arXiv subjects

Richard Mortier

Publications and source records attributed to Richard Mortier.

At least 37 records · Page 2Linked to original sources

arXiv2021

Revisiting IoT Device Identification

Internet-of-Things (IoT) devices are known to be the source of many security problems, and as such, they would greatly benefit from automated management. This requires robustly identifying devices so that appropriate network security policies can be applied. We address this challenge by exploring how to accurately identify IoT devices based on their network behavior, while leveraging approaches previously proposed by other researchers. We compare the accuracy of four different previously proposed machine learning models (tree-based and neural network-based) for identifying IoT devices. We use packet trace data collected over a period of six months from a large IoT test-bed. We show that, while all models achieve high accuracy when evaluated on the same dataset as they were trained on, their accuracy degrades over time, when evaluated on data collected outside the training set. We show that on average the models' accuracy degrades after a couple of weeks by up to 40 percentage points (on average between 12 and 21 percentage points). We argue that, in order to keep the models' accuracy at a high level, these need to be continuously updated.

cs.CR↗

arXiv2021

Rearchitecting Kubernetes for the Edge

Recent years have seen Kubernetes emerge as a primary choice for container orchestration. Kubernetes largely targets the cloud environment but new use cases require performant, available and scalable orchestration at the edge. Kubernetes stores all cluster state in etcd, a strongly consistent key-value store. We find that at larger etcd cluster sizes, offering higher availability, write request latency significantly increases and throughput decreases similarly. Coupled with approximately 30% of Kubernetes requests being writes, this directly impacts the request latency and availability of Kubernetes, reducing its suitability for the edge. We revisit the requirement of strong consistency and propose an eventually consistent approach instead. This enables higher performance, availability and scalability whilst still supporting the broad needs of Kubernetes. This aims to make Kubernetes much more suitable for performance-critical, dynamically-scaled edge solutions.

cs.DC↗

arXiv2021

SenseRT: A Streaming Architecture for Smart Building Sensors

Building Management Systems (BMSs) have evolved in recent years, in ways that require changes to existing network architectures that follow the store-then-analyse approach. The primary cause is the increasing deployment of a diverse range of cost-effective sensors and actuators in smart buildings that generate real-time streaming data. Any in-building system with a large number of sensors needs a framework for real-time data collection and concurrent stream processing from sensors connected using a range of networks. We present SenseRT, a system for managing and analysing in-building real-time streams of sensor data. SenseRT collects streams of real-time data from sensors connected using a range of network protocols. It supports concurrent modules simultaneously performing stream processing over real-time data, asynchronously and non-blocking, with results made available with minimal latency. We describe a prototype implementation deployed in two University department buildings, demonstrating its effectiveness.

cs.NI↗

arXiv2021

Data Management for Building Information Modelling in a Real-Time Adaptive City Platform

Legacy Building Information Modelling (BIM) systems are not designed to process the high-volume, high-velocity data emitted by in-building Internet-of-Things (IoT) sensors. Historical lack of consideration for the real-time nature of such data means that outputs from such BIM systems typically lack the timeliness necessary for enacting decisions as a result of patterns emerging in the sensor data. Similarly, as sensors are increasingly deployed in buildings, antiquated Building Management Systems (BMSs) struggle to maintain functionality as interoperability challenges increase. In combination these motivate the need to fill an important gap in smart buildings research, to enable faster adoption of these technologies, by combining BIM, BMS and sensor data. This paper describes the data architecture of the Adaptive City Platform, designed to address these combined requirements by enabling integrated BIM and real-time sensor data analysis across both time and space.

cs.HC↗

arXiv2020

The Case for Retraining of ML Models for IoT Device Identification at the Edge

Internet-of-Things (IoT) devices are known to be the source of many security problems, and as such they would greatly benefit from automated management. This requires robustly identifying devices so that appropriate network security policies can be applied. We address this challenge by exploring how to accurately identify IoT devices based on their network behavior, using resources available at the edge of the network. In this paper, we compare the accuracy of five different machine learning models (tree-based and neural network-based) for identifying IoT devices by using packet trace data from a large IoT test-bed, showing that all models need to be updated over time to avoid significant degradation in accuracy. In order to effectively update the models, we find that it is necessary to use data gathered from the deployment environment, e.g., the household. We therefore evaluate our approach using hardware resources and data sources representative of those that would be available at the edge of the network, such as in an IoT deployment. We show that updating neural network-based models at the edge is feasible, as they require low computational and memory resources and their structure is amenable to being updated. Our results show that it is possible to achieve device identification and categorization with over 80% and 90% accuracy respectively at the edge.

