Place and Food: A Relational Analysis of Personal Food Environments, Meanings of Place and Diet Quality

This study investigates the relational layers of meanings that people experience at places in their food environment, and how individuals express a sense of place through their collective interactions with, and understandings of, food places. It also explores the patterns of difference among these meanings of place and sense of place in terms of their potential association with dietary quality. The context of this inquiry was two-fold: first, the need identified by several population health researchers and to re-imagine place as relational and include it in the study of behavioural responses to the changing food environment; and secondly, my interest in examining how existing theories of place and sense of place can help explicate people-place-food interactions, a hitherto untested application. Methodologically, this study follows a mixed-methods approach, and is based on semi-structured interviews with a diverse sample of inner-city residents of Waterloo, Ontario. To focus on individuals’ interactions with their food environment, attention is centred on the set of places that they choose to visit routinely to buy food or eat out: a hypothetical construct termed their “personal food environment” (PFE). The mapping of the PFE at each interview became an elicitation tool to verbally draw out the subjective, social and spatial meanings embedded in each place. Analysis with grounded theory also revealed the roles that food plays in shaping facets of personal sense of place, including feelings of belonging or alienation, active involvement and impulsiveness, and, for some people, a sense of connectedness on a global and/or local scale. A subset of these meanings of place and places was reflected in dietary behaviour, as measured by the Canadian Healthy Eating Index, and suggested four place-diet links. The interconnected conceptual and empirical qualities of place and food that emerged from this study are summarized in a conceptual framework. They represent parameters of the PFE that could be tested, defined and validated for use in future research. The findings of this dissertation suggest the need for similar studies with different demographic and geographic groups, and will consequently be of interest to theorists of place and health as well as to planners, food advocates and health professionals to inform programs and policy

Edge Computing for Internet of Things

The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond

Model-based resource management for fine-grained services

The emergence of DevOps has changed the way modern distributed software systems are developed. Architectures decomposed in fine-grained services, such as microservices or function-as-a-service (FaaS), are now widespread across many organizations. From a resource management perspective, although the systems built with such architectures have many benefits, there are still research challenges that need further attention. In this study, we have focused on three such challenges, each concerning a specific system resource: compute, memory, or storage. Firstly, we focus on scaling the capacity of microservices at runtime. Here, the challenge is to design an autoscaler that can decide between vertical and horizontal scaling options to distribute the CPU capacity. Secondly, we focus on estimating the required capacity of an on-premises FaaS platform such that the service level agreements (SLAs) for function response times are satisfied. The challenge here is to address the cold start dilemma, i.e., that a cold start delays a function response but reduces the memory consumption. Thus, we must find a limit of cold starts such that the memory-consumption remains in-check while satisfying the SLAs. Finally, we focus on the storage management for distributed tracing targeted at microservices. The volume of such traces generated in a data center can be in the scale of tens of terabytes per day, but only a small fraction of these traces is useful for troubleshooting. The objective then is to sample only the useful traces. The key to addressing all these challenges is first, modeling the dynamics concerning the resources and subsequently, leveraging the model in a resource controller. To address the first challenge, we have developed an autoscaler ATOM that leverages layered queueing network (LQN) models to take its scaling decisions. Our experiment, with a real-life application, shows that ATOM produces 30-37% better results than the baseline autoscalers. For the second challenge, we have developed COCOA, a cold start aware capacity planner. COCOA utilizes M/M/k setup and LQN models to assess the cold start scenario and estimate the required capacity. We show with simulation that COCOA can reduce over-provisioning by over 70% compared to the availability aware approaches. Finally, addressing the third challenge, we propose SampleHST, a trace sampler that works under a storage budget constraint. SampleHST relies on either bag of words or graph-based models to represent a trace and groups similar traces using online clustering to perform sampling. We have evaluated the performance of SampleHST using data from both literature and production, which shows it produces 1.2x to 19x better results than the state-of-the-art.Open Acces

The 1992 4th NASA SERC Symposium on VLSI Design

Papers from the fourth annual NASA Symposium on VLSI Design, co-sponsored by the IEEE, are presented. Each year this symposium is organized by the NASA Space Engineering Research Center (SERC) at the University of Idaho and is held in conjunction with a quarterly meeting of the NASA Data System Technology Working Group (DSTWG). One task of the DSTWG is to develop new electronic technologies that will meet next generation electronic data system needs. The symposium provides insights into developments in VLSI and digital systems which can be used to increase data systems performance. The NASA SERC is proud to offer, at its fourth symposium on VLSI design, presentations by an outstanding set of individuals from national laboratories, the electronics industry, and universities. These speakers share insights into next generation advances that will serve as a basis for future VLSI design

