Analysing BitTorrent's seeding strategies

BitTorrent is a typical peer-to-peer (P2P) file distribution application that has gained tremendous popularity in recent years. A considerable amount of research exists regarding BitTorrent’s choking algorithm, which has proved to be effective in preventing freeriders. However, the effect of the seeding strategy on the resistance to freeriders in BitTorrent has been largely overlooked. In addition to this, a category of selfish leechers (termed exploiters), who leave the overlay immediately after completion, has never been taken into account in the previous research. In this paper two popular seeding strategies, the Original Seeding Strategy (OSS) and the Time- based Seeding Strategy (TSS), are chosen and we study via mathematical models and simulation their effects on freeriders and exploiters in BitTorrent networks. The mathematical model is verified and we discover that both freeriders and exploiters impact on system performance, despite the seeding strategy that is employed. However, a selfish-leechers threshold is identified; once the threshold is exceeded, we find that TSS outperforms OSS – that is, TSS reduces the negative impact of selfish lechers more effectively than OSS. Based on these results we discuss the choice of seeding strategy and speculate as to how more effective BitTorrent-based file distribu- tion applications can be built

Developing performance-portable molecular dynamics kernels in Open CL

This paper investigates the development of a molecular dynamics code that is highly portable between architectures. Using OpenCL, we develop an implementation of Sandia’s miniMD benchmark that achieves good levels of performance across a wide range of hardware: CPUs, discrete GPUs and integrated GPUs. We demonstrate that the performance bottlenecks of miniMD’s short-range force calculation kernel are the same across these architectures, and detail a number of platform- agnostic optimisations that improve its performance by at least 2x on all hardware considered. Our complete code is shown to be 1.7x faster than the original miniMD, and at most 2x slower than implementations individually hand-tuned for a specific architecture

Connectivity-guaranteed and obstacle-adaptive deployment schemes for mobile sensor networks

Mobile sensors can relocate and self-deploy into a network. While focusing on the problems of coverage, existing deployment schemes largely over-simplify the conditions for network connectivity: they either assume that the communication range is large enough for sensors in geometric neighborhoods to obtain location information through local communication, or they assume a dense network that remains connected. In addition, an obstacle-free field or full knowledge of the field layout is often assumed. We present new schemes that are not governed by these assumptions, and thus adapt to a wider range of application scenarios. The schemes are designed to maximize sensing coverage and also guarantee connectivity for a network with arbitrary sensor communication/sensing ranges or node densities, at the cost of a small moving distance. The schemes do not need any knowledge of the field layout, which can be irregular and have obstacles/holes of arbitrary shape. Our first scheme is an enhanced form of the traditional virtual-force-based method, which we term the Connectivity-Preserved Virtual Force (CPVF) scheme. We show that the localized communication, which is the very reason for its simplicity, results in poor coverage in certain cases. We then describe a Floor-based scheme which overcomes the difficulties of CPVF and, as a result, significantly outperforms it and other state-of-the-art approaches. Throughout the paper our conclusions are corroborated by the results from extensive simulations

Goal-based composition of scalable hybrid analytics for heterogeneous architectures

Crafting scalable analytics in order to extract actionable business intelligence is a challenging endeavour, requiring multiple layers of expertise and experience. Often, this expertise is irreconcilably split between an organisation’s engineers and subject matter domain experts. Previous approaches to this problem have relied on technically adept users with tool-specific training. Such an approach has a number of challenges: Expertise — There are few data-analytic subject domain experts with in-depth technical knowledge of compute architectures; Performance — Analysts do not generally make full use of the performance and scalability capabilities of the underlying architectures; Heterogeneity — calculating the most performant and scalable mix of real-time (on-line) and batch (off-line) analytics in a problem domain is difficult; Tools — Supporting frameworks will often direct several tasks, including, composition, planning, code generation, validation, performance tuning and analysis, but do not typically provide end-to-end solutions embedding all of these activities. In this paper, we present a novel semi-automated approach to the composition, planning, code generation and performance tuning of scalable hybrid analytics, using a semantically rich type system which requires little programming expertise from the user. This approach is the first of its kind to permit domain experts with little or no technical expertise to assemble complex and scalable analytics, for hybrid on- and off-line analytic environments, with no additional requirement for low-level engineering support. This paper describes (i) an abstract model of analytic assembly and execution, (ii) goal-based planning and (iii) code generation for hybrid on- and off-line analytics. An implementation, through a system which we call Mendeleev, is used to (iv) demonstrate the applicability of this technique through a series of case studies, where a single interface is used to create analytics that can be run simultaneously over on- and off-line environments. Finally, we (v) analyse the performance of the planner, and (vi) show that the performance of Mendeleev’s generated code is comparable with that of hand-written analytics

