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Traditional highly-centralized mobile core networks (e.g., Evolved Packet Core (EPC)) need to be constantly upgraded both in their network functions and backhaul links, to meet increasing traffic demands. Network Function Virtualization (NFV) is being investigated as a potential cost-effective solution for this upgrade. A virtual mobile core (here,...
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Context 1
... Packet Core (EPC) is the end-to-end IP-based mobile core network infrastructure. Fig. 1 shows an EPC, its functional entities and interfaces. Delineation between control and data planes is an important aspect of EPC and will be discussed further in the paper. Mobility Management Element (MME), Policy and Charging Rules Function (PCRF), and Home Subscriber Server (HSS) are EPC control plane elements. Serving Gateway (SGW) ...
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... control-plane latency requirements for each NAS procedure depends on whether a default or dedicated bearer is setup which is used for upload and download. The UE upload traffic reaches eNodeB and is directed to SGW. SGW is the mobility anchor for eNodeB's, and hence, upload traffic has to traverse SGW first, then PGW to reach Internet as shown in Fig. 1. If a UE downloads data, SGW and PGW are traversed in reverse order. Fig. 2(c) shows the Data Service Chains (DSCs) for upload and ...
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Citations
... The constraint (17) ensures that all the paths passing on a node for each wavelength do not exceed the number of active fibers present in the optical link. (18) ensures that we have at least one physical path for each optical tunnel of the IP/MPLS layer, while (19) and (20) Number of active lightpaths between (û,v) Number of active lightpaths between (û,v) on wavelength w of chain r Fraction of the capacity used by chain r in lightpaths between (û,v) on wavelength w. ...
With the advent of 5G technology, we are witnessing the development of increasingly bandwidth-hungry network applications, such as enhanced mobile broadband, massive machine-type communications and ultra-reliable low-latency communications. Software Defined Networking (SDN), Network Function Virtualization (NFV) and Network Slicing (NS) are gaining momentum not only in research but also in IT industry representing the drivers of 5G. NS is an approach to network operations allowing the partition of a physical topology into multiple independent virtual networks, called network slices (or slices). Within a single slice, a set of Service Function Chains (SFCs) is defined and the network resources, e.g. bandwidth, can be provisioned dynamically on demand according to specific Quality of Service (QoS) and Service Level Agreement (SLA) requirements. Traditional schemes for network resources provisioning based on static policies may lead to poor resource utilization and suffer from scalability issues. In this article, we investigate the application of Reinforcement Learning (RL) for performing dynamic SFC resources allocation in NFV-SDN enabled metro-core optical networks. RL allows to build a self-learning system able to solve highly complex problems by employing RL agents to learn policies from an evolving network environment. In particular, we build an RL system able to optimize the resources allocation of SFCs in a multi-layer network (packet over flexi-grid optical layer). The RL agent decides if and when to reconfigure the SFCs, given state of the network and historical traffic traces. Numerical simulations show significant advantages of our RL-based optimization over rule-based optimization design.
To realize mobile virtual reality (VR) group gaming services which are currently hampered by the prohibitive bandwidth and the stringent delay requirements, we investigate the problem of provisioning such services using the emerging mobile edge cloudlet (MEC) networks with a distributed content rendering architecture. The underlying dynamic rendering-module placement problem requires to optimize the service’s operational cost and the users’ end-to-end performance, involving multiple intertwined conflicting system objectives that are discrete, nonconvex, and higher degree polynomial functions with coupled decisions and arbitrary user dynamics over time. We solve this online placement problem by leveraging model predictive control (MPC) and overcoming the aforementioned challenges over each prediction window. We explore the connection between the placement problem and the minimal
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cut problem in graph theory and solve the former via solving a series of instances of the latter. We formally prove the performance guarantee of our approach. We also conduct extensive trace-driven evaluations and demonstrate the superior practical performance of our MPC-based approach compared to the
de facto
practices and the state-of-the-art alternatives.
