Software-Defined Infrastructure for IoT-based Energy Systems

Internet of Things (IoT) devices are becoming an essential part of our everyday lives. These physical devices are connected to the internet and can measure or control the environment around us. Further, IoT devices are increasingly being used to monitor buildings, farms, health, and transportation. As these connected devices become more pervasive, these devices will generate vast amounts of data that can be used to gain insights and build intelligence into the system. At the same time, large-scale deployment of these devices will raise new challenges in efficiently managing and controlling them. In this thesis, I argue that the IoT devices need programmability and need to provide software controls in order to manage them efficiently. Further, it will need data-driven modeling techniques to process and analyze a vast amount of data from heterogeneous devices to derive actionable insights. My thesis explores the problems posed by software-defined IoT energy infrastructure. I present four techniques that use systems and machine learning principles to design, analyze and deploy the next generation of smart IoT energy systems. First, I discuss how current state-of-the-art LIDAR-based approaches in identifying ideal locations on rooftops for deploying energy systems such as solar do not scale to many regions of the world. To address the challenges, I propose DeepRoof, a data-driven approach that uses deep learning to estimate the solar potential of roofs using satellite imagery and identify ideal locations for installation. We evaluate our approach on different types of roof and show that our technique is comparable to LIDAR-based methods. Second, I study how excessive solar can cause problems in the grid and examine how programmatic control of the solar output can prevent congestion in the electric grid. Further, I present a decentralized approach that can control the solar arrays in a grid-friendly manner. Also, my approach provides flexible control of solar output, and I show that such mechanisms allow for higher solar penetration in the grid. Third, I discuss the challenges in community-owned (and shared) distributed energy resources that do not provide independent control to users. To do so, I propose vSolar, an approach to virtualize the solar arrays and energy storage that allows independent control. Further, I show how using vSolar users can exercise independent control, implement their custom energy sharing policies, and reduce energy costs through energy trading. Finally, I present the challenges, and the high throughput needs to enable a peer-to-peer energy trading platform using permissioned blockchains. I propose FabricPlus, an enhanced Hyperledger Fabric blockchain, that contains a series of optimizations to enable high throughput transactions. FabricPlus increases the transaction throughput many folds, without requiring any changes to its external interfaces. I also show considerable performance improvement over the baseline Fabric

The 26th Annual Precise Time and Time Interval (PTTI) Applications and Planning Meeting

This document is a compilation of technical papers presented at the 26th Annual PTTI Applications and Planning Meeting. Papers are in the following categories: (1) Recent developments in rubidium, cesium, and hydrogen-based frequency standards, and in cryogenic and trapped-ion technology; (2) International and transnational applications of Precise Time and Time Interval technology with emphasis on satellite laser tracking, GLONASS timing, intercomparison of national time scales and international telecommunications; (3) Applications of Precise Time and Time Interval technology to the telecommunications, power distribution, platform positioning, and geophysical survey industries; (4) Applications of PTTI technology to evolving military communications and navigation systems; and (5) Dissemination of precise time and frequency by means of GPS, GLONASS, MILSTAR, LORAN, and synchronous communications satellites

Architectural Implications of Automatic Parallelization With HELIX-RC

As classic Dennard process scaling fades into the past, power density concerns have driven modern CPU designs to de-emphasize the pursuit of single-thread performance, focusing instead on increasing the number of cores in a chip. Computing throughput on a modern chip continues to improve, since multiple programs can run in parallel, but the performance of single programs improves only incrementally. Many compilers have been designed to automatically parallelize sequentially written programs by leveraging multiple cores for the same task, thereby enabling continued single-thread performance gains. One such compiler is HELIX, which can increase the performance of a mixture of SPECfp and SPECint benchmarks by 2X on a 6-core Nehalem CPU. Previous approaches to automatically parallelize irregular programs have focused on removing apparent dependences through thread-level speculation, which limits the type of code that can be targeted. In contrast, this dissertation increases the amount of code that can be parallelized by addressing the specific communication demands of that code. The dissertation proposes a special purpose extension of the cache hierarchy, called ring cache, to greatly reduce the perceived communication latency between cores running an automatically parallelized program. This co-design of ring cache and the HELIX compiler, called HELIX-RC, increases the speedup of 10 SPEC benchmarks running on 16 simulated in-order cores from an average of 2X to an average of over 8X. Speedups are slightly reduced to 7X on out-of-order cores, which extract instruction-level parallelism on their own. A fully synthesized Verilog implementation of ring cache is evaluated and is shown to consume less than 25mW of power with an area of less than 0.275 square millimeters. This dissertation includes a study comparing single program per core multiprogramming and HELIX-RC. Counterintuitively, some HELIX-RC parallelized benchmarks not only surpass simple multiprogramming in terms of single program performance, but can also beat multiprogramming in terms of total multicore throughput by reducing the effective per-core working set of a program. With communication bottlenecks removed by ring cache, automatic parallelization with HELIX-RC restores a decade of lost single-thread performance improvements.Engineering and Applied Sciences - Engineering Science