Jon Crowcroft

Bio: Jon Crowcroft is an academic researcher from University of Cambridge. The author has contributed to research in topics: The Internet & Multicast. The author has an hindex of 87, co-authored 672 publications receiving 38848 citations. Previous affiliations of Jon Crowcroft include Memorial University of Newfoundland & Information Technology University.

Lower than best effort: a design and implementation

Abstract: In recent years, the Internet architecture has been augmented so that Better-than-Best-Effort (BBE) services, in the form of reserved resources for specific flows, can be provided by the network. To date, this has been realized through two different and sequentially developed efforts. The first is known as Integrated Services and focuses on specific bounds on bandwidth and/or delay for specific flows. The Differentiated Service model was later introduced, which presented a more aggregated and local perspective regarding the forwarding of traffic. A direction that is missing in today's work on service models is a defined schema used to purposely degrade certain traffic to various levels below that of Best Effort. In a sense, a new direction that provides a balancing effect in the deployment of BBE service. This is particularly evident with continual and parallel short transaction flows (like that used for web applications) over low bandwidth links that are not subject to any backoff penalty incurred by congestion because state does not persist. In a more indirect perspective, our model correlates degraded service with the application of usage and security policies -- administrative decisions that can operate in tandem or disjointly from conditions of the network. This paper attempts to address these and other issues and presents the design and implementation of such a new degraded service model and queuing mechanism used to support it.

SpinThrift: saving energy in viral workloads

Abstract: This paper looks at optimising the energy costs for storing user-generated content when accesses are highly skewed towards a few "popular" items, but the popularity ranks vary dynamically. Using traces from a video-sharing website and a social news website, it is shown that the non-popular content, which constitute the majority by numbers, tend to have accesses which spread locally in the social network, in a viral fashion. Based on the proportion of viral accesses, popular data is separated onto a few disks on storage. The popular disks receive the majority of accesses, allowing other disks to be spun down when there are no requests, saving energy.Our technique, SpinThrift, improves upon Popular Data Concentration (PDC), which, in contrast with our binary separation between popular and unpopular items, directs the majority of accesses to a few disks by arranging data according to popularity rank. Disregarding the energy required for data reorganisation, SpinThrift and PDC display similar energy savings. However, because of the dyamically changing popularity ranks, SpinThrift requires less than half the number of data reorderings compared to PDC.

A Parallel Implicit Method for the Steady-State Solution of CTMCs

Abstract: This paper considers the steady-state solution of Continuous Time Markov Chains (CTMCs). CTMCs are a widely used formalism for the performance analysis of computer and communication systems. A large variety of useful performance measures can be derived from a CTMC via the computation of its steady-state probabilities. However, CTMC models for realistic systems are very large. We address this largeness problem in this paper by considering parallelisation of implicit methods. In particular, we consider a modified form of Multi- Terminal Binary Decision Diagrams (MTBDDs) to compactly store CTMCs, and, using Jacobi iterative method, we present a parallel method for the CTMC steady-state solution. Employing a 24-node processor bank, we analyse our parallel implicit method using the experimental results for three widely used CTMC benchmark models, with well over a billion states and sixteen billion transitions.

The Benefits of Deploying Smart Contracts on Trusted Third Parties.

Abstract: The hype about Bitcoin has overrated the potential of smart contracts deployed on-blockchains (on-chains) and underrated the potential of smart contracts deployed on-Trusted Third Parties (on-TTPs). As a result, current research and development in this field is focused mainly on smart contract applications that use on-chain smart contracts. We argue that there is a large class of smart contract applications where on-TTP smart contracts are a better alternative. The problem with on-chain smart contracts is that the fully decentralised model and indelible append-only data model followed by blockchains introduces several engineering problems that are hard to solve. In these situations, the inclusion of a TTP (assuming that the application can tolerate its inconveniences) instead of a blockchain to host the smart contract simplifies the problems and offers pragmatic solutions. The intention and contribution of this paper is to shed some light on this issue. We use a hypothetical use case of a car insurance application to illustrate technical problems that are easier to solve with on-TTP smart contracts than with on-chain smart contracts.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Community detection in graphs

Abstract: The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.