Edge Cloud Server Deployment With Transmission Power Control Through Machine Learning for 6G Internet of Things
Edge Cloud Server Deployment With Transmission Power Control Through Machine Learning for 6G Internet of Things
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DOI：
10.1109/tetc.2019.2963091
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发表时间：
2021-10
影响因子：
5.9
通讯作者：
Tiago Koketsu Rodrigues;Katsuya Suto;N. Kato
中科院分区：
文献类型：
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作者：
Tiago Koketsu Rodrigues;Katsuya Suto;N. Kato
云计算是为客户端设备带来大量弹性资源的重要技术。长期以来，它们的主要缺点是用户和服务器之间的距离较远，但边缘云计算已经弥补了这一点，其中云服务器位于网络边缘。边缘云计算被认为是未来网络的关键，因此，有大量关于如何优化其操作的研究。然而，他们中的绝大多数人忽略了边缘服务器应该部署在哪里的决定，尽管这会严重影响系统的性能。此外，未来的网络还必须处理大量的客户端和服务器，例如物联网和6G网络的特征。这就需要可扩展的解决方案。基于这两点，我们提出了一种基于机器学习的6G物联网环境下的服务器部署策略。我们的解决方案被证明是可行的，同时接近最优。此外，我们还证明了我们的建议比传统的边缘云计算服务器部署解决方案具有更低的延迟和更高的资源效率。
Cloud computing is an important technology for bringing a big pool of elastic resources to client devices. Their main drawback has long been the long distance between users and servers, but this has been remedied by Edge Cloud Computing, where the cloud servers are located in the network edge. Edge Cloud Computing is regarded as essential for future networks and consequently, there is plenty of research on how to optimize its operation. However, the vast majority of them ignore the decision of where the edge servers should be deployed, despite how severely this can affect the performance of the system. Furthermore, future networks must also deal with massive amounts of clients and servers, such as the ones characteristic of the Internet of Things and 6G Networks. This demands solutions that are scalable. Given these two points, we propose a Machine Learning-based server deployment policy in 6G Internet of Things environments. Our solution is proven to approach optimality while being feasible. Furthermore, we also prove that our proposal leads to lower latency and higher resource efficiency than conventional Edge Cloud Computing server deployment solutions.