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In edge computing, application components can be placed over a range of computational devices from cloud data centers to nodes at the network edge. Application placement can have significant impact on important metrics like latency and resource utilization. Thus, application placement is an important optimization problem. In edge computing, the characteristics of both the infrastructure and the application may change over time, which may require the dynamic re-optimization of the application placement. Most algorithms suggested so far for the dynamic re-optimization of edge application placement are centralized, i.e., they rely on one entity collecting information from the whole infrastructure and making decisions centrally. However, centralized approaches suffer from limited scalability and are vulnerable to failures. In this paper, we present a decentralized approach for the dynamic re-optimization of edge application placement. We adopt an algorithm of Malek et al. for distributed systems and modify it to make it applicable to edge computing. In this approach, each node makes decisions autonomously, using auctions for coordination. Our empirical results demonstrate that the proposed algorithm is very effective in optimizing edge application placement. In an edge system with 637 edge nodes and 563 end devices, our algorithm achieves 54% higher reduction of application latency than a previous decentralized algorithm. • Optimized placement of application components on cloud and edge devices. • Decentralized optimization algorithm using local knowledge and local modifications. • Auctions and distributed locking for synchronization. • Minimization of application latency. [ABSTRACT FROM AUTHOR]

• The combined fronthaul network improves the quality of service by the edge computing. • The correction factor meets the physical constraints in network planning. • The performance of the network can be greatly improved by the proposed algorithm. The rapid development of time-sensitive applications, such as autopilot, telemedicine and industrial automation, demands more stringent requirements on end-to-end delay. Therefore, low-delay planning of 5G networks is supposed to be a crucial stage. In this paper, a low-delay layout planning based on the nonlinear decreasing inertia weight particle swarm optimization algorithm (NL-wPSO) is demonstrated. By iteratively calculating the velocity and position of each particle, the proposed algorithm can optimize the layout of AAUs in the fronthaul network. Thereby, the distance between the AAUs and edge computing server will be shortened. Compared to the basic PSO, the simulation results indicate a significant reduction on transmission delay. [ABSTRACT FROM AUTHOR]