Supporting Swap in Real-Time Task Scheduling for Unified Power-Saving in CPU and Memory

Abstract

As the size of data grows rapidly in modern IoT (Internet-of-Things) and CPS (Cyber-Physical System) applications, the memory power consumption of real-time embedded systems increases dramatically. Unlike general-purpose systems where memory consumes about 10% of the CPU power consumption, modern real-time systems have the memory power of 20-50% of CPU power. This is because the memory size of a real-time system should be large enough to accommodate the entire task set, and thus DRAM refresh operations become a major source of power consumption. In this article, we present a new swap scheme for real-time systems, which aims at reducing memory power consumption. To support swap with real-time constraints, we adopt high-speed NVM storage and co-optimize power-savings in CPU and memory. Unlike traditional real-time task models that only consider the executions in CPU, we define an extended task model that characterizes memory and storage paths of tasks as well, and tightly evaluate the worst-case execution time by formulating the overlapped latency between CPU and memory. By optimizing the CPU supply voltage and the memory swap ratio of given task set, our scheme reduces the energy consumption of realtime systems by 31.1% on average under various workload conditions.INDEX TERMS Real-time task scheduling; partial swap; genetic algorithm; power saving; voltage scaling; deadline; high-speed storage; NVM.

Empirical Comparison of Energy Efficiency Between 3-Tier and High-Speed 3-Tier Network Topologies

“…Network structures experiment with three-tier DC architecture and three-tier high-speed data center architecture) were performed by Kliazovich et al (2016). Algorithms were tested to save energy, namely the Green Scheduler (GS) in research by Duy et al (2010), the Power Saver Scheduler (PSS) and Random Scheduler (RS) in research by Lee & Hsieh (2013), and the RR Scheduler (RRS) in research by Mohialdeen (2013) and Yoon et al (2022).…”