Energy Aware Task Scheduling for Soft Real Time Systems using an Analytical Approach for Energy Estimation

TL;DR

This paper introduces an analytical method for estimating energy consumption and scheduling tasks in soft real-time embedded systems, ensuring temporal constraints while optimizing power usage.

Contribution

It presents a novel approach combining power estimation using CDFG and HCFG models with energy-efficient scheduling and voltage assignment techniques.

Findings

Effective energy estimation for embedded software.

Validated approach on various task graph benchmarks.

Achieved energy-efficient scheduling with guaranteed timing constraints.

Abstract

Embedded systems have pervaded all walks of our life. With the increasing importance of mobile embedded systems and flexible applications, considerable progress in research has been made for power management. Power constraints are increasingly becoming the critical component of the design specifications of these systems. It helps in pre-determining the suitable hardware architecture for the target application. The aim of this paper is to present a technique to estimate 'pre-run time' and 'power' of a software mapped onto a hardware system; guaranteeing the compliance of temporal constraints while generating a schedule of tasks of software. Real time systems must handle several independent macro-tasks, each represented by a task graph, which includes communications and precedence constraints. We propose a novel approach for power estimation of embedded software using the Control Data Flow Graph (CDFG) or task graph model. This methodology uses an existing Hierarchical Concurrent Flow Graph (HCFG) technique for the power analysis of the CDFGs. We have evaluated our technique for energy efficient scheduling over various task graph benchmarks. The results obtained prove the utility and efficacy of our proposed approach for power analysis of embedded software. We also present a methodology to obtain an energy optimal voltage assignment and perform scheduling by taking advantage of the relaxation in execution time of tasks.