721 research outputs found

    Survey and Analysis of Production Distributed Computing Infrastructures

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    This report has two objectives. First, we describe a set of the production distributed infrastructures currently available, so that the reader has a basic understanding of them. This includes explaining why each infrastructure was created and made available and how it has succeeded and failed. The set is not complete, but we believe it is representative. Second, we describe the infrastructures in terms of their use, which is a combination of how they were designed to be used and how users have found ways to use them. Applications are often designed and created with specific infrastructures in mind, with both an appreciation of the existing capabilities provided by those infrastructures and an anticipation of their future capabilities. Here, the infrastructures we discuss were often designed and created with specific applications in mind, or at least specific types of applications. The reader should understand how the interplay between the infrastructure providers and the users leads to such usages, which we call usage modalities. These usage modalities are really abstractions that exist between the infrastructures and the applications; they influence the infrastructures by representing the applications, and they influence the ap- plications by representing the infrastructures

    Multisite adaptive computation offloading for mobile cloud applications

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    The sheer amount of mobile devices and their fast adaptability have contributed to the proliferation of modern advanced mobile applications. These applications have characteristics such as latency-critical and demand high availability. Also, these kinds of applications often require intensive computation resources and excessive energy consumption for processing, a mobile device has limited computation and energy capacity because of the physical size constraints. The heterogeneous mobile cloud environment consists of different computing resources such as remote cloud servers in faraway data centres, cloudlets whose goal is to bring the cloud closer to the users, and nearby mobile devices that can be utilised to offload mobile tasks. Heterogeneity in mobile devices and the different sites include software, hardware, and technology variations. Resource-constrained mobile devices can leverage the shared resource environment to offload their intensive tasks to conserve battery life and improve the overall application performance. However, with such a loosely coupled and mobile device dominating network, new challenges and problems such as how to seamlessly leverage mobile devices with all the offloading sites, how to simplify deploying runtime environment for serving offloading requests from mobile devices, how to identify which parts of the mobile application to offload and how to decide whether to offload them and how to select the most optimal candidate offloading site among others. To overcome the aforementioned challenges, this research work contributes the design and implementation of MAMoC, a loosely coupled end-to-end mobile computation offloading framework. Mobile applications can be adapted to the client library of the framework while the server components are deployed to the offloading sites for serving offloading requests. The evaluation of the offloading decision engine demonstrates the viability of the proposed solution for managing seamless and transparent offloading in distributed and dynamic mobile cloud environments. All the implemented components of this work are publicly available at the following URL: https://github.com/mamoc-repo

    ‘It becomes increasingly complex to deal with multiple channels’: materialised communicative competence and digital inequality in English-medium higher education in the digital era

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    This article explores university students’ communicative competence for English-Medium Instruction (EMI) at a Swedish university in the era of digitalisation and blended learning. Based on a linguistic ethnography, we present an argument for communicative competence as repertoire assemblages orchestrating digital materiality and human language to construct meanings. The study shows how diverse digital multimodalities and AI-language tools are essential features of spatial repertoires for academic communication, and how they cooperate with and mediate students’ personal repertoires to accomplish interactive learning tasks in EMI contexts. The study also highlights how digital diversity in EMI causes a ‘digital divide’, potentially impacting power relations among students. These findings underline the importance of acknowledging the communicative value of digital materiality and negotiating difference and normativity for intercultural academic communication in EMI

    Understanding business ecosystem using a 6C framework in Internet-of-Things-based sectors

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    With fast development and application, the Internet of Things (IoT) brings more opportunities to business. This research aims to investigate how IoT could lead to a co-evolving business ecosystem rather than a supply chain. It develops the 6C framework to analyze the data collected from case companies, and identifies three patterns of IoT-based business ecosystem. It also provides a summary of practical implications to guide practitioners building an IoT-based business ecosystem

    Managing enterprise resource planning and multi-organisational enterprise governance:a new contingency framework for the enterprisation of operations

