An Evolutionary Approach for the Hierarchical Scheduling of Safety- and Security-Critical Multicore Architectures

Abstract

The aerospace and defense industry is facing an end-of-life production issue with legacy embedded uniprocessor systems. Most, if not all, embedded processor manufacturers have already moved towards system-on-a-chip multicore architectures. Current scheduling arrangements do not consider schedules related to safety and security. The methods are also inefficient because they arbitrarily assign larger-than-necessary windows of execution. This research creates a hierarchical scheduling framework as a model for real-time multicore systems to integrate the scheduling for safe and secure systems. This provides a more efficient approach which automates the migration of embedded systems’ real-time software tasks to multicore architectures. A novel genetic algorithm with a unique objective function and encoding scheme was created and compared to classical bin-packing algorithms. The simulation results show the genetic algorithm had 1.8–2.5 times less error (a 56–71% difference), outperforming its counterparts in uniformity in utilization. This research provides an efficient, automated method for commercial, private and defense industries to use a genetic algorithm to create a feasible two-level hierarchical schedule for real-time embedded multicore systems that address safety and security constraints.

Controlling the Complexity of Hierarchical Scheduling Frameworks

“…From our previous work, in [8], we define our HSF model, a task τ i is defined as the basic unit of execution and is characterized by worst-case execution time (WCET) defined as C i , a relative deadline D i , and a period of T i . A sporadic task will produce a series of jobs with inter-arrival times separated by T i time units.…”

“…The tasks are sporadic with implicit deadlines where D i = T i for all τ i (also known as Liu and Layland [25] task systems). From [8] we define the following: Definition 1. For n number of tasks, the tasks τ i are organized into tasksets Γ that are then assigned to the specific partition π i .…”

“…For example, Department of Defense programs face a challenging migration problem because legacy software uses older single processing environments which must be upgraded and re-architected to accommodate current requirements and modern multicore architectures. Previous work [8] by the authors created a hierarchical scheduling framework (HSF) integrated with a genetic algorithm to help with this migration and demonstrated the efficacy of our approach. To benefit the defense industry, we must answer the question: how can this previous research be applied to enable the migration of legacy embedded systems' software tasks to multicore architectures?…”