Abstract
Edge computing can provide network services with low latency and real-time processing by operating cloud services on network edges. Edge computing has numerous advantages such as low latency, local characteristics, and network congestion localization, however, associated resource management is becoming a significant challenge because of its features such as hierarchy, decentralization, and heterogeneity. Thus, a joint resource operation and management scheme for edge computing that can greatly reduce the traffic load in the network by considering the generated traffic load and computing resources is proposed in this paper. It is based on the concept of a virtual service flow (VSF) consisting of clients, data sources, a server instance, and external network entities for a specific service. The VSF consists of several drafts according to the expected server location, and each draft estimates a traffic load that can occur according to its characteristics. The location of the edge server of the VSF is determined for each VSF, and it is performed based on the VSF rejection and reconfiguration algorithm using the weighted vector bin packing algorithm that considers the client node coverage of the VSF. The proposed scheme is evaluated based on simulations that consider the actual characteristics of the network services and devices.
Highlights
Edge computing is a new computing model presented to address various issues such as high latency that can occur when current network services operate on cloud computing [1], [2]
We evaluate the effectiveness of the proposed scheme from various perspectives through a comparison with existing methods using simulation
The computing resource load and traffic load according to client usage are determined within a specific range, and this is information that can be empirically secured by system monitoring
Summary
The proposed scheme creates a virtual service flow (VSF) and configures a server by estimating the traffic generated by each flow, and deploys the servers to a network node in consideration of computing resources. Through this scheme, it is possible to flexibly respond to traffic inefficiency that may occur when edge computing is applied in various network situations such as various types of services, traffic patterns, and physical resource status. The proposed scheme has the following contributions. 1) It contributes to a server placement technique that considers computing resources and network traffic together based on the usage of clients in the network to which edge computing is applied. 2) It is possible to increase the utilization of computing resources of edge devices by using a vector bin packing algorithm that considers multiple resources for server placement. 3) By estimating the generated traffic load before server placement, it is possible to provide a quick service response time and reduce network traffic. 4) This leads to a flexible traffic management environment by inducing the service administrator to configure multiple edge servers to improve the response time of the service within the same network
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