Abstract
In the past few years, the energy consumption of real-time systems has become an essential topic of research, especially with the rise of the internet of things. In hard real-time systems, sensitivity to timing is the focal feature of system behaviors. Real-time systems must consider time constraints (mainly deadlines) to deliver the proper results. Therefore, typical real-time systems are inefficient in resource reservation, where resource supply is always higher than the workload demand and thus consumes more power than required. In firm and soft real-time systems, some deadline violations can be tolerated in a specific time interval. The worst-case execution time can be delayed to abound range. Therefore, instead of over-provisioning resources when considering the demand cannot be higher than supply, we propose a new algorithm that can efficiently estimate the resource reservation and reduce power consumption. The proposed algorithm is theoretically studied, and results show that while still satisfying the delay requirements of control systems, significant energy savings (up to 78.5%) could be achieved.
Highlights
In the past decade, real-time embedded systems (RTES) have been used in many applications in our daily lives
RTES have been capable of executing complex tasks due to the recent improvement of real-time embedded systems’ capabilities, which maximizes their power usage [4]
SYSTEM MODEL AND ASSUMPTIONS We present the types of real-time tasks, workload and schedulability analysis model, execution time model, and power model in the system model
Summary
Real-time embedded systems (RTES) have been used in many applications in our daily lives. Azim: Energy-Efficient Periodic Resource Model for Bounded Delay-Tolerant RTS Using these tolerable delay systems to effectively reduce resource over-provisioning while using the schedulability test based on sbf and dbf to guarantee the application’s efficiency (schedulability) is a promising technique for lowering energy consumption. The purpose of an energy-efficient periodic resource model for RTES is to reduce energy consumption in terms of execution/completion time and schedulability of tasks without compromising real-time systems’ performance. The paper’s main contributions are: 1) We propose an energy-efficient periodic resource model algorithm Dynamic Speed Scaling (DSS) technique for real-time systems for minimizing energy usage, maintaining the scheduling of individual tasks. 2) We theoretically show the feasibility of reducing processor speed on minimizing the total energy consumption of hard, soft, and firm real-time tasks.
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