Segment based power-efficient scheduling for real-time DAG tasks on edge devices

Abstract

Smart Mobile Devices (SMDs) are crucial for the edge computing paradigm’s real-world sensing. Real-time applications, which are computationally intensive and periodic with strict time constraints, can typically be used to replicate real-world sensing. Such applications call for increased processing speed, memory capacity, and battery life on SMDs, which are typically resource-constrained due to physical size restrictions. As a result, scheduling real-time applications for SMDs that are power efficient is crucial for the regular operation of edge computing platforms, and downstream decision-making tasks like computation offloading require the prediction of power consumption using power-saving approaches like DVFS. The main question is how to swiftly develop a better solution to the NP-Hard power efficient scheduling problem with DVFS. Thus, by segmenting the aligned tasks on an SMD, we present a segment-based analysis approach. Additionally, we offer a segment-based scheduling algorithm (SEDF) that draws inspiration from the segment-based analysis approach to achieve power-efficient scheduling for these real-time workloads. This segment-based approach yields a power consumption bound (PB), and a computation offloading use case is developed to demonstrate the application of PB in the subsequent decision-making processes. Both simulations and actual device tests are used to confirm the PB, SEDF, and the effectiveness of offloading decision-making. We demonstrate empirically that PB can be utilized to make approximative optimal decisions in decision-making problems involving computation offloading. SEDF is a straightforward and effective scheduling approach that can cut the power consumption of a multi-core SMD by roughly 30%.