imprecise result. Consequently, the amounts of processor time assigned to tasks in a valid schedule can be less than the amounts of time required to complete the tasks. A meaningful formulation of this problem taking into account the quality of the overall result is discussed. Three algorithms for scheduling jobs for which the effects of errors in results produced in different periods are not cumulative are described, and their relative merits are evaluated

Preemptive scheduling on uniform parallel machines with controllable job processing times

In this paper, we provide a unified approach to solving preemptive scheduling problems with uniform parallel machines and controllable processing times. We demonstrate that a single criterion problem of minimizing total compression cost subject to the constraint that all due dates should be met can be formulated in terms of maximizing a linear function over a generalized polymatroid. This justifies applicability of the greedy approach and allows us to develop fast algorithms for solving the problem with arbitrary release and due dates as well as its special case with zero release dates and a common due date. For the bicriteria counterpart of the latter problem we develop an efficient algorithm that constructs the trade-off curve for minimizing the compression cost and the makespan

Imprecise results: Utilizing partial computations in real-time systems

In real-time systems, a computation may not have time to complete its execution because of deadline requirements. In such cases, no result except the approximate results produced by the computations up to that point will be available. It is desirable to utilize these imprecise results if possible. Two approaches are proposed to enable computations to return imprecise results when executions cannot be completed normally. The milestone approach records results periodically, and if a deadline is reached, returns the last recorded result. The sieve approach demarcates sections of code which can be skipped if the time available is insufficient. By using these approaches, the system is able to produce imprecise results when deadlines are reached. The design of the Concord project is described which supports imprecise computations using these techniques. Also presented is a general model of imprecise computations using these techniques, as well as one which takes into account the influence of the environment, showing where the latter approach fits into this model

Imprecise Computation Model, Synchronous Periodic Real-time Task Sets and Total Weighted Error

This paper proposes two scheduling approaches, one-level and two-level scheduling, for synchronous periodic real-time task sets based on the Imprecise Computation Model. The imperative of real-time systems is a reaction on an event within a limited amount of time. Sometimes the available time and resources are not enough for the computations to complete within the deadlines, but still enough to produce approximate results. The Imprecise Computation Model is motivated by this idea, which gives the flexibility to trade off precision for timeliness. In this model a task is logically decomposed into a mandatory and optional subtask. Only the mandatory subtask is required to complete by its deadline, while the optional subtask may be left unfinished. Usually, different scheduling policies are used for the scheduling of mandatory and optional subtasks. For both proposed approaches the earliest deadline first and rate monotonic scheduling algorithms are used for the scheduling of mandatory subtasks, whereas the optional subtasks are scheduled in a way that the total weighted error is minimized. The basic idea of one-level scheduling is to extend the mandatory execution times, while in two-level scheduling the mandatory and optional subtasks are separately scheduled. The single preemptive processor model is assumed

Genetic Algorithms in Real-Time Imprecise Computing

This article describes the use of genetic algorithms in real-time systems that employ the imprecise computation paradigm. In real-time systems, the focus is on ensuring that a set of tasks each complete within their deadlines. Faults may occur in the computation or the environment that can cause missed deadlines. That is why the idea of using partial results, when exact ones cannot be produced within the deadline, has been introduced. This idea has been formalized using the concepts of anytime algorithms and imprecise computation and specific techniques have been developed for designing programs which can produce partial results and for developing systems that can support imprecise computation techniques. Genetic algorithms are methods that can be, without any adaptation, used in an imprecise computation system. They produce a solution that bears a certain measure of reliability. During the process of their execution, this solution is constant ly improving. They can be used as a part of a real-time system, especially for optimizing tasks where the classical algorithms are not applicable or its computational time proves to be too expensive

Fuzzy Feedback Scheduling of Resource-Constrained Embedded Control Systems

The quality of control (QoC) of a resource-constrained embedded control system may be jeopardized in dynamic environments with variable workload. This gives rise to the increasing demand of co-design of control and scheduling. To deal with uncertainties in resource availability, a fuzzy feedback scheduling (FFS) scheme is proposed in this paper. Within the framework of feedback scheduling, the sampling periods of control loops are dynamically adjusted using the fuzzy control technique. The feedback scheduler provides QoC guarantees in dynamic environments through maintaining the CPU utilization at a desired level. The framework and design methodology of the proposed FFS scheme are described in detail. A simplified mobile robot target tracking system is investigated as a case study to demonstrate the effectiveness of the proposed FFS scheme. The scheme is independent of task execution times, robust to measurement noises, and easy to implement, while incurring only a small overhead.Comment: To appear in International Journal of Innovative Computing, Information and Contro