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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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Dynamic Bandwidth Allocation for PON Slicing with Performance-Guaranteed Online Convex Optimization
The emergence of diverse network applications demands more flexible and responsive resource allocation for networks. Network slicing is a key enabling technology that provides each network service with a tailored set of network resources to satisfy specific service requirements. The focus of this paper is the network slicing of access networks realized by Passive Optical Networks (PONs). This paper proposes a learning-based Dynamic Bandwidth Allocation (DBA) algorithm for PON access networks, considering slice-awareness, demand-responsiveness, and allocation fairness. Our online convex optimization-based algorithm learns the implicit traffic trend over time and determines the most robust window allocation that reduces the average latency. Our simulation results indicate that the proposed algorithm reduces the average latency by prioritizing delay-sensitive and heavily-loaded ONUs while guaranteeing a minimal window allocation to all ONUs.
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- Award ID(s):
- 2008856
- PAR ID:
- 10343819
- Date Published:
- Journal Name:
- 2021 IEEE Global Communications Conference (GLOBECOM)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- 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/GLOBECOM46510.2021.9685834
