Modeling Profit of Sliced 5G Networks for Advanced Network Resource Management and Slice Implementation

The core innovation in future 5G cellular networksnetwork slicing, aims at providing a flexible and efficient framework of network organization and resource management. The revolutionary network architecture based on slices, makes most of the current network cost models obsolete, as they estimate the expenditures in a static manner. In this paper, a novel methodology is proposed, in which a value chain in sliced networks is presented. Based on the proposed value chain, the profits generated by different slices are analyzed, and the task of network resource management is modeled as a multiobjective optimization problem. Setting strong assumptions, this optimization problem is analyzed starting from a simple ideal scenario. By removing the assumptions step-by-step, realistic but complex use cases are approached. Through this progressive analysis, technical challenges in slice implementation and network optimization are investigated under different scenarios. For each challenge, some potentially available solutions are suggested, and likely applications are also discussed

Resource allocation for network slicing in mobile networks

This paper provides a survey of resource allocation for network slicing. We focus on two classes of existing solutions: (i) reservation-based approaches, which allocate resources on a reservation basis, and (ii) share-based approaches, which allocate resources based on static overall shares associated to individual slices. We identify the requirements that a slice-based resource allocation mechanism should satisfy, and evaluate the performance of both approaches against these requirements. Our analysis reveals that reservation-based approaches provide a better level of isolation as well as stricter guarantees, by enabling tenants to explicitly reserve resources, but one must pay a price in terms of efficiency unless reservations can be updated very dynamically; in particular, efficiency falls below 50\% when reservations are performed over long timescales. We provide further comparisons in terms of customizability, complexity, privacy and cost predictability, and discuss which approach might be more suitable depending on the network slices' characteristics. We also describe the additional mechanisms required to implement the desired resource allocations while meeting the latency and reliability requirements of the different slice types, and outline some issues for future work.The work of Albert Banchs was supported in part by the H2020 5G-TOURS European project under Grant 856950, and in part by the Spanish State Research Agency (TRUE5G project) under Grant PID2019-108713RB-C52/AEI/10.13039/501100011033. The work of Gustavo de Veciana was supported by NSF Grant CNS-1910112

A Resource Sharing Method for Reliable Slice as a Service Provisioning in 5G Metro Networks

This paper proposes a dynamic slice provisioning analysis in a 5G metro network with reliability guarantees and possible sharing of backup resources. Performance of dedicated (DP) and shared (SP) protection solutions are evaluated with respect to slice resource allocation (i.e., bandwidth and processing units). The main results show a remarkable saving, in terms of slice acceptance rate, by applying SP solutions with respect to conventional DP ones

Be Scalable and Rescue My Slices During Reconfiguration

International audienceModern 5G networks promise more bandwidth, less delay, and more flexibility for an ever increasing number of users and applications, with Software Defined Networking, Network Function Virtualization, and Network Slicing as key enablers. Within that context, efficiently provisioning network and cloud resources of a wide variety of applications with dynamic users' demands is a real challenge. In this work, we consider the problem of network slice reconfiguration. Reconfiguring from time to time network slices allows to reduce the network operational costs and to increase the number of slices that can be managed within the network. However, it impacts users' Quality of Service during the reconfiguration step. To solve this issue, we study solutions implementing a make-before-break scheme. We propose new models and scalable algorithms (relying on column generation techniques) that solve large data instances in few seconds

