The core problem in many sensing applications is that we’re trying to
infer high-resolution information from low-resolution observations —
and keep our trust in this information as the sensors degrade. How can
we do this in a principled way? There’s an emerging body of work on
using topology to manage both sensing and analytics, and in this talk I
try to get a handle on how this might work for some of the problems
we’re interested in. I will present an experiment we did to explore
these ideas, which highlights some fascinating problems.
This is the website for the Systems Research Group (SRG) at the University of St Andrews. Systems is the largest research area in the School of Computer Science, covering the broad areas of distributed systems, networked systems, sensor systems and data-intensive systems.
The Systems Research Group (SRG) consists of twelve interdisciplinary faculty members, and a large cohort of Research Fellows and PhD students. Our interdisciplinary nature means that we can work on projects which cross multiple areas of systems research, many in collaboration with industry partners. Members of the group have expertise in research areas spanning: data centres, cloud computing, many-core systems, networking, middleware, sensor networking, machine learning, Internet of Things (IoT), autonomic computing and software architectures.
We take a very practical approach to research, by building and evaluating real systems, whilst publishing in many of the top-tiered systems research conferences and journals. SRG research is currently funded through the following organisations:
SRG runs a bi-weekly seminar series every other Thursday at 1pm in JC 1.33B during semester time. There are talks from faculty, research fellows, PhD students and visitors. Please check our exciting schedule.
News and Events
The latest Systems Research Group posts from the School of Computer Science blog.
SRG Seminar: “Interactional Justice vs. The Paradox of Self-Amendment and the Iron Law of Oligarchy” by Jeremy Pitt
Self-organisation and self-governance offer an effective approach to resolving collective action problems in multi-agent systems, such as fair and sustainable resource allocation. Nevertheless, self-governing systems which allow unrestricted and unsupervised self-modification expose themselves to several risks, including the Suber’s paradox of self-amendment (rules specify their own amendment) and Michel’s iron law of oligarchy (that the system will inevitably be taken over by a small clique and be run for its own benefit, rather than in the collective interest). This talk will present an algorithmic approach to resisting both the paradox and the iron law, based on the idea of interactional justice derived from sociology, and legal and organizational theory. The process of interactional justice operationalised in this talk uses opinion formation over a social network with respect to a shared set of congruent values, to transform a set of individual, subjective self-assessments into a collective, relative, aggregated assessment.
Using multi-agent simulation, we present some experimental results about detecting and resisting cliques. We conclude with a discussion of some implications concerning institutional reformation and stability, ownership of the means of coordination, and knowledge management processes in ‘democratic’ systems.
Jeremy Pitt is Professor of Intelligent and Self-Organising Systems in the Department of Electrical & Electronic Engineering at Imperial College London, where he is also Deputy Head of the Intelligent Systems & Networks Group. His research interests focus on developing formal models of social processes using computational logic, and their application in self-organising multi-agent systems, for example fair and sustainable common-pool resource management in ad hoc and sensor network. He also has strong interests in human-computer interaction, socio-technical systems, and the social impact of technology; with regard to the latter he has edited two books, This Pervasive Day (IC Press, 2012) and The Computer After Me (IC Press, 2014). He has been an investigator on more than 30 national and European research projects and has published more than 150 articles in journals and conferences. He is a Senior Member of the ACM, a Fellow of the BCS, and a Fellow of the IET; he is also an Associate Editor of ACM Transactions on Autonomous and Adaptive Systems and an Associate Editor of IEEE Technology and Society Magazine.
“Ambient intelligence with sensor networks” by Lucas Amos and “Location, Location, Location: Exploring Amazon EC2 Spot Instance Pricing Across Geographical Regions” by Nnamdi Ekwe-Ekwe
“Indoor environment quality has a significant effect on worker productivity through a complex interplay of factors such as temperature, humidity and levels of Volatile Organic Compounds (VOCs).
In this talk I will discuss my Masters project which used off the shelf sensors and Raspberry Pis to collect environmental readings at one minute intervals throughout the Computer Science buildings. The prevalence of erroneous readings due to sensor failure and the strategy used for the identification and correction of such faults will be presented. Identifiable correlations between environmental variables and attempts to model these relationships will be discussed
Past studies identifying the ideal environmental conditions for human comfort and productivity allow for the objective assessment of indoor environmental conditions. An adaptation of Frešer’s environment rating system will be presented, showing how VOC levels can be incorporated into assessments of environment quality and how this can be communicated to building users.”
“Cloud computing is becoming an almost ubiquitous part of the computing landscape. For many companies today, moving their entire infrastructure and workloads to the cloud reduces complexity, time to deployment, and saves money. Spot Instances, a subset of Amazon’s cloud computing infrastructure (EC2), expands on this. They allow a user to bid on spare compute capacity in Amazon’s data centres at heavily discounted prices. If demand was ever to increase such that the user’s maximum bid is exceeded, their compute instance is terminated.
In this work, we conduct one of the first detailed analyses of how location affects the overall cost of deployment of a spot instance. We simultaneously examine the reliability of pricing data of a spot instance, and whether a user can be confident that their instance has a low risk of termination.
We analyse spot pricing data across all available Amazon Web Services regions for 60 days on a variety of instance types. We find that location does play a critical role in spot instance pricing and also that pricing differs depending on the granularity of the location – from a more coarse-grained AWS region to a more fine-grained Availability Zone within a region. We relate the pricing differences we find to the price’s stability, confirming whether we can be confident in the bid prices we make.
We conclude by showing that it is very possible to run workloads on Spot Instances achieving
both a very low risk of termination as well as paying very low amounts per hour.”