Performance Optimization of Control Applications on Fog Computing Platforms Using Scheduling and Isolation

Abstract

In this paper, we address mixed-criticality applications characterized by their safety criticality and time-dependent performance, which are virtualized on a Fog Computing Platform (FCP). The FCP is implemented as a set of interconnected multicore computing nodes, and brings computation and communication closer to the edge of the network, where the machines are located in industrial applications. We use partitioning and static-cyclic scheduling to provide isolation among mixed-criticality tasks and to guarantee their timing requirements. The temporal and spatial isolation is enforced via partitions, which execute tasks with the same criticality level. We consider that the tasks are scheduled using static cyclic scheduling. We are interested in determining the mapping of tasks to the cores of the fog nodes, the assignment of tasks to the partitions, the partition schedule tables, and the tasks' schedule tables, such that the Quality-of-Control for the control tasks is maximized and we meet the timing requirements for all tasks, including tasks with lower-criticality levels. We are also interested in determining the periods for control tasks to balance the schedulability and the control performance. We have proposed a Simulated Annealing metaheuristic, which relies on a heuristic algorithm for determining the schedules and partitions, to solve this optimization problem. Our optimization strategy has been evaluated on several test cases, showing the effectiveness of the proposed method.

Keywords

• Fog computing
• mixed-criticality systems
• quality-of-control
• scheduling
• partitioning
• optimization