End-to-end scheduling to meet deadlines in distributed systems
End-to-end scheduling to meet deadlines in distributed systems
Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment
Journal of the ACM (JACM)
An Open Environment for Real-Time Applications
Real-Time Systems
Introduction to Algorithms
Heuristic Solutions for the Multiple-Choice Multi-dimension Knapsack Problem
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
FARA ¾ A Framework for Adaptive Resource Allocation in Complex Real-Time Systems
RTAS '98 Proceedings of the Fourth IEEE Real-Time Technology and Applications Symposium
A resource allocation model for QoS management
RTSS '97 Proceedings of the 18th IEEE Real-Time Systems Symposium
Quality adaptation in a multisession multimedia system: model, algorithms, and architecture
Quality adaptation in a multisession multimedia system: model, algorithms, and architecture
Integrated Resource Management and Scheduling with Multi-Resource Constraints
RTSS '04 Proceedings of the 25th IEEE International Real-Time Systems Symposium
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Traditional resource allocation algorithms such as Q-RAM [11] assume that importance (or weight) or utility values for distributed real-time tasks is a totally ordered set to measure the rewards for completing every task. Hence, resource allocation problem can be viewed as the problem of maximizing total utility values. However, in several real-time applications such as Multi-Function Phased Array Radar (MFPAR) systems, totally ordered importance are not available. We develop a twolevel resource allocation framework. The framework allows the schedulers for subsystems or processors in distributed realtime systems to autonomously schedule local sub-tasks and the system performance is enhanced without heavy global optimization overhead. In addition, the framework can trade the run-time overhead including time and memory space with the optimality of resource allocation. We evaluate our framework by extensive simulations for MFPAR systems. The experimental results show that the developed framework outperforms the traditional priority-based approach.