RESUMEN
This article presents the latency minimisation potential provided by the Smart5Grid Open Experimentation Platform (OEP) developed by the Horizon 2020 Smart5Grid Research and Innovation (R&I) project. It discusses the OEP performance and provides experimental data to substantiate its contribution to improving observability and manageability of distributed renewable generation in power grids. That experimental proof is delivered by two pilots running on the OEP: Demo 1 Millisecond Level Precise Distribution Generation Control, and Demo 2 Real-time Wide Area Monitoring (WAM) pilot of 5G virtual Phasor Data Concentrator v(PDC) capabilities for WAM of end-to-end electricity grids. This work reports two Network Applications (NetApps) created to support both demos and provides experimental evidence that the OEP offers latency of comparable measure to well-established wire-bound communications in addition to availability and reliability on top of by-design flexibility, scalability and modularity, which are especially relevant to power systems with high shares of Distributed Renewable Energy Recourses (DRERs). The software and methods used for the OEP development and experimental testbeds applied to measure its latency performance in both tailored pilot demos are explained at length. The test results are presented and interpreted with a view to discussing potential contributions of the presented 5G-enabled solutions for power grid smartification in conditions of high rollout of distributed renewable generation. All pilot demos generate openly accessible data, except where specific security restrictions are applicable.
RESUMEN
Analysis of a patient's genomics data is the first step toward precision medicine. Such analyses are performed on expensive enterprise-class server machines because input data sets are large, and the intermediate data structures are even larger (TB-size) and require random accesses. We present a general method to perform a specific genomics problem, mutation detection, on a cheap commodity personal computer (PC) with a small amount of DRAM. We construct and access large histograms of k-mers efficiently on external storage (SSDs) and apply our technique to a state-of-the-art reference-free genomics algorithm, SMUFIN, to create SMUFIN-F. We show that on two PCs, SMUFIN-F can achieve the same throughput at only one third (36%) the hardware cost and half (45%) the energy compared to SMUFIN on an enterprise-class server. To the best of our knowledge, SMUFIN-F is the first reference-free system that can detect somatic mutations on commodity PCs for whole human genomes. We believe our technique should apply to other k-mer or n-gram-based algorithms.