Jonathon Andrew Crowcroft

SCANDEX: Service Centric Networking for Challenged Decentralised Networks

Do-It-Yourself (DIY) networks are decentralised networks built by an (often) amateur community. As DIY networks do not rely on the need for backhaul Internet connectivity, these networks are mostly a mix of both offline and online networks. Although DIY networks have their own homegrown services, the current Internet-based cloud services are often useful, and access to some services could be beneficial to the community. Considering that most DIY networks have challenged Internet connectivity, migrating current service virtualisation instances could face great challenges. Service Centric Networking (SCN) has been recently proposed as a potential solution to managing services more efficiently using Information Centric Networking (ICN) principles. In this position paper, we present our arguments for the need for a resilient SCN architecture, propose a strawman SCN architecture that combines multiple transmission technologies for providing resilient SCN in challenged DIY networks and, finally, identify key challenges that need to be explored further to realise the full potential of our architecture.

FLICK: developing and running application-specific network services

Data centre networks are increasingly programmable, with application-specific network services proliferating, from custom load-balancers to middleboxes providing caching and aggregation. Developers must currently implement these services using traditional low-level APIs, which neither support natural operations on application data nor provide efficient performance isolation. n nWe describe FLICK, a framework for the programming and execution of application-specific network services on multi-core CPUs. Developers write network services in the FLICK language, which offers high-level processing constructs and application-relevant data types. FLICK programs are translated automatically to efficient, parallel task graphs, implemented in C++ on top of a user-space TCP stack. Task graphs have bounded resource usage at runtime, which means that the graphs of multiple services can execute concurrently without interference using cooperative scheduling. We evaluate FLICK with several services (an HTTP load-balancer, a Memcached router and a Hadoop data aggregator), showing that it achieves good performance while reducing development effort.

Wild interdisciplinarity: ethnography and computer science

Abstract Drawing on the experiences of a novel collaborative project between sociologists and computer scientists, this paper identifies a set of challenges for fieldwork that are generated by this wild interdisciplinarity. Public Access Wi-Fi Service was a project funded by an ‘in-the-wild’ research programme, involving the study of digital technologies within a marginalised community, with the goal of addressing digital exclusion. We argue that similar forms of research, in which social scientists are involved in the deployment of experimental technologies within real world settings, are becoming increasingly prevalent. The fieldwork for the project was highly problematic, with the result that few users of the system were successfully enrolled. We analyse why this was the case, identifying three sets of issues which emerge in the juxtaposition of interdisciplinary collaboration and wild setting. We conclude with a set of recommendations for projects involving technologists and social scientists.

Kadupul: Livin' on the Edge with Virtual Currencies and Time-Locked Puzzles

Devices connected to the Internet today have a wide range of local communication channels available, such as wireless Wifi, Bluetooth or NFC, as well as wired backhaul. In densely populated areas it is possible to create heterogeneous, multihop communication paths using a combination of these technologies, and often transmit data with lower latency than via a wired Internet connection. However, the potential for sharing meshed wireless radios in this way has never been realised due to the lack of economic incentives to do so on the part of individual nodes. In this paper, we explore how virtual currencies might be used to provide an end-to-end incentive scheme to convince forwarding nodes that it is profitable to send messages on via the lowest latency mechanism available. Clients inject a small amount of money to transmit a message, and forwarding engines compete to solve a time-locked puzzle that can be claimed by the node that delivers the result in the lowest latency. Our approach naturally extends congestion control techniques to a surge pricing model when available bandwidth is low and does not require latency measurements.

You Are Sensing, but Are You Biased?: A User Unaided Sensor Calibration Approach for Mobile Sensing

