249 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    HeteroSketch: coordinating network-wide monitoring in heterogeneous and dynamic networks

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    CNS-2107086 - National Science Foundation; CNS-2106946 - National Science FoundationPublished versio

    Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches

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    Traditional networking devices support only fixed features and limited configurability. Network softwarization leverages programmable software and hardware platforms to remove those limitations. In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms. This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0. P4 is the most popular technology to implement programmable data planes. However, programmable data planes, and in particular, the P4 technology, emerged only recently. Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking. The research of this thesis focuses on two open issues of programmable data planes. First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet. Second, it enables BIER in high-performance P4 data planes. BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet. The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study. Two more peer-reviewed papers contain additional content that is not directly related to the main results. They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    On Constructing Spanners from Random Gaussian Projections

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    Graph sketching is a powerful paradigm for analyzing graph structure via linear measurements introduced by Ahn, Guha, and McGregor (SODA\u2712) that has since found numerous applications in streaming, distributed computing, and massively parallel algorithms, among others. Graph sketching has proven to be quite successful for various problems such as connectivity, minimum spanning trees, edge or vertex connectivity, and cut or spectral sparsifiers. Yet, the problem of approximating shortest path metric of a graph, and specifically computing a spanner, is notably missing from the list of successes. This has turned the status of this fundamental problem into one of the most longstanding open questions in this area. We present a partial explanation of this lack of success by proving a strong lower bound for a large family of graph sketching algorithms that encompasses prior work on spanners and many (but importantly not also all) related cut-based problems mentioned above. Our lower bound matches the algorithmic bounds of the recent result of Filtser, Kapralov, and Nouri (SODA\u2721), up to lower order terms, for constructing spanners via the same graph sketching family. This establishes near-optimality of these bounds, at least restricted to this family of graph sketching techniques, and makes progress on a conjecture posed in this latter work

    Oscar Bait: Exploring Links Between an Academy Awards Institutional Persona and Perceptions of Oscar-Worthiness

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    The Academy Awards – or ‘the Oscars’ – and their large-scale television production have historically occupied a unique position as a taste-making apparatus and gatekeeper of prestige stardom. In evaluating ‘the best’ of the (American-centric) filmmaking field, they wield cultural influence over such cinema practices as consumption and evaluation, filmmaking aesthetics and narratives, and the discursive activity of Hollywood’s industrial agents and engaged audiences. This research recontextualises the Oscars’ complex legacy into a new media ecosystem, one in which their established value is undercut by declining broadcast viewership, the changing values and demands of a global film culture, and influential discourses aiming to progress popular culture beyond its problematic histories. In this new paradigm of film production and consumption, I ask what the Oscars mean in a contemporary filmmaking landscape, and the value or influence that established stereotypes of Oscar-worthiness – the colloquial ‘Oscar Bait’ – continue to hold over the awards. I first argue for the Oscars’ position of power within filmmaking production cycles. Using a Bourdieusian framework of ‘taste-making’ and ‘capital’, the Oscars are identified as a site upon which industrial agents negotiate the demands of the cultural terrain. Beyond a theoretical setting, however, the Oscars also occupy the position of an agent – itself vying for prestigious attention in a tumultuous media landscape. As such, I also conceptualise ‘Oscar’ as a mediated industrial persona. To investigate Oscar’s contemporary meaning and its position as a persona, I conducted a textual analysis on a three-year sample (2019-2021) of cultural texts that, combined, contribute to the Oscar persona. This included the televised awards ceremony of each year and their associated paratexts, the core film texts of each year’s competition, and the broader discursive activities of film awards culture. From this methodology I extracted three key thematic contests that courted significant attention, thus speaking to a perceived ‘meaning’ of what the Oscars are for. Firstly, representation within filmmaking endures as an unsettled concept, whereby Oscar constantly must reassess its own values of inclusivity, diversity, and merit. Secondly, Oscar serves as a vital organ of Hollywood’s celebrity mythmaking, whereby individual celebrity narratives are enacted and negotiated for the sake of symbolic capital. Finally, Oscar continues to assert particular ideals, aesthetics, morals, and individuals as the best of the filmmaking field, simultaneously recreating and drawing from such power to present itself as a quality television product. Through these analytical threads, my research impacts current conceptions of cultural prestige and mythmaking within film, interpreting the Oscars as a mediated phenomenon for its power implications and as an institutional persona navigating the demands of its public.Thesis (Ph.D.) -- University of Adelaide, School of Humanities, 202

    Random measure priors in Bayesian frequency recovery from sketches

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    Given a lossy-compressed representation, or sketch, of data with values in a set of symbols, the frequency recovery problem considers the estimation of the empirical frequency of a new data point. Recent studies have applied Bayesian nonparametrics (BNPs) to develop learning-augmented versions of the popular count-min sketch (CMS) recovery algorithm. In this paper, we present a novel BNP approach to frequency recovery, which is not built from the CMS but still relies on a sketch obtained by random hashing. Assuming data to be modeled as random samples from an unknown discrete distribution, which is endowed with a Poisson-Kingman (PK) prior, we provide the posterior distribution of the empirical frequency of a symbol, given the sketch. Estimates are then obtained as mean functionals. An application of our result is presented for the Dirichlet process (DP) and Pitman-Yor process (PYP) priors, and in particular: i) we characterize the DP prior as the sole PK prior featuring a property of sufficiency with respect to the sketch, leading to a simple posterior distribution; ii) we identify a large sample regime under which the PYP prior leads to a simple approximation of the posterior distribution. Then, we develop our BNP approach to a "traits" formulation of the frequency recovery problem, not yet studied in the CMS literature, in which data belong to more than one symbol (trait), and exhibit nonnegative integer levels of associations with each trait. In particular, by modeling data as random samples from a generalized Indian buffet process, we provide the posterior distribution of the empirical frequency level of a trait, given the sketch. This result is then applied under the assumption of a Poisson and Bernoulli distribution for the levels of associations, leading to a simple posterior distribution and a simple approximation of the posterior distribution, respectively

    SketchLib: enabling efficient sketch-based monitoring on programmable switches

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    CNS-2107086 - National Science Foundation; CNS-2106946 - National Science FoundationPublished versio

    Social convergence in times of spatial distancing: The rRole of music during the COVID-19 Pandemic

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