Abstract-Modern real-time embedded applications present high computation requirements which need to be realized within strict time constraints. The current trend towards parallel processing in the embedded domain allows providing higher processing power. However, in some embedded applications, the use of powerful enough multi-core processors, may not be possible due to energy, space or cost constraints. A solution for this problem is to extend the parallel execution of the applications, allowing them to distribute their workload among networked nodes, on peak situations, to remote neighbour nodes in the system. In this context, we present the PartitionedDistributed-Deadline Monotonic Scheduling algorithm for forkjoin parallel/distributed fixed-priority tasks. We study the problem of scheduling fork-join tasks that execute in a distributed system, where the inherent transmission delay of tasks must be considered and cannot be deemed negligible, as in the case of multicore systems. Our scheduling algorithm is shown to have a resource augmentation bound of 4, which implies that any task set that is feasible on unit-speed processors and a single shared real-time network, can be scheduled by our algorithm on processors and a single real-time network that are 4 times faster. We confirm through simulations our analytical results.
In the previous work REF we studied the problem of scheduling fork-join tasks on a distributed system composed of single-processor nodes and a shared bus communication network.
On the scheduling of fork-join parallel/distributed real-time tasks
Proceedings of the 9th IEEE International Symposium on Industrial Embedded Systems (SIES 2014)
Proceedings of the 9th IEEE International Symposium on Industrial Embedded Systems (SIES 2014)
Computer Science