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.