Abstract
With the rapid development of the Internet of Things, a large number of smart devices are being connected to the Internet while the data generated by these devices have put unprecedented pressure on existing network bandwidth and service operations. Edge computing, as a new paradigm, places servers at the edge of the network, effectively relieving bandwidth pressure and reducing delay caused by long-distance transmission. However, considering the high cost of deploying edge servers, as well as the waste of resources caused by the placement of idle servers or the degradation of service quality caused by resource conflicts, the placement strategy of edge servers has become a research hot spot. To solve this problem, an edge server placement method orienting service offloading in IoT called EPMOSO is proposed. In this method, Genetic Algorithm and Particle Swarm Optimization are combined to obtain a set of edge server placements strategies, and Simple Additive Weighting Method is utilized to determine the most balanced edge server placement, which is measured by minimum delay and energy consumption while achieving the load balance of edge servers. Multiple experiments are carried out, and results show that EPMOSO fulfills the multiobjective optimization with an acceptable convergence speed.
Highlights
Internet of ings (IoT) is a network that connects any object to the Internet through sensors to realize intelligent identification, tracking, and control
As a strategy to alleviate the pressure on computing resources, cloud computing is introduced into the IoT [6,7,8]. e data collected by sensors or computing-intensive tasks from smart devices are transmitted to a cloud platform with powerful storage and computing capabilities, where the computation results can be stored in the cloud for subsequent operations [9, 10]
The main contributions can be concluded as follows: (i) We propose an edge server placement method named EPMOSO in which Genetic Algorithm and Particle Swarm Optimization are effectively combined to obtain a set of placement strategies, and most balanced edge server placement considering delay and energy consumption is determined by Simple Additive Weighting Method
Summary
Internet of ings (IoT) is a network that connects any object to the Internet through sensors to realize intelligent identification, tracking, and control. Considering long distance between sensors and cloud platform, the transmission delay is unacceptable in some services, like the real-time identify, track, and control [5, 11]. To reduce the transmission delay and improve the quality of service and user experience, edge computing is introduced to reduce the delay and realize real-time control with edge servers, which are closer to user devices. Erefore, it is necessary to design an efficient edge server layout strategy to ensure the quality of edge services [16]. Considering the above requirements, it is a challenge to find an edge server layout strategy that can realize real-time control and guarantee the overall edge service quality.
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