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RAN Runtime Slicing System for Flexible and Dynamic Service Execution Environment
Abstract and Figures
Network slicing is one key enabler to provide the required flexibility and to realize the service-oriented 5G vision. Unlike the core network slicing, radio access network (RAN) slicing is still at its infancy and several works just start to investigate the challenges and potentials to enable the multi-service RAN, toward a serviced-oriented RAN (SO-RAN) architecture. One of the major concerns in RAN slicing is to provide different levels of isolation and sharing as per slice requirement. Moreover, both control and user plane processing may be customized allowing a slice owner to flexibly control its service. Enabling dynamic RAN composition with flexible functional split for disaggregated RAN deployments is another challenge. In this paper, we propose a RAN runtime slicing system through which the operation and behavior of the underlying RAN could be customized and controlled to meet slice requirements. We present a proof-of-concept prototype of the proposed RAN runtime slicing system for LTE, assess its feasibility and potentials, and demonstrate the isolation, sharing, and customization capabilities with three representative use cases.
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... The RAN runtime [114] allows to dynamically slice and customize a RAN to meet slice requirements, such as isolation, sharing, and customization options. The system fulfills RAN customization requirements by chaining RAN layers in a customized manner, but does not address micro-customizations for individual control plane logic and possible state conflicts between the slice-specific RAN layers. ...
... The RRC in the CU-CP hosts functionality for connection management, mobility, radio resource management, QoS, etc. The functionality can be divided into cell-common and user-specific functions [114]. Common functionality includes information not destined to individual users, such as cell information broadcast or emergency services. ...
5G networks are envisioned to be a paradigm shift towards service-oriented networks. In this thesis, we investigate how to efficiently combine slicing and SD-RAN to provide the required level of flexibility and programmability in the RAN infrastructure to realize service-oriented multi-tenant networks. First, we devise an abstraction of a base station to represent logical base stations and describe a virtualized network service. Second, we propose a novel standard-compliant SD-RAN platform, named FlexRIC, in the form of a software development kit (SDK). Third, we provide a modular design for a slice-aware MAC scheduling framework to efficiently manage and control the radio resources in a multi-service environment with quality-of-service (QoS) support. Finally, we present a dynamic SD-RAN virtualization layer based on the FlexRIC SDK and MAC scheduling framework to flexibly compose a multi-service SD-RAN infrastructure and provide programmability for multiple SD-RAN controllers.
... Although a variety of techniques have been used for resource management (e.g., queueing theory [17], Lagrange methods for optimization [12], [20], Thompson sampling [21], heuristic methods [22]), those based on artificial intelligence (AI) are currently the most widely used. Indeed, AI is considered a key architectural feature for providing resource elasticity in future networks [5]. ...
... For example, our method prescribes the amount of physical radio resources to be assigned to each slice, but does not arrange them within the timefrequency frame structure of the RF interface. For this task, the heuristic scheme proposed in [22] can be used. Our proposal can operate concurrently with an admission control scheme for on-demand incoming slices, such as those developed in [15], [16], and with a mechanism for the allocation of computational resources among slices [9], [26]. ...
Network slicing is a key feature of 5G and beyond networks, allowing the deployment of separate logical networks (network slices), sharing a common underlying physical infrastructure, and characterized by distinct descriptors and behaviors. The dynamic allocation of physical network resources among coexisting slices should address a challenging trade-off: to use resources efficiently while assigning each slice sufficient resources to meet its service level agreement (SLA). We consider the allocation of time-frequency resources from a new perspective: to design a control algorithm capable of learning over the operating network, while keeping the SLA violation rate under an acceptable level during the learning process. For this purpose, traditional model-free reinforcement learning (RL) methods present several drawbacks: low sample efficiency, extensive exploration of the policy space, and inability to discriminate between conflicting objectives, causing inefficient use of the resources and/or frequent SLA violations during the learning process. To overcome these limitations, we propose a model-based RL approach built upon a novel modeling strategy that comprises a kernel-based classifier and a self-assessment mechanism. In numerical experiments, our proposal, referred to as kernel-based RL, clearly outperforms state-of-the-art RL algorithms in terms of SLA fulfillment, resource efficiency, and computational overhead.
