A QoE and Availability-Aware Framework for Network Slice Placement and Resource Allocation

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.

The rapid development of smart grid has put forward higher requirements on communication technology. With the rapid development of power wireless communication network, the demand of power business becomes more and more complicated and diverse. However, the current single-standard network has poor coverage, poor power service carrying capacity, and weak transmission stability and security. To address the above problems, this paper explores the network slicing technology for intelligent power distribution services. Based on the analysis of existing network access selection methods, a network selection strategy for quality of service (QoS) assurance of power services is proposed for the characteristics of power distribution services. Combining its own QoS requirements and the characteristics of the network to be accessed, the power service can realize the access selection of the optimal adaptation network. In order to better meet various vertical industry applications, network slicing is required in both the core network and the radio access network, i.e., end-to-end network slicing is realized, and network slicing can be customized to tailor network functions and reasonably allocate network resources according to service scenario requirements. After receiving a request for network slicing, operators need to map the virtual network functions required for network slicing to the underlying physical network, a process that involves the optimal allocation of physical resources.

With the advent of 5G communication networks, many novel areas of research have emerged and the spectrum of communicating objects has been diversified. Network Function Virtualization (NFV), and Software Defined Networking (SDN), are the two broader areas that are tremendously being explored to optimize the network performance parameters. Cellular Vehicle-to-Everything (C-V2X) is one such example of where end-to-end communication is developed with the aid of intervening network slices. Adoption of these technologies enables a shift towards Ultra-Reliable Low-Latency Communication (URLLC) across various domains including autonomous vehicles that demand a hundred percent Quality of Service (QoS) and extremely low latency rates. Due to the limitation of resources to ensure such communication requirements, telecom operators are profoundly researching software solutions for network resource allocation optimally. The concept of Network Slicing (NS) emerged from such end-to-end network resource allocation where connecting devices are routed toward the suitable resources to meet their requirements. Nevertheless, the bias, in terms of finding the best slice, observed in the network slices renders a non-optimal distribution of resources. To cater to such issues, a Deep Learning approach has been developed in this paper. The incoming traffic has been allocated network slices based on data-driven decisions as well as predictive network analysis for the future. A Long Short Term Memory (LSTM) time series prediction approach has been adopted that renders optimal resource utilization, lower latency rates, and high reliability across the network. The model will further ensure packet prioritization and will retain resource margin for crucial ones.

Network slicing is a promising approach to meet the diverse requirements of the various use cases in the 5G networks. Hence, the mobile operators are moving forward to leveraging network slicing in order to measure up with the individual service expectations in their networks. Deploying different network slice types requires the global view of the network and the automated orchestration and management of the underlying resources. This is facilitated by utilizing software-defined networking and network function virtualization as the 5G key-enabler technologies. In this paper, we propose a practical network slicing resource management scheme which is comprised of a dynamic, priority-based resource allocation cooperating with an admission control unit. Adopting the proposed dynamic resource allocation would allow the admission control to comply with more NS requests while ensuring the desired requirements of the existing network slices. To validate the effectiveness of such a mechanism in a real environment, we take advantage of the features provided by OpenAirInterface and FlexRAN to efficiently manage multiple isolated network slices. In particular, we evaluate the significance of the network slicing, the isolation degree among created slices, and the effectiveness of the proposed scheme through several practical scenarios.

5G networks need to support various use cases which may require different quality of service (QoS). Network slicing (NS) is an innovative concept to customize different logical networks from a common general infrastructure for these services. A slice is composed of a set of virtual network functions (VNFs) that can be deployed on standard commodity servers as virtual machines (VMs). However, the packet delay of VMs may be affected by other VMs on the same server. In this paper, we investigate network slice deployment policy to improve the network performance by guaranteeing the latency requirements. Moreover, a realistic mathematical model is formulated, considering the effects of virtualization on end-to-end delay. Due to the diversity of network slices' topologies and various performance requirements, we propose a heuristic algorithm to deploy different network slices. Extensive experiments demonstrate that our algorithm can get solutions with reasonable execution time and achieve lower delay and higher acceptance ratio.

Network slices are counted as key technology enablers to provide tailored services and isolation for different Fifth Generation (5G) cellular network application instances. A network slice is an independent end-to-end logical network over a shared physical substrate network, capable of providing a specific set of negotiated services to the customers, quite analogous to virtual machines offered in the domain of cloud computing. In this paper, we perform a systematic study of allocation and dynamic on-demand provisioning of network resources for the slices. The core parameters of Quality of Experience (QoE) to end-user systems, Network performance, and Operating efficiency are carefully applied while placing network virtual functions and determining the nodes, links, and resources for assignment to these slices. Network slices are expected to provide end-to-end capabilities while providing customized offerings. Slices can be further divided into various categories such as RAN, Transport and Core network slices. Our work examines the allocation of the above categories as independent sub-problems and latter amalgamate it into an end-to-end multi-objective constrained optimization problem. Our proposed approach is influenced by Multiple Attribute Decision Making, Analytical Hierarchy Processing for slice assignment and enhanced Dinic's Maximum Flow Method to find all possible virtual paths for allocations. Simulations are carried out through NS3 and results are compared against well-known algorithms.

