| 基于综合信任的边缘计算资源协同研究 |
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| 引用本文： | 邓晓衡, 关培源, 万志文, 刘恩陆, 罗杰, 赵智慧, 刘亚军, 张洪刚. 基于综合信任的边缘计算资源协同研究[J]. 计算机研究与发展, 2018, 55(3): 449-477. DOI: 10.7544/issn1000-1239.2018.20170800 |
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| 作者姓名： | 邓晓衡 关培源 万志文 刘恩陆 罗杰 赵智慧 刘亚军 张洪刚 |
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| 作者单位： | 1.1(中南大学信息科学与工程学院 长沙 410075);2.2(中南大学软件学院 长沙 410075);3.3(马萨诸塞大学波士顿分校工程系 波士顿 02125-3393) (dxh@csu.edu.cn) |
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| 基金项目： | 国家自然科学基金项目(61772553, 61379058) |
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| 摘 要： | 边缘计算旨在共享利用边缘设备的计算、通信资源，满足人们对服务的实时响应、隐私与安全、计算自主性等需求，随物联网的发展将有广阔应用前景.调研了边缘计算的特征、基本概念和定义、最新研究进展以及边缘计算研究的挑战与发展趋势.基于边缘计算的核心挑战之一——隐私信任与安全保障问题，针对用户应用需求特征，充分考虑用户体验质量(QoE)来优化边缘计算系统.通过集成用户和资源的身份信任、行为信任、能力信任3个方面为综合信任度，利用信任评估保障对边缘计算资源管理与协同优化.针对终端的动态性、边缘设施能力受限、边缘与终端的邻近性、云中心功能强和距离远的特征，融合云计算、P2P计算、CS与网格计算模式，构建多层自适应的统一计算模型，实现对应用场景动态匹配；研究以用户体验质量为目标的综合资源用户信任评估体系与模型，实现资源QoS向QoE的指标映射，构建资源和用户的身份信任、行为信任评价机制，形成综合信任评估体系与模型；根据应用需求，研究面向计算能力、移动性与可用服务时间、剩余能量、带宽等多重约束的边缘计算的任务卸载、资源调度算法和优化方案，实现资源在终端、边缘、云中心3层级可信共享和优化利用，更好满足用户QoE需求.最后通过流计算任务分配的边缘计算场景验证了模型框架的有效性.
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| 关 键 词： | 分布式计算 边缘计算 雾计算 移动边缘计算 物联网 综合信任 |
Integrated Trust Based Resource Cooperation in Edge Computing |
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| Deng Xiaoheng, Guan Peiyuan, Wan Zhiwen, Liu Enlu, Luo Jie, Zhao Zhihui, Liu Yajun, Zhang Honggang. Integrated Trust Based Resource Cooperation in Edge Computing[J]. Journal of Computer Research and Development, 2018, 55(3): 449-477. DOI: 10.7544/issn1000-1239.2018.20170800 |
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| Authors: | Deng Xiaoheng Guan Peiyuan Wan Zhiwen Liu Enlu Luo Jie Zhao Zhihui Liu Yajun Zhang Honggang |
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| Affiliation: | 1.1(School of Information Science & Engineering, Central South University, Changsha 410075);2.2(School of Software, Central South University, Changsha 410075);3.3(Engineering Department, University of Massachusetts Boston, Boston 02125-3393) |
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| Abstract: | Edge computing, as a new computing paradigm, is designed to share resources of edge devices, such as CPU computing ability, bandwidth, storage capacity and so on, to meet the requirements of the real-time response, privacy and security, computing autonomy. With the development of Internet of things (IoT) and mobile Internet technology, edge computing is of great potential of being widely used. This paper investigates the basic features, concepts and definitions, the latest state of art, and the challenge and trends of edge computing. Based on the key challenge of guarantee of users’ quality of experience (QoE), privacy and security in edge computing, we focus on the requirement of users and consider the quality of experience of users to optimize the edge computing system. We integrate three aspects of trust properties, which are identity trust, behavior trust and ability trust, to evaluate resources and users to ensure the success of resource sharing and collaborative optimization in edge computing. This paper also investigates various computing modes such as cloud computing, P2P computing, CS and grid computing, and constructs a multi-layer, self-adaptive, uniform computing model to dynamically match different application scenarios. This model has four contributions: 1) reveal the mechanism of parameters mapping between quality of service (QoS) and quality of experience; 2) construct identity trust, behavior trust of resources and users evaluation mechanisms; 3) form an integrated trust evaluation architecture and model; 4) design a resource scheduling algorithm for stream processing scenario, considering the computing ability, storage capacity and dynamical channel capacity depends on mobility to improve the quality of experience of users. Through this model and mechanism, resources in the end point, edge network, cloud center three levels are expected to be trusted sharing and optimized using, and the users' QoE needs are well satisfied. At last, simulation results show the validity of the model. |
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| Keywords: | distributed computing edge computing fog computing mobile edge computing Internet of things integrated trust |
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