A Churn for the Better: Localizing Censorship using Network-level Path Churn and Network Tomography

Recent years have seen the Internet become a key vehicle for citizens around the globe to express political opinions and organize protests. This fact has not gone unnoticed, with countries around the world repurposing network management tools (e.g., URL filtering products) and protocols (e.g., BGP, DNS) for censorship. However, repurposing these products can have unintended international impact, which we refer to as "censorship leakage". While there have been anecdotal reports of censorship leakage, there has yet to be a systematic study of censorship leakage at a global scale. In this paper, we combine a global censorship measurement platform (ICLab) with a general-purpose technique -- boolean network tomography -- to identify which AS on a network path is performing censorship. At a high-level, our approach exploits BGP churn to narrow down the set of potential censoring ASes by over 95%. We exactly identify 65 censoring ASes and find that the anomalies introduced by 24 of the 65 censoring ASes have an impact on users located in regions outside the jurisdiction of the censoring AS, resulting in the leaking of regional censorship policies

Emulating Software Defined Network Using Mininet and OpenDaylight Controller Hosted on Amazon Web Services Cloud Platform to Demonstrate a Realistic Programmable Network.

Conference paper written by masters student in satisfaction of masters degreeFollow the link at the top of the record to access the full-text of this item on the publisher's web site.In this paper, a Software Defined Network was created in Mininet using python script. An external interface was added in the form of an OpenDaylight controller to enable communication with the network outside of Mininet. The OpenDaylight controller was hosted on the Amazon Web Services elastic computing node. This controller is used as a control plane device for the switch within Mininet. The OpenDaylight controller was able to create the flows to facilitate communication between the hosts in Mininet and the webserver in the real-life network. In order to test the network, a real life network in the form of a webserver hosted on the Emulated Virtual Environment – Next Generation (EVE-NG) software was connected to Mininet.The University of Johannesburg The University of South AfricaCollege of Engineering, Science and Technolog

An On-chip PVT Resilient Short Time Measurement Technique

As the CMOS technology nodes continue to shrink, the challenges of developing manufacturing tests for integrated circuits become more difficult to address. To detect parametric faults of new generation of integrated circuits such as 3D ICs, on-chip short-time intervals have to be accurately measured. The accuracy of an on-chip time measurement module is heavily affected by Process, supply Voltage, and Temperature (PVT) variations. This work presents a new on-chip time measurement scheme where the undesired effects of PVT variations are attenuated significantly. To overcome the effects of PVT variations on short-time measurement, phase locking methodology is utilized to implement a robust Vernier delay line. A prototype Time-to-Digital Converter (TDC) has been fabricated using TSMC 0.180 µm CMOS technology and experimental measurements have been carried out to verify the performance parameters of the TDC. The measurement results indicate that the proposed solution reduces the effects of PVT variations by more than tenfold compared to a conventional on-chip TDC. A coarse-fine time interval measurement scheme which is resilient to the PVT variations is also proposed. In this approach, two Delay Locked Loops (DLLs) are utilized to minimize the effects of PVT on the measured time intervals. The proposed scheme has been implemented using CMOS 65nm technology. Simulation results using Advanced Design System (ADS) indicate that the measurement resolution varies by less than 0.1ps with ±15% variations of the supply voltage. The proposed method also presents a robust performance against process and temperature variations. The measurement accuracy changes by a maximum of 0.05ps from slow to fast corners. The implemented TDC presents a robust performance against temperature variations too and its measurement accuracy varies a few femto-seconds from -40 ºC to +100 ºC. The principle of robust short-time measurement was used in practice to design and implement a state-of-the-art Coordinate Measuring Machine (CMM) for an industry partner to measure geometrical features of transmission parts with micrometer resolution. The solution developed for the industry partner has resulted in a patent and a product in the market. The on-chip short-time measurement technology has also been utilized to develop a solution to detect Hardware Trojans

Combining VSIDS and CHB Using Restarts in SAT

Conflict Driven Clause Learning (CDCL) solvers are known to be efficient on structured instances and manage to solve ones with a large number of variables and clauses. An important component in such solvers is the branching heuristic which picks the next variable to branch on. In this paper, we evaluate different strategies which combine two state-of-the-art heuristics, namely the Variable State Independent Decaying Sum (VSIDS) and the Conflict History-Based (CHB) branching heuristic. These strategies take advantage of the restart mechanism, which helps to deal with the heavy-tailed phenomena in SAT, to switch between these heuristics thus ensuring a better and more diverse exploration of the search space. Our experimental evaluation shows that combining VSIDS and CHB using restarts achieves competitive results and even significantly outperforms both heuristics for some chosen strategies

The benefits and challenges of renewables on the electric grid and opportunities for systems integration and demand side management

