Statistical Power Supply Dynamic Noise Prediction in Hierarchical Power Grid and Package Networks

One of the most crucial high performance systems-on-chip design challenge is to front their power supply noise sufferance due to high frequencies, huge number of functional blocks and technology scaling down. Marking a difference from traditional post physical-design static voltage drop analysis, /a priori dynamic voltage drop/evaluation is the focus of this work. It takes into account transient currents and on-chip and package /RLC/ parasitics while exploring the power grid design solution space: Design countermeasures can be thus early defined and long post physical-design verification cycles can be shortened. As shown by an extensive set of results, a carefully extracted and modular grid library assures realistic evaluation of parasitics impact on noise and facilitates the power network construction; furthermore statistical analysis guarantees a correct current envelope evaluation and Spice simulations endorse reliable result

Stopping Silent Sneaks: Defending against Malicious Mixes with Topological Engineering

Mixnets provide strong meta-data privacy and recent academic research and industrial projects have made strides in making them more secure, performance, and scalable. In this paper, we focus our work on stratified Mixnets -- a popular design with real-world adoption -- and identify that there still exist heretofore inadequately explored practical aspects such as: relay sampling and topology placement, network churn, and risks due to real-world usage patterns. We show that, due to the lack of incorporating these aspects, Mixnets of this type are far more susceptible to user deanonymization than expected. In order to reason and resolve these issues, we model Mixnets as a three-stage ``Sample-Placement-Forward'' pipeline, and using the results of our evaluation propose a novel Mixnet design, Bow-Tie. Bow-Tie mitigates user deanonymization through a novel adaption of Tor's guard design with an engineered guard layer and client guard-logic for stratified mixnets. We show that Bow-Tie has significantly higher user anonymity in the dynamic setting, where the Mixnet is used over a period of time, and is no worse in the static setting, where the user only sends a single message. We show the necessity of both the guard layer and client guard-logic in tandem as well as their individual effect when incorporated into other reference designs. Ultimately, Bow-Tie is a significant step towards addressing the gap between the design of Mixnets and practical deployment and wider adoption because it directly addresses real-world user and Mixnet operator concerns

Distributed resource integration analysis and network design of electric power distribution systems

The integration of high percentages of distributed energy resources and controllable loads into the distribution system coupled with the strict power quality and service reliability requirements at the power distribution level are necessitating a significant change in the planning, operation and control of the traditional power distribution system. The future power distribution circuits should be able to accommodate the new technologies while simultaneously providing a desired level of power quality and service reliability to the customers. This thesis aims to address the current and future grid requirements of both existing as well as new distribution systems with regard to power quality and service reliability issues. Several methods are proposed to evaluate and mitigate power quality and service reliability concerns due to the integration of smart grid technologies into both existing and new distribution circuits. Notably, for the existing distribution circuits, integration studies are simulated to analyze and mitigate the impacts of electric vehicle loads and photovoltaic generation on the distribution voltages. Furthermore, the problem of siting, sizing and deployment of distributed energy storage systems in meeting distribution planning requirements with regard to integrating distributed generation and providing contingency requirements is also addressed. A new distribution system both grid-connected and operating in islanded mode, however, could be designed to the new requirements. The new distribution circuit could be designed to meet the power quality and service reliability standards directly, thus more efficiently mitigating the concerns. In the thesis, the new distribution circuit design is approached from the perspective of maximizing the service reliability. For the new distribution circuit, approaches to reliability based distribution circuit design are proposed.Electrical and Computer Engineerin

MT-EA4Cloud: A Methodology For testing and optimising energy-aware cloud systems

Currently, using conventional techniques for checking and optimising the energy consumption in cloud systems is unpractical, due to the massive computational resources required. An appropriate test suite focusing on the parts of the cloud to be tested must be efficiently synthesised and executed, while the correctness of the test results must be checked. Additionally, alternative cloud configurations that optimise the energetic consumption of the cloud must be generated and analysed accordingly, which is challenging. To solve these issues we present MT-EA4Cloud, a formal approach to check the correctness – from an energy-aware point of view – of cloud systems and optimise their energy consumption. To make the checking of energy consumption practical, MT-EA4Cloud combines metamorphic testing, evolutionary algorithms and simulation. Metamorphic testing allows to formally model the underlying cloud infrastructure in the form of metamorphic relations. We use metamorphic testing to alleviate both the reliable test set problem, generating appropriate test suites focused on the features reflected in the metamorphic relations, and the oracle problem, using the metamorphic relations to check the generated results automatically. MT-EA4Cloud uses evolutionary algorithms to efficiently guide the search for optimising the energetic consumption of cloud systems, which can be calculated using different cloud simulatorsThis work was supported by the Spanish MINECO/FEDER projects DArDOS, FAME and MASSIVE under Grants TIN2015-65845-C3-1-R, RTI2018-093608-B-C31 and RTI2018-095255- B-I00, and the Comunidad de Madrid project FORTE-CM under grant S2018/TCS-4314. The first author is also supported by the Universidad Complutense de Madrid Santander Universidades grant (CT17/17-CT18/17

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

Digital design techniques for dependable High-Performance Computing

L'abstract è presente nell'allegato / the abstract is in the attachmen

Anomaly Detection in BACnet/IP managed Building Automation Systems

Building Automation Systems (BAS) are a collection of devices and software which manage the operation of building services. The BAS market is expected to be a $19.25 billion USD industry by 2023, as a core feature of both the Internet of Things and Smart City technologies. However, securing these systems from cyber security threats is an emerging research area. Since initial deployment, BAS have evolved from isolated standalone networks to heterogeneous, interconnected networks allowing external connectivity through the Internet. The most prominent BAS protocol is BACnet/IP, which is estimated to hold 54.6% of world market share. BACnet/IP security features are often not implemented in BAS deployments, leaving systems unprotected against known network threats. This research investigated methods of detecting anomalous network traffic in BACnet/IP managed BAS in an effort to combat threats posed to these systems. This research explored the threats facing BACnet/IP devices, through analysis of Internet accessible BACnet devices, vendor-defined device specifications, investigation of the BACnet specification, and known network attacks identified in the surrounding literature. The collected data were used to construct a threat matrix, which was applied to models of BACnet devices to evaluate potential exposure. Further, two potential unknown vulnerabilities were identified and explored using state modelling and device simulation. A simulation environment and attack framework were constructed to generate both normal and malicious network traffic to explore the application of machine learning algorithms to identify both known and unknown network anomalies. To identify network patterns between the generated normal and malicious network traffic, unsupervised clustering, graph analysis with an unsupervised community detection algorithm, and time series analysis were used. The explored methods identified distinguishable network patterns for frequency-based known network attacks when compared to normal network traffic. However, as stand-alone methods for anomaly detection, these methods were found insufficient. Subsequently, Artificial Neural Networks and Hidden Markov Models were explored and found capable of detecting known network attacks. Further, Hidden Markov Models were also capable of detecting unknown network attacks in the generated datasets. The classification accuracy of the Hidden Markov Models was evaluated using the Matthews Correlation Coefficient which accounts for imbalanced class sizes and assess both positive and negative classification ability for deriving its metric. The Hidden Markov Models were found capable of repeatedly detecting both known and unknown BACnet/IP attacks with True Positive Rates greater than 0.99 and Matthews Correlation Coefficients greater than 0.8 for five of six evaluated hosts. This research identified and evaluated a range of methods capable of identifying anomalies in simulated BACnet/IP network traffic. Further, this research found that Hidden Markov Models were accurate at classifying both known and unknown attacks in the evaluated BACnet/IP managed BAS network