cs.NI↗

arXiv2020

Fast Flexible Paxos: Relaxing Quorum Intersection for Fast Paxos

Paxos, the de facto standard approach to solving distributed consensus, operates in two phases, each of which requires an intersecting quorum of nodes. Multi-Paxos reduces this to one phase by electing a leader but this leader is also a performance bottleneck. Fast Paxos bypasses the leader but has stronger quorum intersection requirements. In this paper we observe that Fast Paxos' intersection requirements can be safely relaxed, reducing to just one additional intersection requirement between phase-1 quorums and any pair of fast round phase-2 quorums. We thus find that the quorums used with Fast Paxos are larger than necessary, allowing alternative quorum systems to obtain new tradeoffs between performance and fault-tolerance.

cs.DC↗

arXiv2020

Paxos vs Raft: Have we reached consensus on distributed consensus?

Distributed consensus is a fundamental primitive for constructing fault-tolerant, strongly-consistent distributed systems. Though many distributed consensus algorithms have been proposed, just two dominate production systems: Paxos, the traditional, famously subtle, algorithm; and Raft, a more recent algorithm positioned as a more understandable alternative to Paxos. In this paper, we consider the question of which algorithm, Paxos or Raft, is the better solution to distributed consensus? We analyse both to determine exactly how they differ by describing a simplified Paxos algorithm using Raft's terminology and pragmatic abstractions. We find that both Paxos and Raft take a very similar approach to distributed consensus, differing only in their approach to leader election. Most notably, Raft only allows servers with up-to-date logs to become leaders, whereas Paxos allows any server to be leader provided it then updates its log to ensure it is up-to-date. Raft's approach is surprisingly efficient given its simplicity as, unlike Paxos, it does not require log entries to be exchanged during leader election. We surmise that much of the understandability of Raft comes from the paper's clear presentation rather than being fundamental to the underlying algorithm being presented.

cs.DC↗

arXiv2020

SoK: Beyond IoT MUD Deployments -- Challenges and Future Directions

Due to the advancement of IoT devices in both domestic and industrial environments, the need to incorporate a mechanism to build accountability in the IoT ecosystem is paramount. In the last few years, various initiatives have been started in this direction addressing many socio-technical concerns and challenges to build an accountable system. The solution that has received a lot of attention in both industry and academia is the Manufacturer Usage Description (MUD) specification. It gives the possibility to the IoT device manufacturers to describe communications needed by each device to work properly. MUD implementation is challenging not only due to the diversity of IoT devices and manufacturer/operator/regulators but also due to the incremental integration of MUD-based flow control in the already existing Internet infrastructure. To provide a better understanding of these challenges, in this work, we explore and investigate the prototypes of three implementations proposed by different research teams and organisations, useful for the community to understand which are the various features implemented by the existing technologies. By considering that there exist some behaviours which can be only defined by local policy, we propose a MUD capable network integrating our User Policy Server(UPS). The UPS provides network administrators and endusers an opportunity to interact with MUD components through a user-friendly interface. Hence, we present a comprehensive survey of the challenges.