Environmental policies, reduced manufacturing costs, and technology improvements have all contributed to the growing installation of wind turbines and solar photovoltaic arrays in the electric grid. While these new sources of renewable electrical power provide environmental and economic benefits to the electric grid, they also complicate the balancing of supply and demand required to reliably operate the grid. The seasonal, daily, and sub-hourly fluctuations in the energy output of wind and solar generators must be compensated by operating the existing power plant fleet more flexibly or by providing more flexible sources of electricity demand. This dissertation categorizes and quantifies this compensation by studying the "flexibility requirements'' imposed by wind and solar generation, approximates the economically optimal capacities of regional wind and solar resources in the grid, and explores the ability of a central utility plant to add a flexible source of demand to the electric grid system. These topics are covered in the four chapters described below. Chapter 3 utilizes a unit commitment and dispatch (UC&D) model to simulate large solar generation assets with different geographic locations and orientations. The simulations show the sensitivity of the wholesale energy price, reserve market prices, total dispatch cost, fuel mix, emissions, and water use to changes in net load flexibility requirements. The results show that generating 22,500 GWh of solar energy in a 2011 simulation of the Electric Reliability Council of Texas (ERCOT) reduces total dispatch cost by approximately $900 Million (a 10.3$10 Million (a 3% increase). The results also show that solar PV reduces water consumption, water withdrawals, and CO₂, NO [subscript x], and SO [subscript x] emissions. Installing sufficient solar panel capacity to generate that much electricity also reduces peak load by 4% but increases net load volatility by 40--79% and ramping by 11--33%. In addition, west-located, west-oriented solar resources reduce total dispatch cost more than the other simulated solar scenarios. The west-located, west-oriented solar simulation required greater system flexibility, but utilized more low-cost generators and fewer high-cost generators for energy production than other simulated scenarios. These results suggest that the mix of energy provided by different generation technologies influences the dispatch cost more than the net load flexibility requirements. Chapter 4 develops a quantitative framework for calculating flexibility requirements and performs a statistical analysis of load, wind, and solar data from the Electric Reliability Council of Texas (ERCOT) to show how wind and solar capacity impacts these grid flexibility requirements. Growing wind capacity shows only minor correlation with increasing flexibility requirements, but shows some correlation with ramp down rates and daily volatility in the net load. Growing solar capacity shows a direct correlation with increasing flexibility requirements if load patterns do not change. While adding 15.7 GW of wind power had only minor effects on system flexibility requirements, adding 14.5 GW of solar to the ERCOT grid increases maximum 1-hr ramp rates by 135%, 3-hr ramp rates by 30%, ramp factors by 140%, 1-hr volatility by 100%, and 1-day volatility by 30%. Wind and solar impact flexibility requirements at different times of the day: wind tends to intensify demand-driven flexibility events by ramping up energy production at night when demand is decreasing and ramping down energy production in the morning when demand is increasing, while solar tends to intensify flexibility requirements due to its quick changes in energy output driven by the rising and setting sun. Adding wind to a system with large amounts of solar does not tend to increase flexibility requirements except for the daily volatility. The geographic location and orientation of solar arrays also influences flexibility requirements, with fixed, southeast-facing panels providing a significant reduction. These results can inform strategies for managing the grid flexibility requirements created by growing renewable capacity. Chapter 5 develops a model for calculating the optimal amount of transmission, wind, and solar capacity that should be built in a grid's different regions. It also presents a framework for choosing CO₂ prices by balancing increasing system cost and flexibility requirements with CO₂ emissions reductions. In a simulation of the ERCOT grid, the model suggests a 60 $/ton CO₂ price and an optimal investment of 27.0 GW of transmission capacity to five different regions. These regions install a total of 26.6 GW of wind and 11.1 GW of solar, representing a grid with about 60% thermal and 40% renewable capacity. This renewable mix produces 110 TWh of energy per year, 34% of the total electricity demand. The grid emits 82.2 million tons of CO₂ per year under this scenario, a 65% reduction from the 237 million tons produced when no renewable capacity is installed. At the optimal renewable development solution, all coal and natural gas boiler generators have capacity factors less than 20% with many of them not being dispatched at all. While these results are specific to ERCOT, the methods and model can be used by any grid considering renewable energy capacity expansion. Chapter 6 develops a mixed-integer linear program for modeling the optimal equipment capacity and dispatch of a central utility plant (CUP) in a residential neighborhood and its ability to improve rooftop solar integration. The CUP equipment includes a microturbine, battery, chiller plant, and cooling storage. The CUP model is exposed to a variety of electricity rate structures to see how they influence its operation. The model finds the optimal capacity for each piece of CUP equipment, optimizing their hourly dispatch to meet neighborhood cooling and electric demand while maximizing profit. In an Austin, TX, USA base case, the neighborhood benefits economically by including the CUP, although the CUP demonstrates limited potential to integrate high penetrations of rooftop solar resources. While peak demand and reverse power flows are reduced under all tested rate structures, the CUP worsens net demand ramp rates. A time-of-use rate with no demand charge and moderate differences between off-peak and on-peak prices balances the output parameters, reducing reverse power flows by 43%, peak demand by 51%, and annual cost by 9.1% versus the ``No CUP'' base case while limiting net demand ramp rate increase to 84% more than the base case. Building a clean, resilient, and reliable electric grid for the future is a worthwhile endeavor that will require innovative supply-side and demand-side solutions for integrating the intermittent power output of renewable generation into the electric grid. As a cohesive document, this dissertation communicates the scale and severity of the flexibility requirements that will be required to operate systems with large amounts of wind and solar generation and explores one demand-side method for providing that needed flexibility. There are many opportunities to expand these analyses and explore new sources of grid flexibility in future work.Mechanical Engineerin