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    This research has been undertaken to determine how successful multi-organisational enterprise strategy is reliant on the correct type of Enterprise Resource Planning (ERP) information systems being used. However there appears to be a dearth of research as regards strategic alignment between ERP systems development and multi-organisational enterprise governance as guidelines and frameworks to assist practitioners in making decision for multi-organisational collaboration supported by different types of ERP systems are still missing from theoretical and empirical perspectives. This calls for this research which investigates ERP systems development and emerging practices in the management of multi-organisational enterprises (i.e. parts of companies working with parts of other companies to deliver complex product-service systems) and identify how different ERP systems fit into different multi-organisational enterprise structures, in order to achieve sustainable competitive success. An empirical inductive study was conducted using the Grounded Theory-based methodological approach based on successful manufacturing and service companies in the UK and China. This involved an initial pre-study literature review, data collection via 48 semi-structured interviews with 8 companies delivering complex products and services across organisational boundaries whilst adopting ERP systems to support their collaborative business strategies – 4 cases cover printing, semiconductor manufacturing, and parcel distribution industries in the UK and 4 cases cover crane manufacturing, concrete production, and banking industries in China in order to form a set of 29 tentative propositions that have been validated via a questionnaire receiving 116 responses from 16 companies. The research has resulted in the consolidation of the validated propositions into a novel concept referred to as the ‘Dynamic Enterprise Reference Grid for ERP’ (DERG-ERP) which draws from multiple theoretical perspectives. The core of the DERG-ERP concept is a contingency management framework which indicates that different multi-organisational enterprise paradigms and the supporting ERP information systems are not the result of different strategies, but are best considered part of a strategic continuum with the same overall business purpose of multi-organisational cooperation. At different times and circumstances in a partnership lifecycle firms may prefer particular multi-organisational enterprise structures and the use of different types of ERP systems to satisfy business requirements. Thus the DERG-ERP concept helps decision makers in selecting, managing and co-developing the most appropriate multi-organistional enterprise strategy and its corresponding ERP systems by drawing on core competence, expected competitiveness, and information systems strategic capabilities as the main contingency factors. Specifically, this research suggests that traditional ERP(I) systems are associated with Vertically Integrated Enterprise (VIE); whilst ERPIIsystems can be correlated to Extended Enterprise (EE) requirements and ERPIII systems can best support the operations of Virtual Enterprise (VE). The contribution of this thesis is threefold. Firstly, this work contributes to a gap in the extant literature about the best fit between ERP system types and multi-organisational enterprise structure types; and proposes a new contingency framework – the DERG-ERP, which can be used to explain how and why enterprise managers need to change and adapt their ERP information systems in response to changing business and operational requirements. Secondly, with respect to a priori theoretical models, the new DERG-ERP has furthered multi-organisational enterprise management thinking by incorporating information system strategy, rather than purely focusing on strategy, structural, and operational aspects of enterprise design and management. Simultaneously, the DERG-ERP makes theoretical contributions to the current IS Strategy Formulation Model which does not explicitly address multi-organisational enterprise governance. Thirdly, this research clarifies and emphasises the new concept and ideas of future ERP systems (referred to as ERPIII) that are inadequately covered in the extant literature. The novel DERG-ERP concept and its elements have also been applied to 8 empirical cases to serve as a practical guide for ERP vendors, information systems management, and operations managers hoping to grow and sustain their competitive advantage with respect to effective enterprise strategy, enterprise structures, and ERP systems use; referred to in this thesis as the “enterprisation of operations”

    Resource allocation in mobile edge cloud computing for data-intensive applications

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    Rapid advancement in the mobile telecommunications industry has motivated the development of mobile applications in a wide range of social and scientific domains. However, mobile computing (MC) platforms still have several constraints, such as limited computation resources, short battery life and high sensitivity to network capabilities. In order to overcome the limitations of mobile computing and benefit from the huge advancement in mobile telecommunications and the rapid revolution of distributed resources, mobile-aware computing models, such as mobile cloud computing (MCC) and mobile edge computing (MEC) have been proposed. The main problem is to decide on an application execution plan while satisfying quality of service (QoS) requirements and the current status of system networking and device energy. However, the role of application data in offloading optimisation has not been studied thoroughly, particularly with respect to how data size and distribution impact application offloading. This problem can be referred to as data-intensive mobile application offloading optimisation. To address this problem, this thesis presents novel optimisation frameworks, techniques and algorithms for mobile application resource allocation in mobile-aware computing environments. These frameworks and techniques are proposed to provide optimised solutions to schedule data intensive mobile applications. Experimental results show the ability of the proposed tools in optimising the scheduling and the execution of data intensive applications on various computing environments to meet application QoS requirements. Furthermore, the results clearly stated the significant contribution of the data size parameter on scheduling the execution of mobile applications. In addition, the thesis provides an analytical investigation of mobile-aware computing environments for a certain mobile application type. The investigation provides performance analysis to help users decide on target computation resources based on application structure, input data, and mobile network status
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