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/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

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

Zendy Tools

Zendy Open

Effective network slicing requires an infrastructure/network provider to dealwith the uncertain demand and real-time dynamics of network resource requests.Another challenge is the combinatorial optimization of numerous resources,e.g., radio, computing, and storage. This article develops an optimal and fastreal-time resource slicing framework that maximizes the long-term return of thenetwork provider while taking into account the uncertainty of resource demandfrom tenants. Specifically, we first propose a novel system model which enablesthe network provider to effectively slice various types of resources todifferent classes of users under separate virtual slices. We then capture thereal-time arrival of slice requests by a semi-Markov decision process. Toobtain the optimal resource allocation policy under the dynamics of slicingrequests, e.g., uncertain service time and resource demands, a Q-learningalgorithm is often adopted in the literature. However, such an algorithm isnotorious for its slow convergence, especially for problems with largestate/action spaces. This makes Q-learning practically inapplicable to our casein which multiple resources are simultaneously optimized. To tackle it, wepropose a novel network slicing approach with an advanced deep learningarchitecture, called deep dueling that attains the optimal average reward muchfaster than the conventional Q-learning algorithm. This property is especiallydesirable to cope with real-time resource requests and the dynamic demands ofusers. Extensive simulations show that the proposed framework yields up to 40%higher long-term average return while being few thousand times faster, comparedwith state of the art network slicing approaches.

Optimal and Fast Real-time Resources Slicing with Deep Dueling Neural Networks