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The rigid timing requirement of real-time applications biases the analysis to focus on the worst-case performances. Such a focus cannot provide enough information to optimize the system’s typical resource and energy consumption. In this work, we study the real-time scheduling of parallel tasks on a multi-speed heterogeneous platform while minimizing their typical-case CPU energy consumption. Dynamic power management (DPM) policy is integrated to determine the minimum number of cores required for each task while guaranteeing worst-case execution requirements (under all circumstances). A Hungarian Algorithm-based task partitioning technique is proposed for clustered multi-core platforms, where all cores within the same cluster run at the same speed at any time, while different clusters may run at different speeds. To our knowledge, this is the first work aiming to minimize typical-case CPU energy consumption (while ensuring the worst-case timing correctness for all tasks under any execution condition) through DPM for parallel tasks in a clustered platform. We demonstrate the effectiveness of the proposed approach with existing power management techniques using experimental results and simulations. The experimental results conducted on the Intel Xeon 2680 v3 12-core platform show around 7%-30% additional energy savings. 

The concept of Industry 4.0 introduces the unification of industrial Internet-of-Things (IoT), cyber physical systems, and data-driven business modeling to improve production efficiency of the factories. To ensure high production efficiency, Industry 4.0 requires industrial IoT to be adaptable, scalable, real-time, and reliable. Recent successful industrial wireless standards such as WirelessHART appeared as a feasible approach for such industrial IoT. For reliable and real-time communication in highly unreliable environments, they adopt a high degree of redundancy. While a high degree of redundancy is crucial to real-time control, it causes a huge waste of energy, bandwidth, and time under a centralized approach and are therefore less suitable for scalability and handling network dynamics. To address these challenges, we propose DistributedHART—a distributed real-time scheduling system for WirelessHART networks. The essence of our approach is to adopt local (node-level) scheduling through a time window allocation among the nodes that allows each node to schedule its transmissions using a real-time scheduling policy locally and online. DistributedHART obviates the need of creating and disseminating a central global schedule in our approach, thereby significantly reducing resource usage and enhancing the scalability. To our knowledge, it is the first distributed real-time multi-channel scheduler for WirelessHART. We have implemented DistributedHART and experimented on a 130-node testbed. Our testbed experiments as well as simulations show at least 85% less energy consumption in DistributedHART compared to existing centralized approach while ensuring similar schedulability.