Techno-economic methods for analyzing the energetic and economic effects of solar, storage, and demand response

Growing population, changing climate, urbanization, and rising economic activities have led to an overall increase in electricity demand. Maintaining the balance between supply and this increasing demand often necessitates the usage of old, inefficient, and environmentally-polluting generators as well as the construction of expensive generation, transmission, and distribution infrastructure. Demand response initiatives (e.g. time-varying electricity prices) and distributed energy resources (DERs), like solar photovoltaic panels and onsite energy storage systems, can help offset a portion of this demand while simultaneously reducing harmful emissions. DERs additionally provide a variety of value streams including peak load reduction, energy arbitrage, real time price dispatch, demand charge reduction, congestion management, voltage support, etc. The impact of price-based demand response and DERs at the electricity distribution level is assessed in this dissertation through the following three studies: (1) quantifying the reduction in 4 coincident peak (4CP) loads and Transmission Cost of Service (TCOS) obligations of electric utilities using local distributed solar and storage, (2) evaluating the peak load reduction/shift potential of time-varying electricity pricing in the residential sector, and (3) investigating the combined energetic and economic potential of DERs and time-varying electricity pricing in the residential sector. When the Electric Reliability Council of Texas (ERCOT) peaks for a single 15-minute interval during each summer month between June and September, the loads of individual Distribution Service Providers (DSPs) in the same time interval are recorded. The averages of these DSP loads, defined as 4CP loads, are used to calculate TCOS obligations that each DSP must pay Transmission Service Providers (TSPs) in the next calendar year as compensation for using their transmission infrastructure. First, a generalized tool is built to forecast the change of 4CP loads and corresponding TCOS obligations for electric utilities within ERCOT based on varying amounts of solar and storage capacity. The tool is illustrated by using empirical electricity demand data from the municipally-owned utility in Austin, TX (Austin Energy) and solar generation data from the PVWatts calculator developed by the National Renewable Energy Laboratory. TCOS obligations can be on the order of tens of millions of dollars. Results indicate that solar and storage capacity can substantially lower these payments. For example, a 20 MW increase in local solar capacity in 2018 would reduce Austin Energy’s payment by an estimated $180,000 for each subsequent year. By using the novel approach of incorporating coincident peak demand charge reductions at the distribution level, the economic value of local generation and storage is highlighted. Next, a convex optimization model is developed to analyze the potential for time-varying electricity rate structures to reduce and/or shift peak demand in the residential sector. In this model, a household with four major appliances minimizes electricity costs, with marginally increasing penalties for deviating from temperature set-points or operating appliances at inconvenient times. The four specific appliances included are: heating, ventilation and air-conditioning (HVAC) systems, electric water heaters (EWHs), electric vehicles (EVs), and pool pumps (PPs). The study incorporates a one-parameter thermal model of the home and the electric water heater, so that the penalties can apply to the room and water temperatures rather than the total appliance loads. Analysis is performed on a community of 100 single-family detached homes in Austin, TX. These homes each host a combination of the four end-use devices while some also have onsite solar panels. Results show that dynamic pricing effectively shifts the residential peak away from the time of overall peak load across the electricity system, but can have the adverse impact of making the residential peak higher. The energy consumption does not differ significantly across the different rate structures. Thus, it can be inferred that the time-varying rates encourage customers to concentrate their electricity demand within low-price hours to the extent possible without incurring significant inconvenience. By incorporating the novel approach of including monetary value of customer behavior in price-based demand response models, this study builds a tool to realistically quantify peak load reduction and shifts in the residential sector. Finally, the convex optimization model is extended to consider larger sets of distributed technologies that might be deployed in homes and investigate how different combinations of these technologies affect peak grid load, energy consumption from the grid, and emissions in the residential sector under time-varying pricing structures. In the model, households with varied amalgamations of distributed energy technologies minimize electricity costs, amortized capital, and operational costs over a year, with marginally increasing penalties for deviating from room temperature set-points. The four technologies considered are: solar photovoltaic (PV) panels, lithium-ion batteries, ice cold thermal energy storage (CTES), and smart thermostats. Results show that from an economic perspective, it is optimal for residential customers to install solar panels under tiered rates, time-of-use rates, and critical peak prices while it is cheapest to own a combination of solar panels and smart thermostats when real-time prices and demand charges are in effect. The capital and installation costs of both storage systems are still too high to make them economically profitable investments for typical residential customers. Additionally, solar panels are the main instruments to reduce energy purchased from the grid and carbon dioxide emissions under all pricing schemes. Adding smart thermostats can reduce these metrics to a greater extent by making the home energy-efficient. Further, while the energetic effect of the two storage systems can be favorable or detrimental depending upon the load profile of the particular household and the pricing structure, lithium-ion batteries are the main instruments to avoid high demand charges by spreading the demand in the home (and power bought from the grid) evenly to the extent possible without incurring significant customer discomfort. Thus, this study recommends that residential customers invest in solar panels and smart thermostats to minimize overall annual expenditure and make their homes environmentally efficient. Further, as an effective peak load control mechanism, electric utilities should offer significant rebates to encourage residential customer investment in storage systems in addition to subjecting them to demand charges. Electricity generation from intermittent renewable energy sources has grown rapidly worldwide. DER installation levels continue to rise with the decline in capital costs of energy storage systems and local renewable generation assets, the growth of supportive government policies, and rising concerns about climate change among the masses. Additionally, electric utilities are increasingly employing demand response initiatives to curtail and/or shift peak demand. As a whole, the body of work developed in this dissertation can be used by electric utilities to make optimal decisions about dynamic rate design and policies for increased DER adoption. It can also be used by residential electricity customers to maneuver their own energy consumption patterns and assess the economic viability of investing in DERs.Mechanical Engineerin