Inter-species horizontal transfer resulting in core-genome and niche-adaptive variation within Helicobacter pylori

Background Horizontal gene transfer is central to evolution in most bacterial species. The detection of exchanged regions is often based upon analysis of compositional characteristics and their comparison to the organism as a whole. In this study we describe a new methodology combining aspects of established signature analysis with textual analysis approaches. This approach has been used to analyze the two available genome sequences of H. pylori. Results This gene-by-gene analysis reveals a wide range of genes related to both virulence behaviour and the strain differences that have been relatively recently acquired from other sequence backgrounds. These frequently involve single genes or small numbers of genes that are not associated with transposases or bacteriophage genes, nor with inverted repeats typically used as markers for horizontal transfer. In addition, clear examples of horizontal exchange in genes associated with 'core' metabolic functions were identified, supported by differences between the sequenced strains, including: ftsK, xerD and polA. In some cases it was possible to determine which strain represented the 'parent' and 'altered' states for insertion-deletion events. Different signature component lengths showed different sensitivities for the detection of some horizontally transferred genes, which may reflect different amelioration rates of sequence components. Conclusion New implementations of signature analysis that can be applied on a gene-by-gene basis for the identification of horizontally acquired sequences are described. These findings highlight the central role of the availability of homologous substrates in evolution mediated by horizontal exchange, and suggest that some components of the supposedly stable 'core genome' may actually be favoured targets for integration of foreign sequences because of their degree of conservation

Population mapping in informal settlements with high-resolution satellite imagery and equitable ground-truth

We propose a generalizable framework for the population estimation of dense, informal settlements in low-income urban areas–so called ’slums’–using high-resolution satellite imagery. Precise population estimates are a crucial factor for efficient resource allocations by government authorities and NGO’s, for instance in medical emergencies. We utilize equitable ground-truth data, which is gathered in collaboration with local communities: Through training and community mapping, the local population contributes their unique domain knowledge, while also maintaining agency over their data. This practice allows us to avoid carrying forward potential biases into the modeling pipeline, which might arise from a less rigorous ground-truthing approach. We contextualize our approach in respect to the ongoing discussion within the machine learning community, aiming to make real-world machine learning applications more inclusive, fair and accountable. Because of the resource intensive ground-truth generation process, our training data is limited. We propose a gridded population estimation model, enabling flexible and customizable spatial resolutions. We test our pipeline on three experimental site in Nigeria, utilizing pre-trained and fine-tune vision networks to overcome data sparsity. Our findings highlight the difficulties of transferring common benchmark models to real-world tasks. We discuss this and propose steps forward

Goal-based analytic composition for on- and off-line execution at scale

Crafting scalable analytics in order to extract actionable business intelligence is a challenging endeavour, requiring multiple layers of expertise and experience. Often, this expertise is irreconcilably split between an organisation’s engineers and subject matter or domain experts. Previous approaches to this problem have relied on technically adept users with tool-specific training. These approaches have generally not targeted the levels of performance and scalability required to harness the sheer volume and velocity of large-scale data analytics. In this paper, we present a novel approach to the automated planning of scalable analytics using a semantically rich type system, the use of which requires little programming expertise from the user. This approach is the first of its kind to permit domain experts with little or no technical expertise to assemble complex and scalable analytics, for execution both on- and offline, with no lower-level engineering support. We describe in detail (i) an abstract model of analytic assembly and execution; (ii) goal-based planning and (iii) code generation using this model for both on- and off-line analytics. Our implementation of this model, MENDELEEV, is used to (iv) demonstrate the applicability of our approach through a series of case studies, in which a single interface is used to create analytics that can be run in real-time (on-line) and batch (off-line) environments. We (v) analyse the performance of the planner, and (vi) show that the performance of MENDELEEV’s generated code is comparable with that of hand-written analytics

Parallelising wavefront applications on general-purpose GPU devices

Pipelined wavefront applications form a large portion of the high performance scientific computing workloads at supercomputing centres. This paper investigates the viability of graphics processing units (GPUs) for the acceleration of these codes, using NVIDIA's Compute Unified Device Architecture (CUDA). We identify the optimisations suitable for this new architecture and quantify the characteristics of those wavefront codes that are likely to experience speedups

Heuristic solutions to the target identifiability problem in directional sensor networks

Existing algorithms for orienting sensors in directional sensor networks have primarily concerned themselves with the problem of maximizing the number of covered targets, assuming that target identification is a non-issue. Such an assumption however, does not hold true in all situations. In this paper, heuristic algorithms for choosing active sensors and orienting them with the goal of balancing coverage and identifiability are presented. The performance of the algorithms are verified via extensive simulations, and shown to confer increased target identifiability compared to algorithms originally designed to simply maximize the number of targets covered