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    エッジクラウドコンピューティングにおける資源割当手法に関する研究

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    Edge and cloud computing is expected as a novel network architecture for supporting people’s daily lives and work. In edge and cloud computing, users expect to receive a wide variety of services with different requirements in terms of transmission rates on various locations, so there are some important issues regarding efficient service provision to meet user requirements independently of their locations: (1) fair service provision in cloud computing, (2) service provision according to user requirements in edge computing, and (3) efficient transmission in edge and cloud computing. In this study, I propose efficient schemes to allocate and use network resources among flows to solve these issues in edge and cloud computing and show the effectiveness of the proposed schemes through simulation evaluations.北九州市立大

    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
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