A Time-driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing

Compared to traditional distributed computing environments such as grids, cloud computing provides a more cost-effective way to deploy scientific workflows. Each task of a scientific workflow requires several large datasets that are located in different datacenters from the cloud computing environment, resulting in serious data transmission delays. Edge computing reduces the data transmission delays and supports the fixed storing manner for scientific workflow private datasets, but there is a bottleneck in its storage capacity. It is a challenge to combine the advantages of both edge computing and cloud computing to rationalize the data placement of scientific workflow, and optimize the data transmission time across different datacenters. Traditional data placement strategies maintain load balancing with a given number of datacenters, which results in a large data transmission time. In this study, a self-adaptive discrete particle swarm optimization algorithm with genetic algorithm operators (GA-DPSO) was proposed to optimize the data transmission time when placing data for a scientific workflow. This approach considered the characteristics of data placement combining edge computing and cloud computing. In addition, it considered the impact factors impacting transmission delay, such as the band-width between datacenters, the number of edge datacenters, and the storage capacity of edge datacenters. The crossover operator and mutation operator of the genetic algorithm were adopted to avoid the premature convergence of the traditional particle swarm optimization algorithm, which enhanced the diversity of population evolution and effectively reduced the data transmission time. The experimental results show that the data placement strategy based on GA-DPSO can effectively reduce the data transmission time during workflow execution combining edge computing and cloud computing

Physical Design of Optoelectronic System-on-a-Chip/Package Using Electrical and Optical Interconnects: CAD Tools and Algorithms

Current electrical systems are faced with the limitation in performance by the electrical interconnect technology determining overall processing speed. In addition, the electrical interconnects containing many long distance interconnects require high power to drive. One of the best ways to overcome these bottlenecks is through the use of optical interconnect to limit interconnect latency and power. This research explores new computer-aided design algorithms for developing optoelectronic systems. These algorithms focus on place and route problems using optical interconnections covering system-on-a-chip design as well as system-on-a-package design. In order to design optoelectronic systems, optical interconnection models are developed at first. The CAD algorithms include optical interconnection models and solve place and route problems for optoelectronic systems. The MCNC and GSRC benchmark circuits are used to evaluate these algorithms.Ph.D.Committee Chair: Abhijit Chatterjee; Committee Member: C. P. Wong; Committee Member: David E. Schimmel; Committee Member: John A. Buck; Committee Member: Madhavan Swaminatha

Analog Photonics Computing for Information Processing, Inference and Optimisation

This review presents an overview of the current state-of-the-art in photonics computing, which leverages photons, photons coupled with matter, and optics-related technologies for effective and efficient computational purposes. It covers the history and development of photonics computing and modern analogue computing platforms and architectures, focusing on optimization tasks and neural network implementations. The authors examine special-purpose optimizers, mathematical descriptions of photonics optimizers, and their various interconnections. Disparate applications are discussed, including direct encoding, logistics, finance, phase retrieval, machine learning, neural networks, probabilistic graphical models, and image processing, among many others. The main directions of technological advancement and associated challenges in photonics computing are explored, along with an assessment of its efficiency. Finally, the paper discusses prospects and the field of optical quantum computing, providing insights into the potential applications of this technology.Comment: Invited submission by Journal of Advanced Quantum Technologies; accepted version 5/06/202

Future Energy Efficient Data Centers With Disaggregated Servers

The popularity of the Internet and the demand for 24/7 services uptime is driving system performance and reliability requirements to levels that today's data centers can no longer support. This paper examines the traditional monolithic conventional server (CS) design and compares it to a new design paradigm: the disaggregated server (DS) data center design. The DS design arranges data centers resources in physical pools, such as processing, memory, and IO module pools, rather than packing each subset of such resources into a single server box. In this paper, we study energy efficient resource provisioning and virtual machine (VM) allocation in DS-based data centers compared to CS-based data centers. First, we present our new design for the photonic DS-based data center architecture, supplemented with a complete description of the architectural components. Second, we develop a mixed integer linear programming (MILP) model to optimize VM allocation for the DS-based data center, including the data center communication fabric power consumption. Our results indicate that, in DS data centers, the optimum allocation of pooled resources and their communication power yields up to 42% average savings in total power consumption when compared with the CS approach. Due to the MILP high computational complexity, we developed an energy efficient resource provisioning heuristic for DS with communication fabric (EERP-DSCF), based on the MILP model insights, with comparable power efficiency to the MILP model. With EERP-DSCF, we can extend the number of served VMs, where the MILP model scalability for a large number of VMs is challenging. Furthermore, we assess the energy efficiency of the DS design under stringent conditions by increasing the CPU to memory traffic and by including high noncommunication power consumption to determine the conditions at which the DS and CS designs become comparable in power consumption. Finally, we present a complete analysis of the communication patterns in our new DS design and some recommendations for design and implementation challenges