GPU Accelerated Simulation of Transport Systems

Computer modelling and simulation of road networks are a vital tool used to evaluate, design and manage road network infrastructure. Road network simulations are however computationally expensive, with simulation runtime imposing limits on the scale and quantity of simulations performed within a reasonable time frame. This thesis examines the appropriateness of many-core processing architectures (such as GPUs) for the acceleration of microscopic and macroscopic road network simulation, and the potential impact on the choice of modelling approach. Fine-grained agent-based microscopic simulations of individual vehicles are parallelised using GPUs, achieving high performance through a novel graph-based communication strategy for data-parallel simulations. A minimal benchmark model and scalable road network are defined and used experimentally to evaluate performance compared to Aimsun, a commercial simulation tool for multi-core processors. Performance improvements of up to 67x are demonstrated for large scale simulations. High-level macroscopic simulations model network flow rather than individual vehicles. Although less computationally demanding than microscopic models, simulation runtimes can still be significant, often due to the calculation of many shortest paths. A novel Many-Source Shortest Path (MSSP) algorithm is proposed to concurrently find multiple shortest paths through sparse transport networks using GPUs. This is embedded within a commercial multi-core CPU macroscopic simulation tool, SATURN, and the performance evaluated on large-scale real-world road networks, demonstrating assignment performance improvements of up to 8.6x when comparing multi-processor GPU and CPU implementations. Finally, the impact of the performance improvements to both modelling techniques are evaluated using a common benchmark model and the relative improvements demonstrated by the benchmarking of each approach using different transport networks. These results suggest that GPUs will allow modellers to shift towards using finer-grained simulations for a broader range of modelling tasks

Operating system support for warehouse-scale computing

Modern applications are increasingly backed by large-scale data centres. Systems software in these data centre environments, however, faces substantial challenges: the lack of uniform resource abstractions makes sharing and resource management inefficient, infrastructure software lacks end-to-end access control mechanisms, and work placement ignores the effects of hardware heterogeneity and workload interference. In this dissertation, I argue that uniform, clean-slate operating system (OS) abstractions designed to support distributed systems can make data centres more efficient and secure. I present a novel distributed operating system for data centres, focusing on two OS components: the abstractions for resource naming, management and protection, and the scheduling of work to compute resources. First, I introduce a reference model for a decentralised, distributed data centre OS, based on pervasive distributed objects and inspired by concepts in classic 1980s distributed OSes. Translucent abstractions free users from having to understand implementation details, but enable introspection for performance optimisation. Fine-grained access control is supported by combining storable, communicable identifier capabilities, and context-dependent, ephemeral handle capabilities. Finally, multi-phase I/O requests implement optimistically concurrent access to objects while supporting diverse application-level consistency policies. Second, I present the DIOS operating system, an implementation of my model as an extension to Linux. The DIOS system call API is centred around distributed objects, globally resolvable names, and translucent references that carry context-sensitive object meta-data. I illustrate how these concepts support distributed applications, and evaluate the performance of DIOS in microbenchmarks and a data-intensive MapReduce application. I find that it offers improved, finegrained isolation of resources, while permitting flexible sharing. Third, I present the Firmament cluster scheduler, which generalises prior work on scheduling via minimum-cost flow optimisation. Firmament can flexibly express many scheduling policies using pluggable cost models; it makes high-quality placement decisions based on fine-grained information about tasks and resources; and it scales the flow-based scheduling approach to very large clusters. In two case studies, I show that Firmament supports policies that reduce colocation interference between tasks and that it successfully exploits flexibility in the workload to improve the energy efficiency of a heterogeneous cluster. Moreover, my evaluation shows that Firmament scales the minimum-cost flow optimisation to clusters of tens of thousands of machines while still making sub-second placement decisions.St John's College Supplementary Emolument Fund DARP

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

Automated Reasoning

This volume, LNAI 13385, constitutes the refereed proceedings of the 11th International Joint Conference on Automated Reasoning, IJCAR 2022, held in Haifa, Israel, in August 2022. The 32 full research papers and 9 short papers presented together with two invited talks were carefully reviewed and selected from 85 submissions. The papers focus on the following topics: Satisfiability, SMT Solving,Arithmetic; Calculi and Orderings; Knowledge Representation and Jutsification; Choices, Invariance, Substitutions and Formalization; Modal Logics; Proofs System and Proofs Search; Evolution, Termination and Decision Prolems. This is an open access book

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways