156 research outputs found

    Locality-preserving allocations Problems and coloured Bin Packing

    Get PDF
    We study the following problem, introduced by Chung et al. in 2006. We are given, online or offline, a set of coloured items of different sizes, and wish to pack them into bins of equal size so that we use few bins in total (at most α\alpha times optimal), and that the items of each colour span few bins (at most β\beta times optimal). We call such allocations (α,β)(\alpha, \beta)-approximate. As usual in bin packing problems, we allow additive constants and consider (α,β)(\alpha,\beta) as the asymptotic performance ratios. We prove that for \eps>0, if we desire small α\alpha, no scheme can beat (1+\eps, \Omega(1/\eps))-approximate allocations and similarly as we desire small β\beta, no scheme can beat (1.69103, 1+\eps)-approximate allocations. We give offline schemes that come very close to achieving these lower bounds. For the online case, we prove that no scheme can even achieve (O(1),O(1))(O(1),O(1))-approximate allocations. However, a small restriction on item sizes permits a simple online scheme that computes (2+\eps, 1.7)-approximate allocations

    Subject index volumes 1–92

    Get PDF

    Intelligent Load Balancing in Cloud Computer Systems

    Get PDF
    Cloud computing is an established technology allowing users to share resources on a large scale, never before seen in IT history. A cloud system connects multiple individual servers in order to process related tasks in several environments at the same time. Clouds are typically more cost-effective than single computers of comparable computing performance. The sheer physical size of the system itself means that thousands of machines may be involved. The focus of this research was to design a strategy to dynamically allocate tasks without overloading Cloud nodes which would result in system stability being maintained at minimum cost. This research has added the following new contributions to the state of knowledge: (i) a novel taxonomy and categorisation of three classes of schedulers, namely OS-level, Cluster and Big Data, which highlight their unique evolution and underline their different objectives; (ii) an abstract model of cloud resources utilisation is specified, including multiple types of resources and consideration of task migration costs; (iii) a virtual machine live migration was experimented with in order to create a formula which estimates the network traffic generated by this process; (iv) a high-fidelity Cloud workload simulator, based on a month-long workload traces from Google's computing cells, was created; (v) two possible approaches to resource management were proposed and examined in the practical part of the manuscript: the centralised metaheuristic load balancer and the decentralised agent-based system. The project involved extensive experiments run on the University of Westminster HPC cluster, and the promising results are presented together with detailed discussions and a conclusion

    Working With Incremental Spatial Data During Parallel (GPU) Computation

    Get PDF
    Central to many complex systems, spatial actors require an awareness of their local environment to enable behaviours such as communication and navigation. Complex system simulations represent this behaviour with Fixed Radius Near Neighbours (FRNN) search. This algorithm allows actors to store data at spatial locations and then query the data structure to find all data stored within a fixed radius of the search origin. The work within this thesis answers the question: What techniques can be used for improving the performance of FRNN searches during complex system simulations on Graphics Processing Units (GPUs)? It is generally agreed that Uniform Spatial Partitioning (USP) is the most suitable data structure for providing FRNN search on GPUs. However, due to the architectural complexities of GPUs, the performance is constrained such that FRNN search remains one of the most expensive common stages between complex systems models. Existing innovations to USP highlight a need to take advantage of recent GPU advances, reducing the levels of divergence and limiting redundant memory accesses as viable routes to improve the performance of FRNN search. This thesis addresses these with three separate optimisations that can be used simultaneously. Experiments have assessed the impact of optimisations to the general case of FRNN search found within complex system simulations and demonstrated their impact in practice when applied to full complex system models. Results presented show the performance of the construction and query stages of FRNN search can be improved by over 2x and 1.3x respectively. These improvements allow complex system simulations to be executed faster, enabling increases in scale and model complexity