Asynchronous Advanced Encryption Standard Hardware with Random Noise Injection for Improved Side-Channel Attack Resistance

This work presents the design, hardware implementation, and performance analysis of novel asynchronous AES (advanced encryption standard) Key Expander and Round Function, which offer increased side-channel attack (SCA) resistance. These designs are based on a delay-insensitive (DI) logic paradigm known as null convention logic (NCL), which supports useful properties for resisting SCAs including dual-rail encoding, clock-free operation, and monotonic transitions. Potential benefits include reduced and more uniform switching activities and reduced signal-to-noise (SNR) ratio. A novel method to further augment NCL AES hardware with random voltage scaling technique is also presented for additional security. Thereby, the proposed components leak significantly less side-channel information than conventional clocked approaches. To quantitatively verify such improvements, functional verification and WASSO (weighted average simultaneous switching output) analysis have been carried out on both conventional synchronous approach and the proposed NCL based approach using Mentor Graphics ModelSim and Xilinx simulation tools. Hardware implementation has been carried out on both designs exploiting a specified side-channel attack standard evaluation FPGA board, called SASEBO-GII, and the corresponding power waveforms for both designs have been collected. Along with the results of software simulations, we have analyzed the collected waveforms to validate the claims related to benefits of the proposed cryptohardware design approach

Design Space Exploration and Resource Management of Multi/Many-Core Systems

The increasing demand of processing a higher number of applications and related data on computing platforms has resulted in reliance on multi-/many-core chips as they facilitate parallel processing. However, there is a desire for these platforms to be energy-efficient and reliable, and they need to perform secure computations for the interest of the whole community. This book provides perspectives on the aforementioned aspects from leading researchers in terms of state-of-the-art contributions and upcoming trends

Zoom Out and See Better: Scalable Message Tracing for Post-Silicon SoC Debug

We present a method for selecting trace messages for post-silicon validation of System-on-Chip (SoC). Our message selection is guided by specifications of interacting flows in common user applications. In current practice, such messages are selected based on designer expertise. We formulate the problem as an optimization of mutual information gain and trace buffer utilization. Our approach scales to systems far beyond the capacity of current signal selection techniques. We achieve an average trace buffer utilization of 98.96% with an average flow specification coverage of 94.3% and an average bug localization to only 21.11% of the potential root causes in our large-scale debugging effort. We present efficacy of our selected messages in debugging and root cause analysis using five realistic case studies consisting of complex and subtle bugs from the OpenSPARC T2 processor.IBMOpe