End-to-end network slicing in unmanned aerial vehicle (UAV) networks has attracted significant attention for delivering differentiated services tailored to the quality of service (QoS) requirements of user terminals (UTs). In a sliced UAV network, the dual mobility of both UTs and UAVs, combined with the three-layer association of UT-UAV-network slice (NS), makes handoff a critical and challenging problem for maintaining the communication performance of UTs. This paper proposes a sequential handoff process where a central controller makes handoff decisions for each UT sequentially according to the UT queue. This process is modeled as a Markov decision process (MDP). In the MDP, the state space includes NS available resources, UTs' QoS requirements, and UT queue information. Additionally, the reward function accounts for UTs' QoS and handoff overhead. The double deep Q-network (DDQN) technique is then employed to design a handoff algorithm. This algorithm achieves the optimization goal of ensuring UTs' QoS requirements while avoiding frequent handoffs by maximizing the total reward of the MDP. Additionally, a state-dependent exploration technique with adaptive intensity adjustment, called noisy network (NoisyNet), is utilized to drive the DDQN for effective exploration, thereby enhancing the performance of the handoff algorithm. Finally, the runtime complexity of the proposed NoisyNet-DDQN-based sequential handoff (NoisyNet-DDQN-SH) algorithm is analyzed. The simulation results show that the NoisyNet-DDQN-SH algorithm converges well. Moreover, it exhibits strong robustness. Additionally, the NoisyNet technique effectively enhances the algorithm's performance, making the NoisyNet-DDQN-SH algorithm superior to the vanilla DDQN (with an ϵ-greedy strategy), greedy, maximum signal-to-interference-plus-noise ratio (MaxSINR), and random algorithms.

This paper investigates the network slicing technique based resource allocation in small-cell networks. Motivating to maximize the sum rate of the network, it considers the joint design of network slicing (NS), subcarrier allocation and power allocation, optimization problem is formulated as a mixed integer non-linear programing (MINLP) problem. To tackle the formulated MINLP problem, we propose the low-complexity resource allocation scheme, including the heuristic NS assignment algorithm, and the many-to-one matching sub-carrier allocation (M2OM-SA) algorithm. In particular, the heuristic NS algorithm is first carried out, in order to efficiently assign the different number of subbands to different types of users with diverse service requirements. Then, based on the NS result, the M2OM-SA algorithm is implemented independently for each NS group, in which the best available subcarriers can be assigned to the users within the same NS group. Finally, our simulation results show that the proposed resource allocation scheme can significantly outperform the existing schemes in terms of the sum rate of the networks. Therefore, we can conclude that the proposed resource allocation scheme can be considered as a promising candidate for future small-cell networks with diverse service requirements coexist.

In this chapter, we present a comprehensive end-to-end (E2E) network slicing framework for fifth-generation (5G) wireless and core networks and provide our vision on network slicing for beyond 5G (B5G) networks using artificial intelligence (AI) technologies. We first give an introduction on the 5G communication networks by specifying distinct 5G networking characteristics. As the key features of 5G pose challenges on quality-of-service (QoS)-oriented resource provisioning, a new concept of network slicing is presented, based on software-defined networking (SDN) and network function virtualization (NFV) technologies, to enhance the overall resource utilization while guaranteeing QoS isolation among services. We focus on developing specific network slicing solutions for both 5G wireless networks and core networks. In addition, the evolution of B5G networks is discussed in terms of a potential networking paradigm shift with new service key performance indicators (KPIs). To satisfy B5G service requirements, a framework of AI-assisted network slicing lifecycle is developed to automate the slice creation with reduced slice management complexity. A case study is presented to demonstrate the effectiveness of the proposed E2E network slicing solutions.Keywords5G communication networksNetwork slicingWireless networksRadio resource slicingCore networksComputing and transmission resource slicingBeyond 5G networksAI-assisted network slicingQoSE2E delay

Multi-access Edge Computing fundamentals, services, enablers and challenges: A complete survey

Network function virtualization (NFV) technology is widely used in network slicing in 5G networks and traffic processing in wide area network. However, with the increasing of service requests during the life cycle of a virtual network function (VNF), how to flexibly deploy the VNF becomes a key problem to make maximum use of the limited capacity of the physical network resources, and meanwhile satisfy the requirements of quality of service (QoS) in NFV scenario. In this paper, aiming to solve whether and how to scale VNF on demand, we formulated this problem as a non-convex linear mathematical optimization model where the optimization goal is to minimize the delay and energy consumption of the service function chain (SFC). Specifically, we propose a VNF flexible deployment scheme based on Reinforcement Learning (RL). Moreover, we train the agent by interacting with the physical network environment and take action according to the state of physical node to find the optimal physical resource allocation strategy of the VNF scaling. In addition, the state space, action space and the reward function are defined as available resource, migration or scaling decision and the reciprocal of total cost respectively. Extensive simulation results demonstrate that the proposed algorithm outperforms the comparison algorithm in terms of reducing the delay and increasing the ratio of successful scaling request.

