面向复杂多样物联网业务的边缘计算资源优化配置与部署研究

随着复杂多样的物联网业务不断涌现，如何为差异化的应用提供优质的边缘计算服务具有重要的理论和实际意义。本项目针对物联网应用数据量大、时延敏感性强、计算密集度高以及“终端-边缘-云”多层资源的异构性等特点，研究面向复杂多样物联网业务的边缘计算资源优化配置与部署，以提升系统性能、边缘服务质量及用户体验。定义组件聚类质量评价函数，建立应用图和资源图的映射，提出边缘计算中上下文感知的细粒度自适应任务卸载方法，以降低成本及能耗。采用维诺图划分边缘服务区域，构建“边缘-云”的资源配置成本函数，建议多层异构资源平滑动态分配策略，最小化时延及资源配置成本。建立基于缓存价值、内容可靠性和网络代价的联合效用函数，设计分布式协同边缘缓存调度算法。研究基于用户分布和节点代价的边缘服务器稀疏部署优化策略，以提供高效的边缘服务。构建面向车联网和智能制造的边缘计算实验环境，测试所提出的方法，并提供校园边缘计算示范应用。

With the continuous emergence of complex and diverse services in Internet of Things (IoT), how to provide high quality edge computing services for differentiated applications has important theoretical and practical significance. Aiming at IoT applications with the characteristics of large data quantity, strong delay sensitivity, high computing intensity and heterogeneity of multi-layer device-edge-cloud resources, this project studies optimal resource allocation and deployment of edge computing for varied complex services in IoT in order to improve system performance, quality of services and user experience. The quality evaluation function of component clustering is defined. The graph mapping between application graph and resource graph is built. The context-aware fine-granularity self-adaptive task offloading method in edge computing is proposed to reduce cost and energy consumption. Subsequently, the Voronoi diagram is adopted to divide edge service areas, and cost function of edge-cloud resource allocation is constructed. The smooth dynamic allocation strategy for multi-layer heterogeneous resources is suggested to minimize time delay and resource allocation cost. Moreover, the joint utility function based on cache value, content reliability and network cost is constructed. The distributed cooperative edge cache scheduling algorithm is designed. Furthermore, the sparse deployment optimization strategy of edge servers based on user distribution and node costs is studied to provide efficient edge services. Finally, the edge computing experimental environment for Internet of Vehicles and intelligent manufacturing is built to test the proposed methods, and the demonstration applications of campus edge computing is also provided.