Next-generation optical metro-access networks are
expected to support end-to-end virtual network slices for critical
5G services. However, disasters affecting physical infrastructures
upon which network slices are mapped can cause significant
disruption in these services. Operators can deploy recovery units
or trucks to restore services based on slice requirements. In
this study, we investigate the problem of slice-aware service
restoration in metro-access networks with specialized recovery
trucks to restore services after a disaster failure. We model
the problem based on classical vehicle-routing problem to find
optimal routes for recovery trucks to failure sites to provide
temporary backup service until the network components are repaired.
Our proposed slice-aware service-restoration approach is
formulated as a mixed integer linear program with the objective
to minimize penalty of service disruption across different network
slices.We compare our slice-aware approach with a slice-unaware
approach and show that our proposed approach can achieve
significant reduction in service-disruption penalty
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DeepCare: Deep Learning-Based Smart Healthcare Framework using 5G Assisted Network Slicing
5G and beyond communication networks require satisfying very low latency standards, high reliability, high- speed user connectivity, more security, improved capacity and better service demands. Augmenting such a wide range of KPIs (Key Performance Indicators) needs a smart, intelligent and programmable solution for TSPs (Telecommunication Service Providers). Resource availability and quality sustainability are challenging parameters in a heterogeneous 5G environment. Programmable Dynamic Network Slicing (PDNS) is a key technology enabling parameter that can allow multiple tenants to bring their versatile applications simultaneously over shared physical infrastructure. Latest emerging technologies like virtualized Software- Defined Networks (vSDN) and Artificial Intelligence (AI) play a pivotal supporting role in solving the above-mentioned constraints. Using the PDNS framework, we have proposed a novel slice backup algorithm leveraging Deep Learning (DL) neural network to orchestrate network latency and load efficiently. Our model has been trained using the available KPIs and incoming traffic is analyzed. The proposed solution performs stable load balancing between shared slices even if certain extreme conditions (slice unavailability) through intelligent resource allocation. The framework withstands service outage and always select the most suitable slice as a backup. Our results show latency-aware resource distribution for better network stability.
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- Award ID(s):
- 2219741
- PAR ID:
- 10535370
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 978-1-6654-7340-8
- Page Range / eLocation ID:
- 201 to 206
- Format(s):
- Medium: X
- Location:
- Gandhinagar, Gujarat, India
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ANTS56424.2022.10227802
