60,083 research outputs found

    Cloud computing and context-awareness : a study of the adapted user experience

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    Today, mobile technology is part of everyday life and activities and the mobile ecosystems are blossoming, with smartphones and tablets being the major growth drivers. The mobile phones are no longer just another device, we rely on their capabilities in work and in private. We look to our mobile phones for timely and updated information and we rely on this being provided any time of any day at any place. Nevertheless, no matter how much you trust and love your mobile phone the quality of the information and the user experience is directly associated with the sources and presentation of information. In this perspective, our activities, interactions and preferences help shape the quality of service, content and products we use. Context-aware systems use such information about end-users as input mechanisms for producing applications based on mobile, location, social, cloud and customized content services. This represents new possibilities for extracting aggregated user-centric information and includes novel sources for context-aware applications. Accordingly, a Design Research based approach has been taken to further investigate the creation, presentation and tailoring of user-centric information. Through user evaluated experiments findings show how multi-dimensional context-aware information can be used to create adaptive solutions tailoring the user experience to the users’ needs. Research findings in this work; highlight possible architectures for integration of cloud computing services in a heterogeneous mobile environment in future context-aware solutions. When it comes to combining context-aware results from local computations with those of cloud based services, the results provide findings that give users tailored and adapted experiences based on the collective efforts of the two.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Next Generation Cloud Computing: New Trends and Research Directions

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    The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201

    Efficient data representation for XML in peer-based systems

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    Purpose - New directions in the provision of end-user computing experiences mean that the best way to share data between small mobile computing devices needs to be determined. Partitioning large structures so that they can be shared efficiently provides a basis for data-intensive applications on such platforms. The partitioned structure can be compressed using dictionary-based approaches and then directly queried without firstly decompressing the whole structure. Design/methodology/approach - The paper describes an architecture for partitioning XML into structural and dictionary elements and the subsequent manipulation of the dictionary elements to make the best use of available space. Findings - The results indicate that considerable savings are available by removing duplicate dictionaries. The paper also identifies the most effective strategy for defining dictionary scope. Research limitations/implications - This evaluation is based on a range of benchmark XML structures and the approach to minimising dictionary size shows benefit in the majority of these. Where structures are small and regular, the benefits of efficient dictionary representation are lost. The authors' future research now focuses on heuristics for further partitioning of structural elements. Practical implications - Mobile applications that need access to large data collections will benefit from the findings of this research. Traditional client/server architectures are not suited to dealing with high volume demands from a multitude of small mobile devices. Peer data sharing provides a more scalable solution and the experiments that the paper describes demonstrate the most effective way of sharing data in this context. Social implications - Many services are available via smartphone devices but users are wary of exploiting the full potential because of the need to conserve battery power. The approach mitigates this challenge and consequently expands the potential for users to benefit from mobile information systems. This will have impact in areas such as advertising, entertainment and education but will depend on the acceptability of file sharing being extended from the desktop to the mobile environment. Originality/value - The original work characterises the most effective way of sharing large data sets between small mobile devices. This will save battery power on devices such as smartphones, thus providing benefits to users of such devices

    A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

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    Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless Communications and Mobile Computing journa

    Addressing the Challenges in Federating Edge Resources

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    This book chapter considers how Edge deployments can be brought to bear in a global context by federating them across multiple geographic regions to create a global Edge-based fabric that decentralizes data center computation. This is currently impractical, not only because of technical challenges, but is also shrouded by social, legal and geopolitical issues. In this chapter, we discuss two key challenges - networking and management in federating Edge deployments. Additionally, we consider resource and modeling challenges that will need to be addressed for a federated Edge.Comment: Book Chapter accepted to the Fog and Edge Computing: Principles and Paradigms; Editors Buyya, Sriram
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