English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In the management of computer network systems, the service function chaining(SFC) modules play an important role by generating efficient paths for networktraffic through physical servers with virtualized network functions (VNF). Toprovide the highest quality of services, the SFC module should generate a validpath quickly even in various network topology situations including dynamic VNFresources, various requests, and changes of topologies. The previous supervisedlearning method demonstrated that the network features can be represented bygraph neural networks (GNNs) for the SFC task. However, the performance waslimited to only the fixed topology with labeled data. In this paper, we applyreinforcement learning methods for training models on various networktopologies with unlabeled data. In the experiments, compared to the previoussupervised learning method, the proposed methods demonstrated remarkableflexibility in new topologies without re-designing and re-training, whilepreserving a similar level of performance.

Reinforcement Learning of Graph Neural Networks for Service Function Chaining