A Simulation of Auroral Absorption

HF radio transmissions propagate long distances by reflecting off the ionosphere. At high latitudes radio propagation is strongly affected by the northern lights (aurora borealis), which causes ionization at low altitudes and hence the absorption of radio waves. Models of this process are still in a primitive state. A simulation of radio wave propagation was created in order to test Foppiano and Bradley\u27s empirical model of auroral absorption. The simulation attempts to predict the net absorption of signals at a receiver by simulating a large number of transmitters, even though the exact sources of the signals are unknown. Although the simulation takes into account auroral and nonauroral absorption as well as other sources of path loss, the analysis focuses on the nighttime aurora. An intelligent search algorithm is used in order to efficiently adjust the model to best fit the data. The output of the simulation is qualitatively and quantitatively compared to signal levels observed with HF radio receivers located in northern Canada. The analysis allows us to develop alternative models of auroral absorption which account for the level of geomagnetic activity, and these are compared to the standard Foppiano and Bradley model

Integrating a Lighting System With Objective Light Movement

When applying a light design within computer graphics software, there is no clear-cut way to achieve accurate and noticeable light movement without rigorous work animating the lighting tools by hand. This task can be arduous and involve constant test renders throughout a single frame range. This project strives to explain the production of a short animated feature, by incorporating a video-based lighting system, which is intended to assist with scenes that require objective light movement. The video-based lighting method will strive to minimize (not eliminate) the need to animate by hand light motion from environment lighting. Since lighting design is closely tied to artistic aesthetics, the proposed method must also be flexible enough to successfully light different scenarios with an intended artistic vision. While the video-based lighting system is the focus of this project, it will not be the only method used to light this animated feature. This paper will briefly cover the production of the short animation as a whole, since almost all aspects of the production pipeline provide motivation for the lighting

Control-based Scheduling in a Distributed Stream Processing System

Stream processing systems receive continuous streams of messages with raw information and produce streams of messages with processed information. The utility of a stream-processing system depends, in part, on the accuracy and timeliness of the output. Streams in complex event processing systems are processed on distributed systems; several steps are taken on different processors to process each incoming message, and messages may be enqueued between steps. This paper deals with the problems of distributed dynamic control of streams to optimize the total utility provided by the system. A challenge of distributed control is that timeliness of output depends only on the total end-toend time and is otherwise independent of the delays at each separate processor whereas the controller for each processor takes action to control only the steps on that processor and cannot directly control the entire network. This paper identifies key problems in distributed control and analyzes two scheduling algorithms that help in an initial analysis of a difficult problem

IOT Stream Analytics Platform

The Internet of Things (IoT) is changing people’s surrounding physical world into an information ecosystem that facilitate our everyday life. Billions of smart objects become data-generating “things” that can sense environmental changes and report their sensed data. Leveraging the huge amount of sensory information is a key issue to realize the IoT solutions in many areas. Adequate technologies are required for data collection, transmission, data processing, analysis, reporting, and advanced querying. In this thesis, an IoT Stream Analytics Platform that supports IoT application and service development is proposed: it provides user applications a way to capture flowing data from multitudes of data sources and provide analytical insights in real time based on user needs. Developers can conveniently build their IoT applications on this platform without having to consider the diversity and complexity of smart devices and their underlying networks

Evaluating DHT-Based Service Placement for Stream Based Overlays

Stream-based overlay networks (SBONs) are one approach to implementing large-scale stream processing systems. A fundamental consideration in an SBON is that of service placement, which determines the physical location of in-network processing services or operators, in such a way that network resources are used efficiently. Service placement consists of two components: node discovery, which selects a candidate set of nodes on which services might be placed, and node selection, which chooses the particular node to host a service. By viewing the placement problem as the composition of these two processes we can trade-off quality and efficiency between them. A bad discovery scheme can yield a good placement, but at the cost of an expensive selection mechanism. Recent work on operator placement [3, 9] proposes to leverage routing paths in a distributed hash table (DHT) to obtain a set of candidate nodes for service placement. We evaluate the appropriateness of using DHT routing paths for service placement in an SBON, when aiming to minimize network usage. For this, we consider two DHT-based algorithms for node discovery, which use either the union or intersection of DHT routing paths in the SBON, and compare their performance to other techniques. We show that current DHT-based schemes are actually rather poor node discovery algorithms, when minimizing network utilization. An efficient DHT may not traverse enough hops to obtain a sufficiently large candidate set for placement. The union of DHT routes may result in a low-quality set of discovered nodes that requires an expensive node selection algorithm. Finally, the intersection of DHT routes relies on route convergence, which prevents the placement of services with a large fan-in.Engineering and Applied Science