    Learning reliable representations when proxy objectives fail

    Get PDF
    Representation learning involves using an objective to learn a mapping from data space to a representation space. When the downstream task for which a mapping must be learned is unknown, or is too costly to cast as an objective, we must rely on proxy objectives for learning. In this Thesis I focus on representation learning for images, and address three cases where proxy objectives fail to produce a mapping that performs well on the downstream tasks. When learning neural network mappings from image space to a discrete hash space for fast content-based image retrieval, a proxy objective is needed which captures the requirement for relevant responses to be nearer to the hash of any query than irrelevant ones. At the same time, it is important to ensure an even distribution of image hashes across the whole hash space for efficient information use and high discrimination. Proxy objectives fail when they do not meet these requirements. I propose composing hash codes in two parts. First a standard classifier is used to predict class labels that are converted to a binary representation for state-of-the-art performance on the image retrieval task. Second, a binary deep decision tree layer (DDTL) is used to model further intra-class differences and produce approximately evenly distributed hash codes. The DDTL requires no discretisation during learning and produces hash codes that enable better discrimination between data in the same class when compared to previous methods, while remaining robust to real-world augmentations in the data space. In the scenario where we require a neural network to partition the data into clusters that correspond well with ground-truth labels, a proxy objective is needed to define how these clusters are formed. One such proxy objective involves maximising the mutual information between cluster assignments made by a neural network from multiple views. In this context, views are different augmentations of the same image and the cluster assignments are the representations computed by a neural network. I demonstrate that this proxy objective produces parameters for the neural network that are sub-optimal in that a better set of parameters can be found using the same objective and a different training method. I introduce deep hierarchical object grouping (DHOG) as a method to learn a hierarchy (in the sense of easy-to-hard orderings, not structure) of solutions to the proxy objective and show how this improves performance on the downstream task. When there are features in the training data from which it is easier to compute class predictions (e.g., background colour), when compared to features for which it is relatively more difficult to compute class predictions (e.g., digit type), standard classification objectives (e.g., cross-entropy) fail to produce robust classifiers. The problem is that if a model learns to rely on `easy' features it will also ignore `complex' features (easy versus complex are purely relative in this case). I introduce latent adversarial debiasing (LAD) to decouple easy features from the class labels by first modelling the underlying structure of the training data as a latent representation using a vector-quantised variational autoencoder, and then I use a gradient-based procedure to adjust the features in this representation to confuse the predictions of a constrained classifier trained to predict class labels from the same representation. The adjusted representations of the data are then decoded to produce an augmented training dataset that can be used for training in a standard manner. I show in the aforementioned scenarios that proxy objectives can fail and demonstrate that alternative approaches can mitigate against the associated failures. I suggest an analytic approach to understanding the limits of proxy objectives for every use case in order to make the adjustments to the data or the objectives and ensure good performance on downstream tasks

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    LIPIcs, Volume 244, ESA 2022, Complete Volume

    Get PDF
    LIPIcs, Volume 244, ESA 2022, Complete Volum

    Border dialogues : race, class and space in the industrialization of East London, c1902-1963

    Get PDF
    Bibliography: pages 361-389.This dissertation explores the local path of industrialization in the port City of East London from its emergence as the urban commercial axis of the Border Region of the Eastern Cape, to the dominance of manufacturing capitalism in its material life. The trajectory of this process between c1902 and 1963 was hesitant, uneven and contradictory, and its local economy remained marginal within South Africa, if not within the Region it critically served to help define. From the space of this marginality, a profound edge on the multiple possible routes, and ambiguities to, and in industrialization are demonstrated, and a cautionary critique of dominant 'national' and 'Randcentric' explanations offered. Employing concerns of spatiality, and of the analysis and local constructions of class and race, the separate, and inter-connected relations between the Workplaces, the Council and Municipal Administration and the Location/s are detailed. Framed within these concerns, local industrialization patterned a distinctive periodization that did not necessarily follow existing explanation, but neither did it determine alIloca1ized processes of continuity and change. These tensions between colonial, racial and class social and material spatialities and histories sedimented industrialization in a context that would remain simultaneously narrowly enabled, and dependently constrained. In this, local forms of power and knowledge, subaltern capacities and agency, and the distinct forms of space intersected in a complex web of relations of domination and subordination, and of solidarity and co-operation. These are traced through the four key periods highlighted. The dissertation can be seen to fall into these four periods tracked across the three material and social terrains, and analysed through the combined, separate and uneven racial and class forces patterned, and re-shaped in East London's process of industrialization. It concludes with the period of its transition onto the national terrains of the apartheid state's secondary phase of systemic and inclusive restructuring. Thereafter, local industrialization became integrated into a new 'national' dynamic of intervention and contradiction

    Designing, Building, and Modeling Maneuverable Applications within Shared Computing Resources

    Get PDF
    Extending the military principle of maneuver into war-fighting domain of cyberspace, academic and military researchers have produced many theoretical and strategic works, though few have focused on researching actual applications and systems that apply this principle. We present our research in designing, building and modeling maneuverable applications in order to gain the system advantages of resource provisioning, application optimization, and cybersecurity improvement. We have coined the phrase “Maneuverable Applications” to be defined as distributed and parallel application that take advantage of the modification, relocation, addition or removal of computing resources, giving the perception of movement. Our work with maneuverable applications has been within shared computing resources, such as the Clemson University Palmetto cluster, where multiple users share access and time to a collection of inter-networked computers and servers. In this dissertation, we describe our implementation and analytic modeling of environments and systems to maneuver computational nodes, network capabilities, and security enhancements for overcoming challenges to a cyberspace platform. Specifically we describe our work to create a system to provision a big data computational resource within academic environments. We also present a computing testbed built to allow researchers to study network optimizations of data centers. We discuss our Petri Net model of an adaptable system, which increases its cybersecurity posture in the face of varying levels of threat from malicious actors. Lastly, we present work and investigation into integrating these technologies into a prototype resource manager for maneuverable applications and validating our model using this implementation
    corecore