The physics of optical computing

There has been a resurgence of interest in optical computing over the past decade, both in academia and in industry, with much of the excitement centered around special-purpose optical computers for neural-network processing. Optical computing has been a topic of periodic study for over 50 years, including for neural networks three decades ago, and a wide variety of optical-computing schemes and architectures have been proposed. In this paper we provide a systematic explanation of why and how optics might be able to give speed or energy-efficiency benefits over electronics for computing, enumerating 11 features of optics that can be harnessed when designing an optical computer. One often-mentioned motivation for optical computing -- that the speed of light $c$ is fast -- is not a key differentiating physical property of optics for computing; understanding where an advantage could come from is more subtle. We discuss how gaining an advantage over state-of-the-art electronic processors will likely only be achievable by careful design that harnesses more than one of the 11 features, while avoiding a number of pitfalls that we describe.Comment: 31 pages; 11 figure

Joint Optimization of Illumination and Communication for a Multi-Element VLC Architecture

Because of the ever increasing demand wireless data in the modern era, the Radio Frequency (RF) spectrum is becoming more congested. The remaining RF spectrum is being shrunk at a very heavy rate, and spectral management is becoming more difficult. Mobile data is estimated to grow more than 10 times between 2013 and 2019, and due to this explosion in data usage, mobile operators are having serious concerns focusing on public Wireless Fidelity (Wi-Fi) and other alternative technologies. Visible Light Communication (VLC) is a recent promising technology complementary to RF spectrum which operates at the visible light spectrum band (roughly 400 THz to 780 THz) and it has 10,000 times bigger size than radio waves (roughly 3 kHz to 300 GHz). Due to this tremendous potential, VLC has captured a lot of interest recently as there is already an extensive deployment of energy efficient Light Emitting Diodes (LEDs). The advancements in LED technology with fast nanosecond switching times is also very encouraging. In this work, we present hybrid RF/VLC architecture which is capable of providing simultaneous lighting and communication coverage in an indoor setting. The architecture consists of a multi-element hemispherical bulb design, where it is possible to transmit multiple data streams from the multi-element hemispherical bulb using LED modules. We present the detailed components of the architecture and make simulations considering various VLC transmitter configurations. Also, we devise an approach for an efficient bulb design mechanism to maintain both illumination and communication at a satisfactory rate, and analyze it in the case of two users in a room. The approach involves formulating an optimization problem and tackling the problem using a simple partitioning algorithm. The results indicate that good link quality and high spatial reuse can be maintained in a typical indoor communication setting

Multi-dimensional modeling and simulation of semiconductor nanophotonic devices

Self-consistent modeling and multi-dimensional simulation of semiconductor nanophotonic devices is an important tool in the development of future integrated light sources and quantum devices. Simulations can guide important technological decisions by revealing performance bottlenecks in new device concepts, contribute to their understanding and help to theoretically explore their optimization potential. The efficient implementation of multi-dimensional numerical simulations for computer-aided design tasks requires sophisticated numerical methods and modeling techniques. We review recent advances in device-scale modeling of quantum dot based single-photon sources and laser diodes by self-consistently coupling the optical Maxwell equations with semiclassical carrier transport models using semi-classical and fully quantum mechanical descriptions of the optically active region, respectively. For the simulation of realistic devices with complex, multi-dimensional geometries, we have developed a novel hp-adaptive finite element approach for the optical Maxwell equations, using mixed meshes adapted to the multi-scale properties of the photonic structures. For electrically driven devices, we introduced novel discretization and parameter-embedding techniques to solve the drift-diffusion system for strongly degenerate semiconductors at cryogenic temperature. Our methodical advances are demonstrated on various applications, including vertical-cavity surface-emitting lasers, grating couplers and single-photon sources

A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers

Plasmon-mediated Energy Conversion in Metal Nanoparticle-doped Hybrid Nanomaterials

Climate change and population growth demand long-term solutions for clean water and energy. Plasmon-active nanomaterials offer a promising route towards improved energetics for efficient chemical separation and light harvesting schemes. Two material platforms featuring highly absorptive plasmonic gold nanoparticles (AuNPs) are advanced herein to maximize photon conversion into thermal or electronic energy. Optical extinction, attributable to diffraction-induced internal reflection, was enhanced up to 1.5-fold in three-dimensional polymer films containing AuNPs at interparticle separations approaching the resonant wavelength. Comprehensive methods developed to characterize heat dissipation following plasmonic absorption was extended beyond conventional optical and heat transfer descriptions, where good agreement was obtained between measured and estimated thermal profiles for AuNP-polymer dispersions. Concurrently, in situ reduction of AuNPs on two-dimensional semiconducting tungsten disulfide (WS2) addressed two current material limitations for efficient light harvesting: low monolayer content and lack of optoelectronic tunability. Order-of-magnitude increases in WS2 monolayer content, enhanced broadband optical extinction, and energetic electron injection were probed using a combination of spectroscopic techniques and continuum electromagnetic descriptions. Together, engineering these plasmon-mediated hybrid nanomaterials to facilitate local exchange of optical, thermal, and electronic energy supports design and implementation into several emerging sustainable water and energy applications