Single Resource Scheduling Research Articles

Transparent Real-Time Task Scheduling on Temporal Resource Partitions

The Hierarchical Real-Time Scheduling (HiRTS) technique helps improve overall resource utilization in real-time embedded systems. With HiRTS, a computation resource is divided into a group of temporal resource partitions, each of which accommodates multiple real-time tasks. Besides the computation resource partitioning problem, real-time task scheduling on resource partitions is also a major problem of HiRTS. The existing scheduling techniques for dedicated resources, like schedulability tests and utilization bounds, are unable to work without changes on temporal resource partitions in most cases. In this paper, we show how to achieve maximal transparency for task scheduling on Regular Partitions, a type of resource partition introduced by the Regularity-based Resource Partition (RRP) Model. We show that several classes of real-time scheduling problems on a regular partition can be transformed into equivalent problems on a dedicated single resource, such that comprehensive single-resource scheduling techniques provide optimal solutions. Furthermore, this transformation method could be applied to different types of real-time tasks such as periodic tasks, sporadic tasks and aperiodic tasks.

A single resource scheduling problem with job-selection flexibility, tardiness costs and controllable processing times

We consider single-resource scheduling when candidate jobs may be accepted (producing job-specific profit) or rejected. Our solution approaches seek to maximize the profitability of the resulting schedule under job-specific tardiness costs and reducible processing times (at a cost). We present an algorithm that maximizes schedule profit for a given sequence of jobs, along with two heuristic approaches for generating good job sequences. A set of computational tests on randomly generated problem instances demonstrates the relative effectiveness of our proposed heuristic approaches.

A heuristic approach for minimizing weighted tardiness and overtime costs in single resource scheduling

Many firms face practical scheduling challenges in balancing the costs of overtime against the costs of tardiness, where tardiness costs vary by job or project. This paper addresses this challenge by considering scheduling jobs for processing during a finite number of discrete periods on a single resource. The resource can process jobs in two modes, regular time and overtime, at different costs per unit time, and jobs cannot be preempted. Each job has associated release and due dates, and tardy jobs incur a job-specific cost per period tardy. The schedule planner wishes to minimize the sum of weighted job tardiness and resource overtime costs. We provide a pseudopolynomial time algorithm for determining the optimal usage of regular time and overtime for any fixed sequence of jobs. We use this algorithm as a subroutine for heuristically solving the general scheduling problem using a priority sequencing rule. A local search algorithm then improves upon the initial solution generated by the priority rules. We use a strengthened linear programming formulation to provide good lower bounds for assessing the performance of the heuristics. Computational tests show that the algorithm generates close to optimal solutions for problems with high tardiness costs in reasonable computing time. Scope and purpose This paper addresses a practical job or project scheduling problem in which the schedule planner is concerned with both overtime and tardy delivery costs. In many scheduling contexts, when multiple jobs compete for a resource with limited capacity, operations managers must determine the sequence in which to process jobs. One lever management can use to avoid tardy project or job delivery is through the use of overtime, which typically comes at an increased cost to the firm. When different jobs have different priorities and, therefore, different tardiness costs, the problem of determining the best plan for sequencing jobs and for using overtime to complete jobs is highly complex and has not been adequately addressed in the literature. This paper provides a heuristic scheduling algorithm for solving these problems. The problem considered generalizes several well-known problems in the single-machine scheduling literature (M. Pinedo, Scheduling: Theory, Algorithms and Systems, Upper Saddle River, Prentice-Hall, NJ, 2002), while the heuristic algorithm is based on highly effective local search methods (Ghosh and Chakravarti, Comput. Oper. Res. 26(3) (1999) 271) developed for providing good solutions for difficult combinatorial optimization problems.

Optimal Online Algorithms for Minimax Resource Scheduling

We consider a very general online scheduling problem with an objective to minimize the maximum level of resource allocated. We find a simple characterization of an optimal deterministic online algorithm. We develop further results for the two, more specific problems of single resource scheduling and hierarchical line balancing. We determine how to compute optimal online algorithms for both problems using linear programming and integer programming, respectively. We show that randomized algorithms can outperform deterministic algorithms, but only if the amount of work done is a nonconcave function of resource allocation.

Fair on-line scheduling of a dynamic set of tasks on a single resource

Consider a set of “tasks” competing for the use of a single “resource”, where (i) only one task is allowed to use the resource at a time, (ii) the resource is scheduled in unit-time intervals, (iii) each task requires a specific fraction of the resource capacity over an extended period, and (iv) tasks arrive and depart at any time. We refer to such a task system as an instance of the single-resource scheduling problem. The problem of designing a “fair” scheduling algorithm for such task systems has recently received a great deal of attention in the literature. This paper makes two main contributions. First, we point out that Tijdeman's work on the so-called “chairman assignment problem” provides a simple and efficient on-line algorithm for the static version of the single-resource scheduling problem (i.e., where the set of tasks competing to use the resource does not change over time). We then extend Tijdeman's algorithm to obtain a simple and efficient on-line algorithm for the dynamic single-resource scheduling problem.

Distributed Pipeline Scheduling: A Framework for Distributed, Heterogeneous Real-Time System Design

This paper describes the distributed pipeline scheduling framework that provides a systematic approach to designing distributed, heterogeneous real-time systems. This paper formalizes distributed pipelining scheduling by providing a set of abstractions and transformations to map real-time applications to system resources, to create highly efficient and predictable systems, and to decompose the very complex multiresource system timing analysis problem into a set of simpler application stream and single-resource schedulability problems to ascertain that all real-time application timing requirements are met. Distributed pipeline scheduling includes support for distributed, heterogeneous system resources and diverse local scheduling policies, global scheduling policies for efficient resource utilization, flow-control mechanisms for predictable system behaviour, and a range of system reconfiguration options to meet application timing requirements. An audio/video example is used in this paper to demonstrate the power and utility of distributed pipeline scheduling. 1. INTRODUCTIO N It is not uncommon for serious performance problems to arise in the development of distributed, heterogeneous real-time systems. Part of the problem has been due to the lack of a systematic approach for designing these systems. Discrete-event simulation and timeline construction approaches tend to scale poorly, with the complexity of the model approaching the complexity of the system under development. These models become unwieldy, insufficiently maintained and are then discarded. As a result, the performance properties of the final system diverge greatly from initial goals. This paper presents a framework, denoted the distributed pipeline scheduling framework, for designing distributed, heterogeneous real-time systems that execute in a pipelined, efficient and predictable manner. Furthermore, analysis techniques presented in the framework decompose the very complex multi-resource system-timing analysis problem into a set of simpler single-resource, single-application stream problems that can accurately predict the performance properties of the resulting system design and can be used to determine that all real-time application timing requirements are met. If application timing requirements are not met, the framework also delineates how manipulating system configuration parameters can attempt to meet the timing requirements. Figure 1 provides an overview of the distributed pipeline scheduling framework. Each logical application stream model (LASM) and optional parameters list (OP) capture a real-time application's timing and processing requirements. The LASM is composed of a set of precedence-constraint processing steps. Each processing step is an unit of execution on any resource, e.g. a thread is a processing step executing on a processor-type resource. The target platform model (TPM) represents a connected set of system resources, each of which can be found in the library of single-resource scheduling models.