1,721,004 research outputs found
Stream Processing on Clustered Edge Devices
No full textThe Internet of Things continuously generates avalanches of raw sensor data to be transferred to the Cloud for processing and storage. Due to network latency and limited bandwidth, this vertical offloading model, however, fails to meet requirements of time-critical data-intensive applications which must act upon generated data with minimum time delays. To address such a limitation, this article proposes a novel distributed architecture enabling stream data processing at the edge of the network, broadening the principle of enabling processing closer to data sources adopted by Fog and Edge Computing. Specifically, this architecture extends the Apache NiFi stream processing middleware with support for run-time clustering of heterogeneous edge devices, such that computational tasks can be horizontally offloaded to peer devices and executed in parallel. As opposed to vertical offloading on the Cloud, the proposed solution does not suffer from increased network latency and is thus able to offer 5-25 times faster response time, as demonstrated by the experiments on a run-time license plate recognition system
EXCLAIM framework: a monitoring and analysis framework to support self-governance in Cloud Application Platforms
Full text linkThe Platform-as-a-Service segment of Cloud Computing has been steadily growing over the past several years, with more and more software developers opting for cloud platforms as convenient ecosystems for developing, deploying, testing and maintaining their software. Such cloud platforms also play an important role in delivering an easily-accessible Internet of Services. They provide rich support for software development, and, following the principles of Service-Oriented Computing, offer their subscribers a wide selection of pre-existing, reliable and reusable basic services, available through a common platform marketplace and ready to be seamlessly integrated into users' applications. Such cloud ecosystems are becoming increasingly dynamic and complex, and one of the major challenges faced by cloud providers is to develop appropriate scalable and extensible mechanisms for governance and control based on run-time monitoring and analysis of (extreme amounts of) raw heterogeneous data.
In this thesis we address this important research question -- \textbf{how can we support self-governance in cloud platforms delivering the Internet of Services in the presence of large amounts of heterogeneous and rapidly changing data?} To address this research question and demonstrate our approach, we have created the Extensible Cloud Monitoring and Analysis (EXCLAIM) framework for service-based cloud platforms. The main idea underpinning our approach is to encode monitored heterogeneous data using Semantic Web languages, which then enables us to integrate these semantically enriched observation streams with static ontological knowledge and to apply intelligent reasoning. This has allowed us to create an extensible, modular, and declaratively defined architecture for performing run-time data monitoring and analysis with a view to detecting critical situations within cloud platforms.
By addressing the main research question, our approach contributes to the domain of Cloud Computing, and in particular to the area of autonomic and self-managing capabilities of service-based cloud platforms. Our main contributions include the approach itself, which allows monitoring and analysing heterogeneous data in an extensible and scalable manner, the prototype of the EXCLAIM framework, and the Cloud Sensor Ontology. Our research also contributes to the state of the art in Software Engineering by demonstrating how existing techniques from several fields (i.e., Autonomic Computing, Service-Oriented Computing, Stream Processing, Semantic Sensor Web, and Big Data) can be combined in a novel way to create an extensible, scalable, modular, and declaratively defined monitoring and analysis solution
Effects of nitrite and nitroxyl on human vascular and platelet function.
Full text linkThe identification of Nitric oxide (NO) as an endothelium-derived relaxing factor stimulated research into the physiology of this most important biological messenger, which maintains a healthy vascular endothelium and an anti-thrombotic intravascular environment. Healthy endothelial cells constantly produce NO to create ‘basal’ vasorelaxation via the classical L-arginine/sGC/cGMP activation cascade. Under physiological conditions this NO pathway is the fundamental to maintenance of normal cardiovascular health, and conversely it is the substrate for development of many cardiovascular disease states, when the balance in this system becomes impaired. Endothelial dysfunction, with the closely associated phenomenon of “NO resistance”, can affect any NO-sensitive tissues including blood vessels and platelets, and is now believed to trigger atherogenesis and thrombogenesis. Treatment of cardiovascular diseases associated with this phenomenon utilizing NO donors often has proved to be ineffective. Furthermore, treatment with organic nitrates is subject to development of nitrate tolerance, limiting efficacy of this class of agents. Several agents can ameliorate NO resistance over days or weeks, but there remains a problem in circumventing NO resistance in cardiac emergencies. In this thesis we demonstrate for the first time in humans partial circumvention of NO resistance with nitroxyl, a structural analogue of NO. Additionally, another NO sibling nitrite (NO₂⁻) has been attracting substantial interest in the last decade. Evidence has been accumulating that effects of nitrite are increased during hypoxia: - nitrite becomes a potent vasodilator and anti-aggregant when compared to normoxic environment. This is especially important in the situation of chronic tissue hypoxia or in acute vascular emergencies. Key findings from the experiments in this thesis are: 1. Nitrite is a potent vasodilator compared to GTN: in general nitrite vasodilator effects are significantly potentiated in hypoxia in human saphenous veins. However, in human internal mammary arteries, nitrite-induced vasodilation is not potentiated under hypoxia. Prolonged exposure of human saphenous vein to nitrite does not cause tolerance or cross-tolerance to GTN. Nitrite effects in saphenous veins are substantially inhibited by ODQ, suggesting that they are largely mediated by soluble guanylate cyclase. Haemoglobin, myoglobin and red blood cells significantly increase hypoxic potentiation of nitrite vasodilator effects in human saphenous veins. Hypoxic potentiation of nitrite is diminished when saphenous vein intrinsic myoglobin is blocked by ferricyanide. 