In this paper, we consider the network slicing () problem which aims to map multiple customized virtual network requests (also called services) to a common shared network infrastructure and manage network resources to meet diverse quality of service (QoS) requirements. We propose a mixed-integer nonlinear programming (MINLP) formulation for the considered NS problem that can flexibly route the traffic flow of the services on multiple paths and provide end-to-end delay and reliability guarantees for all services. To overcome the computational difficulty due to the intrinsic nonlinearity in the MINLP formulation, we transform the formulation into an equivalent mixed-integer linear programming () formulation and further show that their continuous relaxations are equivalent. In sharp contrast to the continuous relaxation of the formulation which is a nonconvex nonlinear programming problem, the continuous relaxation of the formulation is a polynomial-time solvable linear programming problem, which significantly facilitates the algorithmic design. Based on the newly proposed formulation, we develop a customized column generation () algorithm for solving the problem. The proposed algorithm is a decomposition-based algorithm and is particularly suitable for solving large-scale problems. Numerical results demonstrate the efficacy of the proposed formulations and the proposed algorithm.

Network Slicing (NS) technique is comprehensively reshaping the next-generation communication networks (e.g. 5G, 6G). Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) predominantly control the flow of service functions on NS to incorporate versatile applications as per user demands. In the virtualized-Software Defined Networking vSDN environment, a chain of well-defined virtual network functions (VNFs) are installed on Service Function Chains (SFCs) by multiple Internet Service Providers (ISPs) concurrently. Generation, allocation, re-allocation, release and destroying associative VNFs on SFC is an extremely difficult task while keeping high selection accuracy. Towards solving this fundamental issue, in this work, we have proposed a multi-layered SFC formation for adaptive VNF allocation on dynamic slices. We have formulated an ILP to address the VNF-EAP (VNF-Embedding and Allocation Problem) over real network topology (AT&T Topology). Leveraging machine learning techniques we have shown an intelligent VNF selection mechanism to optimize resource utilization. The performance evaluation shows remarkable efficiency on ML-driven dynamic VNF selections over static allocations on SFCs by halving resource usage. Further, we have also studied a VNF typecasting technique for service backup on outage slices in the field of disaster management activities.

Network slicing is a key technology in fifth-generation (5G) mobile networks. Slicing divides a physical network into multiple dedicated logical networks to meet the requirements of diverse use cases. Efficient slice deployment algorithms are critical in reducing network operators' costs and energy consumption and in providing users better service. Many researchers have focused on static deployment when investigating network slices, effectively ignoring network operators' requirements for the dynamic deployment and expansion of such slices. In this paper, we first construct a joint optimization problem of cost and energy consumption. Then, we propose a prediction-assisted adaptive network slice expansion algorithm to deploy network slices dynamically. The proposed algorithm consists of three parts. First, we devise a Holt-Winters (HW) prediction algorithm to determine traffic demand for network slices. This method is intended to avoid frequent changes in network topology. Second, we propose a virtual network function (VNF) adaptive scaling strategy to reasonably determine the number of VNFs and resources required for network slices to avoid resource wastage. Finally, we develop a proactive online algorithm to deploy network slices. This method deploys network slices reasonably via the VNF deployment algorithm and link-routing algorithm to ensure slices' service requirements. Resource capacity and delay requirements are also considered in our evaluation to ensure that network costs and energy consumption are minimized. We then perform a series of simulation experiments to compare the proposed method's performance to state-of-the-art dynamic network slicing technologies. Ultimately, our solution is deemed a suitable candidate for dynamic deployment of 5G network slices; the solution demonstrates advantages of high resource utilization, low deployment costs, and low energy consumption.

With the advent of 5G era, network slicing has received a great deal of attention as a means to support a variety of wireless services in a flexible manner. Network slicing is a technique to divide a single physical resource network into multiple slices supporting independent services. In beyond 5G (B5G) systems, the main goal of network slicing is to assign the physical resource blocks (RBs) such that the quality of service (QoS) requirements of eMBB, URLLC, and mMTC services are satisfied. Since the goal of each service category is dearly distinct and the computational burden caused by the increased number of time slots is huge, it is in general very difficult to assign RB properly. In this paper, we propose a deep reinforcement learning (DRL)-based network slicing technique to find out the resource allocation policy maximizing the long-term throughput while satisfying the QoS requirements in the B5G systems. Key ingredient of the proposed technique is to reduce the action space by eliminating undesirable actions that cannot satisfy the QoS requirements. Numerical results demonstrate that the proposed technique is effective in maximizing the long-term throughput and handling the coexistence of use cases in the B5G environments.