... The life cycle of a working slice instance and the proposed RAN subslicing are illustrated in Figure 2. Similar subslicing has been carried out in [23], where dynamic inter-slice radio resource partitioning in the time-frequency plane is proposed. The optimization goal is to find the largest unallocated space. ...
In 5G and beyond, the network slicing is a crucial feature that ensures the fulfillment of service requirements. Nevertheless, the impact of the number of slices and slice size on the radio access network (RAN) slice performance has not yet been studied. This research is needed to understand the effects of creating subslices on slice resources to serve slice users and how the performance of RAN slices is affected by the number and size of these subslices. A slice is divided into numbers of subslices of different sizes, and the slice performance is evaluated based on the slice bandwidth utilization and slice goodput. A proposed subslicing algorithm is compared with k-means UE clustering and equal UE grouping. The MATLAB simulation results show that subslicing can improve slice performance. If the slice contains all UEs with a good block error ratio (BLER), then a slice performance improvement of up to 37% can be achieved, and it comes more from the decrease in bandwidth utilization than the increase in goodput. If a slice contains UEs with a poor BLER, then the slice performance can be improved by up to 84%, and it comes only from the goodput increase. The most important criterion in subslicing is the minimum subslice size in terms of resource blocks (RB), which is 73 for a slice that contains all good-BLER UEs. If a slice contains UEs with poor BLER, then the subslice can be smaller.
... In [93], a RAN runtime framework for slice control and orchestration is proposed. Then, a detailed approach on radio resource slicing with different levels of isolation and sharing is described. ...
5G Radio Access Network (RAN) aims to evolve new technologies spanning the Cloud infrastructure, virtualization techniques and Software Defined Network capabilities. Advanced solutions are introduced to split the RAN functions between centralized and distributed locations to improve the RAN flexibility. However, one of the major concerns is to efficiently allocate RAN resources, while supporting heterogeneous 5G service requirements. In this thesis, we address the problematic of the user-centric RAN slice provisioning, within a Cloud RAN infrastructure enabling flexible functional splits. Our research aims to jointly meet the end users’ requirements, while minimizing the deployment cost. The problem is NP-hard. To overcome the great complexity involved, we propose a number of heuristic provisioning strategies and we tackle the problem on four stages. First, we propose a new implementation of a cost efficient C-RAN architecture, enabling on-demand deployment of RAN resources, denoted by AgilRAN. Second, we consider the network function placement sub-problem and propound a new scalable user-centric functional split selection strategy named SPLIT-HPSO. Third, we integrate the radio resource allocation scheme in the functional split selection optimization approach. To do so, we propose a new heuristic based on Swarm Particle Optimization and Dijkstra approaches, so called E2E-USA. In the fourth stage, we consider a deep learning based approach for user-centric RAN Slice Allocation scheme, so called DL-USA, to operate in real-time. The results obtained prove the efficiency of our proposed strategies.
... The main aim of RAN slicing is to enable dynamic on-demand allocation of radio resources among multiple services. A run time slicing method that isolates RAN slices and a set of algorithms in order to partition inter-slice resources are proposed in [34]. Another RAN slicing method allocates radio resources between enhanced mobile broadband and vehicle-to-everything services using RL combined with a heuristic algorithm to maximize utilization [35]. ...
One of the most difficult challenges in radio access network slicing occurs in the connection establishment phase where multiple devices use a common random access channel in order to gain access to the network. It is now very well known that random access channel congestion is a serious issue in case of sporadic arrival of machine-to-machine nodes and may result in a significant delay for all nodes. Hence, random access channel resources are also needed to be allocated to different services to enable random access network slicing. In the random access channel procedure, the nodes transmit a selected preamble from a predefined set of preambles. If multiple nodes transmit the same preamble at the same random access channel opportunity, a collision occurs at the eNodeB. To isolate the one service class from others during this phase, one approach is to allocate different preamble subsets to different service classes. This research proposes an adaptive preamble subset allocation method using deep reinforcement learning. The proposed method can distribute preambles to different service classes according to their priority providing virtual isolation for service classes. The results indicate that the proposed mechanism can quickly adapt the preamble allocation according to the changing traffic demands of service classes.