2. In platelets, the anti-aggregatory effects of nitrite are markedly and selectively potentiated under hypoxia. However, nitrite is subject to “NO resistance”. Antiaggregatory actions of nitrite are more potent in venous relative to arterial blood and correlate with (greater) deoxyhaemoglobin levels. Deoxyhaemoglobin is the primary nitrite reductase in blood. We have also presented evidence that continuous generation of NO from endogenous nitrite is important in homeostasis of platelet aggregability. 3. Nitroxyl is a more potent anti-aggregant than SNP. Anti-aggregatory effects of nitroxyl are partially sGC mediated. Nitroxyl partially circumvents the phenomenon of “NO resistance” in platelets. Nitroxyl is also a potent dilator of human saphenous veins. Its effects are not NO-mediated but partially sGCmediated.Thesis (Ph.D.) -- University of Adelaide, School of Medicine, 201
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Automating IoT Data-Intensive Application Allocation in Clustered Edge Computing
Full text linkEnabling data processing at the network edge, as close to the actual source of data as possible, is a challenging, yet realistic goal to be achieved by the Internet of Things (IoT), which still primarily relies on the Cloud for data processing. By further extending the Fog and Edge computing principles, recent research advancements enabled aggregation of computing resources from multiple edge devices to support data-intensive task processing using Big Data clustering middleware. The use of these existing solutions, however, is hindered by the heterogeneous, dynamic, mobile, resource-constrained, and time-critical nature of IoT ecosystems. More specifically, a particularly challenging goal is to discover, select, and cluster suitable edge devices - on the one hand, and decompose and allocate data-intensive tasks with respect to discovered resources - on the other. To address this challenge, this paper introduces a novel decentralized architecture for clustering heterogeneous edge devices and executing data-intensive IoT workflows. The proposed approach first breaks down a complex workflow into simpler tasks, then discovers and selects suitable edge devices, and finally allocates the tasks to the selected nodes, connecting them to recompose the original workflow. The proposed approach benefits from an intelligent mapping algorithm that takes into account available cluster resources and processing demands to efficiently allocate fine-grained tasks to selected nodes. To support the clusterisation process, the proposed solution relies on a unified semantic knowledge base that provides a common vocabulary of terms for modelling task requirements and edge device properties, as well as enables automated task grouping and match-making for device discovery and selection, using built-in reasoning capabilities.acceptedVersio
Context-Aware Digital Twins to Support Software Management at the Edge
Full text linkWith millions of connected edge gateways, there is a pressing challenge of remote maintenance of containerised software components after the initial release. To support remote update operations, edge software providers have been increasingly adopting digital twin-based device management platforms for run-time monitoring and interaction. A common limitation of these solutions is the lack of support for modelling the multi-dimensional context of edge devices deployed in the field, which hinders the software management in a tailored and context-aware manner. This paper aims to address this lack of context-awareness in digital twins required for edge software assignment by introducing two modelling principles, which allow focusing on the device fleet as a whole and capturing the diverse cyber-physical-social context of individual devices. As part of proof of concept, these principles were incorporated in an existing digital twin platform. This prototype implementation demonstrates the viability of the proposed modelling principles via a running example in the context of a telemedicine application system.acceptedVersio
Towards IoT Diversity via Automated Fleet Management
Full text linkLarge-scale Internet of Things (IoT) systems are characterised by an increased level of heterogeneity, both in terms of hardware and software caused by varying device functionality, capabilities and performance. Moreover, since agile business requirements force IoT vendors to continuously modify the software components deployed at the Edge, even initially uniform devices constituting a common IoT ecosystem might end up running software differing in individual compo nents and/or configurations. The amount of effort required to maintain and operate such an increasingly diverse ecosystem – i.e. to perform fleet management – grows proportionally to the size and complexity of the IoT fleet, and is especially important for IoT vendors aiming to achieve economies of scale. To address this challenge, this paper proposes a model based diversity engineering approach to enable automated fleet management. Based on a model of an IoT system with fine grained modifications to be applied, the proposed approach is able to diversify and manage large-scale IoT systems at run-time. As a proof of concept, the proposed approach was implemented on top of the Azure IoT Hub fleet management facilities, and validated on a Remote Patient Monitoring use case scenario.publishedVersio
DivENACT: Diversity-Aware Fleet Management of Edge Devices
No full textDivENACT is an online tool for managing a fleet of Edge devices (gateways) based on Azure IoT Hub. This archive is the latest version after the end of ENACT projec
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