Edge computing can deliver network services with low latency and real-time processing by providing cloud services at the network edge. Edge computing has a number of advantages such as low latency, locality, and network traffic distribution, but the associated resource management has become a significant challenge because of its inherent hierarchical, distributed, and heterogeneous nature. Various cloud-based network services such as crowd sensing, hierarchical deep learning systems, and cloud gaming each have their own traffic patterns and computing requirements. To provide a satisfactory user experience for these services, resource management that comprehensively considers service diversity, client usage patterns, and network performance indicators is required. In this study, an algorithm that simultaneously considers computing resources and network traffic load when deploying servers that provide edge services is proposed. The proposed algorithm generates candidate deployments based on factors that affect traffic load, such as the number of servers, server location, and client mapping according to service characteristics and usage. A final deployment plan is then established using a partial vector bin packing scheme that considers both the generated traffic and computing resources in the network. The proposed algorithm is evaluated using several simulations that consider actual network service and device characteristics.

中文翻译：

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渐进的面向流量的资源管理，以减少边缘计算中的网络拥塞

边缘计算可以通过在网络边缘提供云服务来提供低延迟和实时处理的网络服务。边缘计算具有许多优势，例如低延迟，本地性和网络流量分配，但是相关的资源管理由于其固有的分层，分布式和异构性质而已成为一项重大挑战。各种基于云的网络服务（例如人群感应，分层深度学习系统和云游戏）均具有自己的流量模式和计算要求。为了为这些服务提供令人满意的用户体验，需要全面考虑服务多样性，客户端使用模式和网络性能指标的资源管理。在这项研究中，提出了一种在部署提供边缘服务的服务器时同时考虑计算资源和网络流量负载的算法。所提出的算法根据影响流量负载的因素（例如服务器数量，服务器位置以及根据服务特征和用途的客户端映射）生成候选部署。然后，使用考虑了网络中生成的流量和计算资源的部分向量bin打包方案，建立了最终的部署计划。使用考虑实际网络服务和设备特征的几种模拟对提出的算法进行评估。例如服务器的数量，服务器的位置以及根据服务特征和使用情况进行的客户端映射。然后，使用考虑了网络中生成的流量和计算资源的部分向量bin打包方案，建立了最终的部署计划。使用考虑实际网络服务和设备特征的几种模拟对提出的算法进行评估。例如服务器的数量，服务器的位置以及根据服务特征和使用情况进行的客户端映射。然后，使用考虑了网络中生成的流量和计算资源的部分向量bin打包方案，建立了最终的部署计划。使用考虑实际网络服务和设备特征的几种模拟对提出的算法进行评估。