Industries are considering the adoption of cloud and edge computing for real-time applications due to current improvements in network latencies and the advent of Fog and Edge computing. Current cloud paradigms are not designed for real-time applications, as they neither provide low latencies/jitter nor the guarantees and determinism required by real-time applications. Experts estimate that data centers use 1% of global electricity for powering the equipment, and in turn, for dealing with the produced heat. Hence, energy consumption is a crucial metric in cloud technologies. Applying energy conservation techniques is not straightforward due to the increased scheduling overheads and application execution times. Inspired by slot shifting, we propose an algorithm to support energy-aware time-triggered execution of periodic real-time VMs while still providing the ability to execute aperiodic real-time and best-effort VMs in the slack of the time-triggered ones. The algorithm considers energy reduction techniques based on dynamic power management and dynamic voltage and frequency scaling. We implement our algorithm as an extension to the Linux kernel scheduler (for use with the KVM hypervisor) and evaluate it on a server-grade Intel Xeon node.

翻译：工业界正考虑采用云和边缘计算技术处理实时应用，这得益于当前网络延迟的改善以及雾计算和边缘计算的兴起。现有云范式因无法提供实时应用所需的低延迟/抖动及确定性与可预测性保障，故非为实时应用设计。专家估计，数据中心消耗全球1%的电力用于设备供电及相应散热处理。因此，能耗成为云技术中的关键指标。然而，由于调度开销增加和应用程序执行时间延长，节能技术的应用并非易事。受时隙转移技术启发，我们提出一种算法，在支持周期性实时虚拟机能量感知时间触发执行的同时，仍能在时间触发虚拟机的空闲时隙中执行非周期性实时虚拟机及尽力而为型虚拟机。该算法综合考虑了基于动态电源管理与动态电压频率调整的节能技术。我们将该算法实现为Linux内核调度器的扩展（用于KVM虚拟机管理器），并在服务器级Intel Xeon节点上进行了评估。