... However, it must be underlined that current network slicing in 5G networks has mainly been carried out and evaluated in the context of the 5GC segment, thus neglecting the radio segment, which is the focus of this paper [28]. Furthermore, many of the contributions at the RAN level focus on theoretical analyses or simulation-level evaluations [29][30][31][32]. Thus, the evaluation of the solutions is not performed in a real 5G architecture. ...
Network slicing is a promising technique used in the smart delivery of traffic and can satisfy the requirements of specific applications or systems based on the features of the 5G network. To this end, an appropriate slice needs to be selected for each data flow to efficiently transmit data for different applications and heterogeneous requirements. To apply the slicing paradigm at the radio segment of a cellular network, this paper presents two approaches for dynamically classifying the traffic types of individual flows and transmitting them through a specific slice with an associated 5G quality-of-service identifier (5QI). Finally, using a 5G standalone (SA) experimental network solution, we apply the radio resource sharing configuration to prioritize traffic that is dispatched through the most suitable slice. The results demonstrate that the use of network slicing allows for higher efficiency and reliability for the most critical data in terms of packet loss or jitter.
... However, resource sharing also brings challenges for slice isolation. Accordingly, a proof-of-concept prototype for RAN run-time slicing which has the capability of isolating, sharing, and customizing resources for three use cases was demonstrated in [6]. Based on a flexible numerology structure in 5G NR, dynamic resource allocation was formulated as an optimization problem for quality of service (QoS) provisioning given the co-existence of eMBB and URLLC services in [7]. ...
The improvements in worker ranches can be attributed to the advancements in scattered enrolling, which have led to considerable increase in energy utilization and the impact on the climate in terms of carbon impressions. To minimize the usage of power, a few basic approaches are required to be framed for sustainability of green cloud. Green cloud architecture (GCA) is being derived from these models driving, not exclusively, to energy proficiency yet likewise to a carbon spread consideration thought. To minimize energy use, a greener climate is considered a solution. Among all associations, the data correspondence headway (ICT) industry is clearly responsible for more prominent improvements in the overall energy use. The objective of this chapter is to determine green circled listing to drive the recyclability or biodegradability of dead matter and present day office wastage by minimizing the utilization of unsafe materials and growing energy consumption during the lifetime.
Network slicing is a fundamental capability for future Fifth Generation (5G) networks to facilitate the cost-effective deployment and operation of multiple logical networks over a common physical network infrastructure in a way that each logical network (i.e. network slice) can be customized and dimensioned to best serve the needs of specific applications (e.g. mobile broadband, smart city, connected car, public safety, fixed wireless access) and users (e.g. general public, enterprise customers, virtual operators, content providers). The practical realization of such capability still raises numerous technical challenges, both in the Core and RAN parts of the 5G system. Through a comprehensive analysis of the impact that the realization of RAN slicing has on the different layers of the radio interface protocol architecture, this article proposes a framework for the support and specification of RAN slices based on the definition of a set of configuration descriptors that characterize the features, policies and resources to be put in place across the radio protocol layers of a next-generation RAN node.
Emerging 5G mobile networks are envisioned to become multi-service environments, enabling the dynamic deployment of services with a diverse set of performance requirements, accommodating the needs of mobile network operators, verticals and over-the-top (OTT) service providers. Virtualizing the mobile network in a flexible way is of paramount importance for a cost-effective realization of this vision. While virtualization has been extensively studied in the case of the mobile core, virtualizing the radio access network (RAN) is still at its infancy. In this paper, we present Orion, a novel RAN slicing system that enables the dynamic on-the-fly virtualization of base stations, the flexible customization of slices to meet their respective service needs and which can be used in an end-to-end network slicing setting. Orion guarantees the functional and performance isolation of slices, while allowing for the efficient use of RAN resources among them. We present a concrete prototype implementation of Orion for LTE, with experimental results, considering alternative RAN slicing approaches, indicating its efficiency and highlighting its isolation capabilities. We also present an extension to Orion for accommodating the needs of OTT providers.