Network intelligence in 6G: challenges and opportunities

Proceeding of: the 16th ACM Workshop on Mobility in the Evolving Internet Architecture (in conjunction with MobiCom 2021: The 27th Annual International Conference On Mobile Computing And Networking, January 31-February 04, 2022, New Orleans, United States)The success of the upcoming 6G systems will largely depend on the quality of the Network Intelligence (NI) that will fully automate network management. Artificial Intelligence (AI) models are commonly regarded as the cornerstone for NI design, as they have proven extremely successful at solving hard problems that require inferring complex relationships from entangled, massive (network traffic) data. However, the common approach of plugging "vanilla" AI models into controllers and orchestrators does not fulfil the potential of the technology. Instead, AI models should be tailored to the specific network level and respond to the specific needs of network functions, eventually coordinated by an end-to-end NI-native architecture for 6G. In this paper, we discuss these challenges and provide results for a candidate NI-driven functionality that is properly integrated into the proposed architecture: network capacity forecasting.The authors of this paper have received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101017109 (DAEMON Network intelligence for aDAptive and sElf-Learning MObile Networks). This paper is also funded by the Spanish State Research Agency (TRUE5G project, PID2019-108713RB-C52PID2019-108713RB-C52 /AEI / 10.13039/501100011033)Publicad

Network slice reconfiguration by exploiting deep reinforcement learning with large action space

It is widely acknowledged that network slicing can tackle the diverse usage scenarios and connectivity services that the 5G-and-beyond system needs to support. To guarantee performance isolation while maximizing network resource utilization under dynamic traffic load, network slice needs to be reconfigured adaptively. However, it is commonly believed that the fine-grained resource reconfiguration problem is intractable due to the extremely high computational complexity caused by numerous variables. In this paper, we investigate the reconfiguration within a core network slice with aim of minimizing long-term resource consumption by exploiting Deep Reinforcement Learning (DRL). This problem is also intractable by using conventional Deep Q Network (DQN), as it has a multi-dimensional discrete action space which is difficult to explore efficiently. To address the curse of dimensionality, we propose a discrete Branching Dueling Q-network (discrete BDQ) by incorporating the action branching architecture into DQN, for drastically decreasing the number of estimated actions. Based on the discrete BDQ network, we develop an intelligent network slice reconfiguration algorithm (INSRA). Extensive simulation experiments are conducted to evaluate the performance of INSRA and the numerical results reveal that INSRA can minimize the long-term resource consumption and achieve high resource efficiency compared with several benchmark algorithms

Resource Calendaring for Mobile Edge Computing in 5G Networks

Mobile Edge Computing (MEC) is a key technology for the deployment of next generation (5G and beyond) mobile networks, specifically for reducing the latency experienced by mobile users which require ultra-low latency, high bandwidth, as well as real-time access to the radio network. In this paper, we propose an optimization framework that considers several key aspects of the resource allocation problem for MEC, by carefully modeling and optimizing the allocation of network resources including computation and storage capacity available on network nodes as well as link capacity. Specifically, both an exact optimization model and an effective heuristic are provided, jointly optimizing (1) the connections admission decision (2) their scheduling, also called calendaring (3) and routing as well as (4) the decision of which nodes will serve such connections and (5) the amount of processing and storage capacity reserved on the chosen nodes. Numerical experiments are conducted in several real-size network scenarios, which demonstrate that the heuristic performs close to the optimum in all the considered network scenarios, while exhibiting a low computing time

Reconfiguring Network Slices at the Best Time With Deep Reinforcement Learning

International audienceThe emerging 5G induces a great diversity of use cases, a multiplication of the number of connections, an increase in throughput as well as stronger constraints in terms of quality of service such as low latency and isolation of requests. To support these new constraints, Network Function Virtualization (NFV) and Software Defined Network (SDN) technologies have been coupled to introduce the network slicing paradigm. Due to the high dynamicity of the demands, it is crucial to regularly reconfigure the network slices in order to maintain an efficient provisioning of the network. A major concern is to find the best frequency to carry out these reconfigurations, as there is a tradeoff between a reduced network congestion and the additional costs induced by the reconfiguration. In this paper, we tackle the problem of deciding the best moment to reconfigure by taking into account this trade-off. By coupling Deep Reinforcement Learning for decision and a Column Generation algorithm to compute the reconfiguration, we propose Deep-REC and show that choosing the best time during the day to reconfigure allows to maximize the profit of the network operator while minimizing the use of network resources and the congestion of the network. Moreover, by selecting the best moment to reconfigure, our approach allows to decrease the number of needed reconfigurations compared to an algorithm doing periodic reconfigurations during the day