The Effects of Geomagnetic Disturbances on Electrical Power Systems

Solar storms that generate coronal mass ejections are a cause for concern due to the damage that they cause in high voltage power grids. Geomagnetically induced currents can be introduced onto the grid and cause many adverse effects. The vulnerability of the bulk electric power systems to such events has increased during the past few decades because the power system transmission lines have become more interconnected and have increased in length. Real and reactive power flows, voltage fluctuations, frequency shifts, undesired relay operations, higher order harmonic currents, undesired damage to assets and failure of assets are all possible outcomes from a large geomagnetic disturbance. A 100 year solar storm could cause mass blackouts and colossal damage to any high voltage power grid, if proper monitoring and mitigation techniques are not used. This thesis presents an in-depth background on geomagnetic disturbances and how they affect the electrical power grid. The thesis will model geomagnetic disturbances on a theoretical grid using the simulation software OpenDSS. The thesis will also discuss monitoring and mitigation techniques that can be applied to the power grid to lessen the chance of failure or damage to assets, and analyze real world data collected from a Midwestern solar storm that had an effect on two power transformers equipped with online monitoring

Curracurrong: a stream processing system for distributed environments

Advances in technology have given rise to applications that are deployed on wireless sensor networks (WSNs), the cloud, and the Internet of things. There are many emerging applications, some of which include sensor-based monitoring, web traffic processing, and network monitoring. These applications collect large amount of data as an unbounded sequence of events and process them to generate a new sequences of events. Such applications need an adequate programming model that can process large amount of data with minimal latency; for this purpose, stream programming, among other paradigms, is ideal. However, stream programming needs to be adapted to meet the challenges inherent in running it in distributed environments. These challenges include the need for modern domain specific language (DSL), the placement of computations in the network to minimise energy costs, and timeliness in real-time applications. To overcome these challenges we developed a stream programming model that achieves easy-to-use programming interface, energy-efficient actor placement, and timeliness. This thesis presents Curracurrong, a stream data processing system for distributed environments. In Curracurrong, a query is represented as a stream graph of stream operators and communication channels. Curracurrong provides an extensible stream operator library and adapts to a wide range of applications. It uses an energy-efficient placement algorithm that optimises communication and computation. We extend the placement problem to support dynamically changing networks, and develop a dynamic program with polynomially bounded runtime to solve the placement problem. In many stream-based applications, real-time data processing is essential. We propose an approach that measures time delays in stream query processing; this model measures the total computational time from input to output of a query, i.e., end-to-end delay

Curracurrong: a stream processing system for distributed environments

Advances in technology have given rise to applications that are deployed on wireless sensor networks (WSNs), the cloud, and the Internet of things. There are many emerging applications, some of which include sensor-based monitoring, web traffic processing, and network monitoring. These applications collect large amount of data as an unbounded sequence of events and process them to generate a new sequences of events. Such applications need an adequate programming model that can process large amount of data with minimal latency; for this purpose, stream programming, among other paradigms, is ideal. However, stream programming needs to be adapted to meet the challenges inherent in running it in distributed environments. These challenges include the need for modern domain specific language (DSL), the placement of computations in the network to minimise energy costs, and timeliness in real-time applications. To overcome these challenges we developed a stream programming model that achieves easy-to-use programming interface, energy-efficient actor placement, and timeliness. This thesis presents Curracurrong, a stream data processing system for distributed environments. In Curracurrong, a query is represented as a stream graph of stream operators and communication channels. Curracurrong provides an extensible stream operator library and adapts to a wide range of applications. It uses an energy-efficient placement algorithm that optimises communication and computation. We extend the placement problem to support dynamically changing networks, and develop a dynamic program with polynomially bounded runtime to solve the placement problem. In many stream-based applications, real-time data processing is essential. We propose an approach that measures time delays in stream query processing; this model measures the total computational time from input to output of a query, i.e., end-to-end delay

When Two Choices Are not Enough: Balancing at Scale in Distributed Stream Processing

Carefully balancing load in distributed stream processing systems has a fundamental impact on execution latency and throughput. Load balancing is challenging because real-world workloads are skewed: some tuples in the stream are associated to keys which are significantly more frequent than others. Skew is remarkably more problematic in large deployments: more workers implies fewer keys per worker, so it becomes harder to "average out" the cost of hot keys with cold keys. We propose a novel load balancing technique that uses a heaving hitter algorithm to efficiently identify the hottest keys in the stream. These hot keys are assigned to $d \geq 2$ choices to ensure a balanced load, where $d$ is tuned automatically to minimize the memory and computation cost of operator replication. The technique works online and does not require the use of routing tables. Our extensive evaluation shows that our technique can balance real-world workloads on large deployments, and improve throughput and latency by $\mathbf{150\%}$ and $\mathbf{60\%}$ respectively over the previous state-of-the-art when deployed on Apache Storm.Comment: 12 pages, 14 Figures, this paper is accepted and will be published at ICDE 201