event-driven scheduling for dynamic workload scaling in uniprocessor embedded systems
Many embedded systems are designed to take timely reactions to the occurrences of interested scenarios. Sometimes transient overloads might be experienced due to hardware malfunctions or workload bursts. Thus a mechanism to focus system attention on urgent events could be a key to provide reasonably stable service. In this paper, we propose a new approach for workload scaling in uniprocessor real-time embedded systems. A deterministic algorithm is adopted to selectively fed hardware events into a system, and an event-driven task model is introduced to formulate complex precedence constraints among tasks. Such a new approach removes the need for the adjustments of task periods and task phasing, which is crucial for many time-driven systems. The proposed approach was implemented in a real-time surveillance system, for which good accuracy and responsiveness were obtained under stressing workloads.
embedded systems real-time systems overload management adaptive systems