cs.NI↗

arXiv2019

Programming Unikernels in the Large via Functor Driven Development

Compiling applications as unikernels allows them to be tailored to diverse execution environments. Dependency on a monolithic operating system is replaced with linkage against libraries that provide specific services. Doing so in practice has revealed a major barrier: managing the configuration matrix across heterogenous execution targets. A realistic unikernel application depends on hundreds of libraries, each of which may place different demands on the different target execution platforms (e.g.,~cryptographic acceleration). We propose a modular approach to structuring large scale codebases that cleanly separates configuration, application and operating system logic. Our implementation is built on the \mirage unikernel framework, using the \ocaml language's powerful abstraction and metaprogramming facilities. Leveraging modules allows us to build many components independently, with only loose coupling through a set of standardised signatures. Components can be parameterized by other components and composed. Our approach accounts for state, dependency ordering, and error management, and our usage over the years has demonstrated significant efficiency benefits by leveraging compiler features such as global link-time optimisation during the configuration process. We describe our application architecture and experiences via some practical applications of our approach, and discuss how library development in \mirage can facilitate adoption in other unikernel frameworks and programming languages.

cs.PL↗

arXiv2019

Fractal: Automated Application Scaling

To date, cloud applications have used datacenter resources through manual configuration and deployment of virtual machines and containers. Current trends see increasing use of microservices, where larger applications are split into many small containers, to be developed and deployed independently. However, even with the rise of the devops movement and orchestration facilities such as Kubernetes, there is a tendency to separate development from deployment. We present an exploration of a more extreme point on the devops spectrum: Fractal. Developers embed orchestration logic inside their application, fully automating the processes of scaling up and down. Providing a set of extensions to and an API over the Jitsu platform, we outline the design of Fractal and describe the key features of its implementation: how an application is self-replicated, how replica lifecycles are managed, how failure recovery is handled, and how network traffic is transparently distributed between replicas. We present evaluation of a self-scaling website, and demonstrate that Fractal is both useful and feasible.

cs.DC↗

arXiv2019

Network Service Dependencies in Commodity Internet-of-Things Devices

We continue to see increasingly widespread deployment of IoT devices, with apparent intent to embed them in our built environment likely to accelerate if smart city and related programmes succeed. In this paper we are concerned with the ways in which current generation IoT devices are being designed in terms of their ill-considered dependencies on network connectivity and services. Our hope is to provide evidence that such dependencies need to be better thought through in design, and better documented in implementation so that those responsible for deploying these devices can be properly informed as to the impact of device deployment (at scale) on infrastructure resilience. We believe this will be particularly relevant as we feel that commodity IoT devices are likely to be commonly used to retrofit `smart' capabilities to existing buildings, particularly domestic buildings. To the existing body of work on network-level behaviour of IoT devices, we add \one~a protocol-level breakdown and analysis of periodicity, \two~an exploration of the service and infrastructure dependencies that will implicitly be taken in `smart' environments when IoT devices are deployed, and \three~examination of the robustness of device operation when connectivity is disrupted. We find that many devices make use of services distributed across the planet and thus appear dependent on the global network infrastructure even when carrying out purely local actions. Some devices cease to operate properly without network connectivity (even where their behaviour appears, on the face of it, to require only local information, e.g.,~the Hive thermostat). Further, they exhibit quite different network behaviours, typically involving significantly more traffic and possibly use of otherwise unobserved protocols, when connectivity is recovered after some disruption.

cs.NI↗

arXiv2019

Zest: REST over ZeroMQ

In this paper, we introduce Zest (REST over ZeroMQ), a middleware technology in support of an Internet of Things (IoT). Our work is influenced by the Constrained Application Protocol (CoAP) but emphasises systems that can support fine-grained access control to both resources and audit information, and can provide features such as asynchronous communication patterns between nodes. We achieve this by using a hybrid approach that combines a RESTful architecture with a variant of a publisher/subscriber topology that has enhanced routing support. The primary motivation for Zest is to provide inter-component communications in the Databox, but it is applicable in other contexts where tight control needs to be maintained over permitted communication patterns.

cs.DC↗

arXiv2019

A Generalised Solution to Distributed Consensus

Distributed consensus, the ability to reach agreement in the face of failures and asynchrony, is a fundamental primitive for constructing reliable distributed systems from unreliable components. The Paxos algorithm is synonymous with distributed consensus, yet it performs poorly in practice and is famously difficult to understand. In this paper, we re-examine the foundations of distributed consensus. We derive an abstract solution to consensus, which utilises immutable state for intuitive reasoning about safety. We prove that our abstract solution generalises over Paxos as well as the Fast Paxos and Flexible Paxos algorithms. The surprising result of this analysis is a substantial weakening to the quorum requirements of these widely studied algorithms.

