Abstract-The elastic task model is a powerful model for adapting periodic real-time systems in the presence of uncertainty. This work generalizes the existing elastic scheduling approach in several directions. First, it presents a general framework, which formulates a trade-off between task schedulability and a specific performance metric as an optimization problem. Such a framework allows real-time systems under overloads to graciously adapt by adjusting their performance level. Second, it is shown in this work that the well-known task compression algorithm in fact solves a quadratic programming problem that seeks to minimize the sum of the squared deviation of a task's utilization from initial desired utilization. This finding indicates that the task compression algorithm may be applied to efficiently solve other similar types of problems that often arise in real-time applications. In particular, an iterative approach is proposed to solve the period selection problem for real-time tasks with deadlines less than respective periods. Further, the framework is adapted to solve the deadline selection problem, which is useful in some control systems with fixed periods. Index Terms-Real-time and embedded systems, sequencing and scheduling, performance of systems.
Chantem et al. REF focused on the elastic task model to select task periods for adapting periodic real-time systems in the presence of uncertainty.
Generalized Elastic Scheduling for Real-Time Tasks
IEEE Transactions on Computers
IEEE Transactions on Computers
Computer Science