Mobile devices are becoming pervasive to our daily lives: they follow us everywhere and we use them for much more than just communication. These devices are also equipped with a myriad of different sensors that have the potential to allow the tracking of human activities, user patterns, location, direction and much more. Following this direction, many movements including sports, quantified self, and mobile health ones are starting to heavily rely on this technology, making it pivotal that the sensors offer high accuracy. However, heterogeneity in hardware manufacturing, slight substrate differences, electronic interference as well as external disturbances are just few of the reasons that limit sensor output accuracy which in turn hinders sensor usage in applications which need very high granularity and precision, such as quantified-self applications. Although, calibration of sensors is a widely studied topic in literature to the best of our knowledge no publicly available research exists that specifically tackles the calibration of mobile phones and existing methods that can be adapted for use in mobile devices not only require user interaction but they are also not adaptive to changes. Additionally, alternative approaches for performing more granular and accurate sensing exploit body-wide sensor networks using mobile phones and additional sensors; as one can imagine these techniques can be bulky, tedious, and not particularly user friendly. Moreover, existing techniques for performing data corrections post-acquisition can produce inconsistent results as they miss important context information provided from the device itself; which when used, has been shown to produce better results without a imposing a significant power-penalty. In this paper we introduce a novel multiposition calibration scheme that is specifically targeted at mobile devices Our scheme exploits machine learning techniques to perform an adaptive, power-efficient auto-calibration procedure with which achieves high output sensor accuracy when compared to state of the art techniques without requiring any user interaction or special equipment beyond device itself Moreover, the energy costs associated with our approach are lower than the alternatives (such as Kalman filter based solutions) and the overall power penalty is < 5% when compared against power usage that is exhibited when using uncalibrated traces, thus, enabling our technique to be used efficiently on a wide variety of devices Finally, our evaluation illustrates that calibrated signals offer a tangible benefit in classification accuracy, ranging from 3 to 10%, over uncalibrated ones when using state of the art classifiers, on the other hand when using simpler SVM classifiers the classification improvement is boosted ranging from 8% to 12% making lower performing classifiers much more reliable Additionally, we show that for similar activities which are hard to distinguish otherwise, we reach an accuracy of > 95% when using neural network classifiers and > 88% when using SVM classifiers where uncalibrated data classification only reaches ~ 85% and ~ 80% respectively This can be a make or break factor in the use of accelerometer and gyroscope data in applications requiring high accuracy e g sports, health, games and others

Extending programs with debug-related features, with application to hardware development

This work has received funding from the EPSRC NaaS grant EP/K034723/1, European Unions Horizon 2020 research and innovation programme 2014-2018 under the SSICLOPS (grant agreement No. 644866), the Leverhulme Trust Early Career Fellowship ECF-2016-289 and the Newton Trust.

FairCache: Introducing Fairness to ICN Caching - Technical Report

Caching is a core principle of information-centric networking (ICN). Many novel algorithms have been proposed for enabling ICN caching, many of which rely on collaborative principles, i.e. multiple caches interacting to decide what to store. Past work has assumed entirely altruistic nodes that will sacrifice their own performance for the global optimum. In this paper, we argue that this assumption is flawed. We address this problem by modelling the in-network caching problem as a Nash bargaining game. We develop optimal and heuristic caching solutions that explicitly consider both performance and fairness. We argue that only algorithms that are fair to all parties will encourage engagement and cooperation. Through extensive simulations, we show our heuristic solution, FairCache, ensures that all collaborative caches achieve performance gains without undermining the performance of others.

From photons to big-data applications: terminating terabits

Computer architectures have entered a watershed as the quantity of network data generated by user applications exceeds the data-processing capacity of any individual computer end-system. It will become impossible to scale existing computer systems while a gap grows between the quantity of networked data and the capacity for per system data processing. Despite this, the growth in demand in both task variety and task complexity continues unabated. Networked computer systems provide a fertile environment in which new applications develop. As networked computer systems become akin to infrastructure, any limitation upon the growth in capacity and capabilities becomes an important constraint of concern to all computer users. Considering a networked computer system capable of processing terabits per second, as a benchmark for scalability, we critique the state of the art in commodity computing, and propose a wholesale reconsideration in the design of computer architectures and their attendant ecosystem. Our proposal seeks to reduce costs, save power and increase performance in a multi-scale approach that has potential application from nanoscale to data-centre-scale computers.

Data Analytics Service Composition and Deployment on IoT Devices

Thiswork is funded in part by the EPSRC Databox project (EP/N028260/2),nNaaS (EP/K031724/2) and Contrive (EP/N028422/1).

You Are Sensing, but Are You Biased?

Thisworkwas supported by The Alan Turing Institute under grants: TU/C/000003, TU/B/000069, and EP/N510129/1.