cs.DC↗

arXiv2018

Data Analytics Service Composition and Deployment on Edge Devices

Data analytics on edge devices has gained rapid growth in research, industry, and different aspects of our daily life. This topic still faces many challenges such as limited computation resource on edge devices. In this paper, we further identify two main challenges: the composition and deployment of data analytics services on edge devices. We present the Zoo system to address these two challenge: on one hand, it provides simple and concise domain-specific language to enable easy and and type-safe composition of different data analytics services; on the other, it utilises multiple deployment backends, including Docker container, JavaScript, and MirageOS, to accommodate the heterogeneous edge deployment environment. We show the expressiveness of Zoo with a use case, and thoroughly compare the performance of different deployment backends in evaluation.

cs.SE↗

arXiv2018

Privacy-Preserving Personal Model Training

Many current Internet services rely on inferences from models trained on user data. Commonly, both the training and inference tasks are carried out using cloud resources fed by personal data collected at scale from users. Holding and using such large collections of personal data in the cloud creates privacy risks to the data subjects, but is currently required for users to benefit from such services. We explore how to provide for model training and inference in a system where computation is pushed to the data in preference to moving data to the cloud, obviating many current privacy risks. Specifically, we take an initial model learnt from a small set of users and retrain it locally using data from a single user. We evaluate on two tasks: one supervised learning task, using a neural network to recognise users' current activity from accelerometer traces; and one unsupervised learning task, identifying topics in a large set of documents. In both cases the accuracy is improved. We also analyse the robustness of our approach against adversarial attacks, as well as its feasibility by presenting a performance evaluation on a representative resource-constrained device (a Raspberry Pi).

cs.LG↗

arXiv2018

An Analysis of Home IoT Network Traffic and Behaviour

Internet-connected devices are increasingly present in our homes, and privacy breaches, data thefts, and security threats are becoming commonplace. In order to avoid these, we must first understand the behaviour of these devices. In this work, we analyse network traces from a testbed of common IoT devices, and describe general methods for fingerprinting their behavior. We then use the information and insights derived from this data to assess where privacy and security risks manifest themselves, as well as how device behavior affects bandwidth. We demonstrate simple measures that circumvent attempts at securing devices and protecting privacy.

cs.NI↗

arXiv2017

User-centric Composable Services: A New Generation of Personal Data Analytics

Machine Learning (ML) techniques, such as Neural Network, are widely used in today's applications. However, there is still a big gap between the current ML systems and users' requirements. ML systems focus on improving the performance of models in training, while individual users cares more about response time and expressiveness of the tool. Many existing research and product begin to move computation towards edge devices. Based on the numerical computing system Owl, we propose to build the Zoo system to support construction, compose, and deployment of ML models on edge and local devices.

cs.LG↗

arXiv2017

Probabilistic Synchronous Parallel

Most machine learning and deep neural network algorithms rely on certain iterative algorithms to optimise their utility/cost functions, e.g. Stochastic Gradient Descent. In distributed learning, the networked nodes have to work collaboratively to update the model parameters, and the way how they proceed is referred to as synchronous parallel design (or barrier control). Synchronous parallel protocol is the building block of any distributed learning framework, and its design has direct impact on the performance and scalability of the system. In this paper, we propose a new barrier control technique - Probabilistic Synchronous Parallel (PSP). Com- paring to the previous Bulk Synchronous Parallel (BSP), Stale Synchronous Parallel (SSP), and (Asynchronous Parallel) ASP, the proposed solution e ectively improves both the convergence speed and the scalability of the SGD algorithm by introducing a sampling primitive into the system. Moreover, we also show that the sampling primitive can be applied atop of the existing barrier control mechanisms to derive fully distributed PSP-based synchronous parallel. We not only provide a thorough theoretical analysis1 on the convergence of PSP-based SGD algorithm, but also implement a full-featured distributed learning framework called Actor and perform intensive evaluation atop of it.

cs.DC↗