5 research outputs found
Laxity dynamics and LLF schedulability analysis on multiprocessor platforms
LLF (Least Laxity First) scheduling, which assigns a higher priority to a task with a smaller laxity, has been known as an optimal preemptive scheduling algorithm on a single processor platform. However, little work has been made to illuminate its characteristics upon multiprocessor platforms. In this paper, we identify the dynamics of laxity from the system’s viewpoint and translate the dynamics into LLF multiprocessor schedulability analysis. More specifically, we first characterize laxity properties under LLF scheduling, focusing on laxity dynamics associated with a deadline miss. These laxity dynamics describe a lower bound, which leads to the deadline miss, on the number of tasks of certain laxity values at certain time instants. This lower bound is significant because it represents invariants for highly dynamic system parameters (laxity values). Since the laxity of a task is dependent of the amount of interference of higher-priority tasks, we can then derive a set of conditions to check whether a given task system can go into the laxity dynamics towards a deadline miss. This way, to the author’s best knowledge, we propose the first LLF multiprocessor schedulability test based on its own laxity properties. We also develop an improved schedulability test that exploits slack values. We mathematically prove that the proposed LLF tests dominate the state-of-the-art EDZL tests. We also present simulation results to evaluate schedulability performance of both the original and improved LLF tests in a quantitative manner
Reinforcement learning based multi core scheduling (RLBMCS) for real time systems
Embedded systems with multi core processors are increasingly popular because of the diversity of applications that can be run on it. In this work, a reinforcement learning based scheduling method is proposed to handle the real time tasks in multi core systems with effective CPU usage and lower response time. The priority of the tasks is varied dynamically to ensure fairness with reinforcement learning based priority assignment and Multi Core MultiLevel Feedback queue (MCMLFQ) to manage the task execution in multi core system
LLF schedulability analysis on multiprocessor platforms
LLF (Least Laxity First) scheduling, which assigns
a higher priority to a task with smaller laxity, has been
known as an optimal preemptive scheduling algorithm on a
single processor platform. However, its characteristics upon
multiprocessor platforms have been little studied until now.
Orthogonally, it has remained open how to efficiently schedule
general task systems, including constrained deadline task
systems, upon multiprocessors. Recent studies have introduced
zero laxity (ZL) policy, which assigns a higher priority to
a task with zero laxity, as a promising scheduling approach
for such systems (e.g., EDZL). Towards understanding the
importance of laxity in multiprocessor scheduling, this paper
investigates the characteristics of ZL policy and presents the
first ZL schedulability test for any work-conserving scheduling
algorithm that employs this policy. It then investigates the
characteristics of LLF scheduling, which also employs the ZL
policy, and derives the first LLF-specific schedulability test
on multiprocessors. It is shown that the proposed LLF test
dominates the ZL test as well as the state-of-art EDZL test