5G networks will support very diverse and challenging requirements. Network slicing offers an effective way to unlock the full potential of 5G networks and meet those requirements using a common network infrastructure. This article presents a cloud-native approach to network slicing that advances a fundamental rethinking of the mobile network to shift its architectural vision from a network of entities to a network of capabilities, and its driving purpose from a network for connectivity to a network for services. The approach covers the entire life cycle of network slices, encompassing their design, creation, deployment, customization, and optimization. We provide an overview of our cloud-native approach to network slicing and describe a proof-of-concept implementation that demonstrates its key principles.
Cellular traffic continues to grow rapidly making the scalability of the cellular infrastructure a critical issue. However, there is mounting evidence that the current Evolved Packet Core (EPC) is ill-suited to meet these scaling demands: EPC solutions based on specialized appliances are expensive to scale and recent software EPCs perform poorly, particularly with increasing numbers of devices or signaling traffic.
In this paper, we design and evaluate a new system architecture for a software EPC that achieves high and scalable performance. We postulate that the poor scaling of existing EPC systems stems from the manner in which the system is decomposed which leads to device state being duplicated across multiple components which in turn results in frequent interactions between the different components. We propose an alternate approach in which state for a single device is consolidated in one location and EPC functions are (re)organized for efficient access to this consolidated state. In effect, our design "slices" the EPC by user.
We prototype and evaluate PEPC, a software EPC that implements the key components of our design. We show that PEPC achieves 3-7x higher throughput than comparable software EPCs that have been implemented in industry and over 10x higher throughput than a popular open-source implementation (OpenAirInterface). Compared to the industrial EPC implementations, PEPC sustains high data throughput for 10-100x more users devices per core, and a 10x higher ratio of signaling-to-data traffic. In addition to high performance, PEPC's by-user organization enables efficient state migration and customization of processing pipelines. We implement user migration in PEPC and show that state can be migrated with little disruption, e.g., migration adds only up to 4μs of latency to median per packet latencies.
Knowing the variety of services and applications to be supported in the upcoming 5G systems, the current "one size fits all" network architecture is no more efficient. Indeed, each 5G service may have different needs in terms of latency, bandwidth, and reliability, which cannot be sustained by the same physical network infrastructure. In this context, network virtualization represents a viable way to provide a network slice tailored to each service. Several 5G initiatives (from industry and academia) have been pushing for solutions to enable network slicing in mobile networks, mainly based on SDN, NFV, and cloud computing as key enablers. The proposed architectures focus principally on the process of instantiating and deploying network slices, while ignoring how they are enforced in the mobile network. While several techniques of slicing the network infrastructure exist, slicing the RAN is still challenging. In this article, we propose a new framework to enforce network slices, featuring radio resources abstraction. The proposed framework is complementary to the ongoing solutions of network slicing, and fully compliant with the 3GPP vision. Indeed, our contributions are twofold: a fully programmable network slicing architecture based on the 3GPP DCN and a flexible RAN (i.e., programmable RAN) to enforce network slicing; a two-level MAC scheduler to abstract and share the physical resources among slices. Finally, a proof of concept on RAN slicing has been developed on top of OAI to derive key performance results, focusing on the flexibility and dynamicity of the proposed architecture to share the RAN resources among slices.
The upcoming 5G ecosystem is envisioned to build business-driven Network Slices to accommodate the different needs of divergent service types, applications and services in support of vertical industries. In this paper, we describe the Network Slicing concept, by unveiling a novel Network Slicing architecture for integrated 5G communications. Further, we demonstrate its realization, for the case of evolved LTE, using state of the art
technologies. Finally, we elaborate on the LTE specific requirements towards 5G and point out existing challenges and open issues.
With the continued exponential growth of mobile traffic and the rise of diverse applications, the current LTE radio access network (RAN) architecture of cellular operators face mounting challenges. Current RAN suffers from insufficient radio resource coordination, inefficient infrastructure utilization, and inflexible data paths. We present the high level design of PRAN, which centralizes base stations' L1/L2 processing into a cluster of commodity servers. PRAN uses a flexible data path model to support new protocols; multiple base stations' L1/L2 processing tasks are scheduled on servers with performance guarantees; and a RAN scheduler coordinates the allocation of shared radio resources between operators and base stations. Our evaluation shows the feasibility of fast data path control and efficiency of resource pooling (a potential for a 30× reduction on resources).