Real-time systems engineering and applications
Real-time systems engineering and applications
Parametric scheduling for hard real-time systems
Parametric scheduling for hard real-time systems
A team of robotic agents for surveillance
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Atomicity and isolation for transactional processes
ACM Transactions on Database Systems (TODS)
A distributed surveillance task using miniature robots
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Deadline Scheduling for Real-Time Systems: Edf and Related Algorithms
Deadline Scheduling for Real-Time Systems: Edf and Related Algorithms
Parametric Dispatching of Hard Real-Time Tasks
IEEE Transactions on Computers
A Specification Framework for Real-Time Scheduling
SOFSEM '02 Proceedings of the 29th Conference on Current Trends in Theory and Practice of Informatics: Theory and Practice of Informatics
On the Parallel Execution Time of Tiled Loops
IEEE Transactions on Parallel and Distributed Systems
On the Efficient Scheduling of Non-Periodic Tasks in Hard Real-Time Systems
RTSS '99 Proceedings of the 20th IEEE Real-Time Systems Symposium
Improving Quality-of-Control Using Flexible Timing Constraints: Metric and Scheduling Issues
RTSS '02 Proceedings of the 23rd IEEE Real-Time Systems Symposium
The Monitoring of Timing Constraints on Time Intervals
RTSS '02 Proceedings of the 23rd IEEE Real-Time Systems Symposium
Enhancing the performance and dependability of real-time systems
IPDS '95 Proceedings of the International Computer Performance and Dependability Symposium on Computer Performance and Dependability Symposium
Dynamic and Aggressive Scheduling Techniques for Power-Aware Real-Time Systems
RTSS '01 Proceedings of the 22nd IEEE Real-Time Systems Symposium
Duality in the parametric polytope and its applications to a scheduling problem
Duality in the parametric polytope and its applications to a scheduling problem
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Real-time systems are increasingly appearing in complex and dynamic environments such as cruise controllers, life support systems and nuclear reactors. These systems contain components that sense, control and stabilize the environment towards achieving the mission or target. These consociate components synchronize, compute and control themselves locally or have a centralized component to achieve their mission. Distributed computing techniques improve the overall performance and reliability of large real-time systems with spread components. Partially Clairvoyant scheduling was introduced in Saksena, M., PhD thesis (1994) to determine the schedulability of hard real-time jobs with variable execution times. The problem of deciding the Partially Clairvoyant schedulability of a constrained set of jobs was well studied in Gerber, R., et al., IEEE Trans. Comput. 44(3), 471---479 (1995), Choi, S. and Agrawala, A.K., Real-Time Syst. 19(1), 5---40 (2000), Subramani, K., J. Math. Model. Algorithms 2(2), 97---119 (2003). These algorithms determine the schedulability of a job-set offline and produce a set of dispatch functions for each job in the job-set. The dispatch functions of a job depend on the start and execution times of the jobs sequenced before the job. The dispatching problem is concerned with the online computation of the start time interval of a job such that none of the constraints are violated. In certain situations, the dispatcher fails to dispatch a job as it takes longer to compute the interval within which the job has to be dispatched, this phenomenon is called Loss of Dispatchability. For a job-set of size n, sequential approaches using function lists suffer from two major drawbacks, viz., 驴(n) dispatching time and the Loss of Dispatchability phenomenon. Existing approaches to this problem have been along sequential lines, through the use of stored function lists. In this paper, we propose and evaluate three distributed dispatching algorithms for Partially Clairvoyant schedules. For a job-set of size n, the algorithms have dispatch times of O(1) per job. In the first algorithm, one processor executes all the jobs and other processors compute the dispatch functions. This scenario simplifies design and is better in situations where one processor controls all other devices. In the other algorithms, all the processors execute jobs pre-assigned to them and compute the dispatch functions; which is a plausible scenario in distributed controlling.