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RAN Runtime Slicing System for Flexible and Dynamic Service Execution Environment

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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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... RadioSaber [16] further refines this approach by incorporating channel awareness into the slice-level scheduler, optimizing RB allocation based on users' Channel Quality Indicators (CQIs). Ensuring slice-level service quality is a primary concern across various RAN slicing proposals [4,10,32]. Recent efforts, such as Zipper [5], a ML-based algorithm to compute SLA-compliant schedules in real-time, albeit with a focus on application-level service assurance. Despite these advancements, existing RAN slicing frameworks predominantly focus on single-antenna systems and commonly employ slot-based and RB-based slicing methodologies [16,19,30]. ...
... To make it more realistic, unlike the previous even distribution of users among slices, we generate a random set of numbers (i.e. [10,12,18,20,25,33,45,37] with a mean of 25) to determine the number of users in each slice. With = 16, our experiments include both sparsely populated (|K | < ) and densely populated (|K | > ) slices. ...
Preprint
An important aspect of 5G networks is the development of Radio Access Network (RAN) slicing, a concept wherein the virtualized infrastructure of wireless networks is subdivided into slices (or enterprises), tailored to fulfill specific use-cases. A key focus in this context is the efficient radio resource allocation to meet various enterprises' service-level agreements (SLAs). In this work, we introduce a channel-aware and SLA-aware RAN slicing framework for massive multiple input multiple output (MIMO) networks where resource allocation extends to incorporate the spatial dimension available through beamforming. Essentially, the same time-frequency resource block (RB) can be shared across multiple users through multiple antennas. Notably, certain enterprises, particularly those operating critical infrastructure, necessitate dedicated RB allocation, denoted as private networks, to ensure security. Conversely, some enterprises would allow resource sharing with others in the public network to maintain network performance while minimizing capital expenditure. Building upon this understanding, the proposed scheduler comprises scheduling schemes under both scenarios: where different slices share the same set of RBs, and where they require exclusivity of allocated RBs. We validate the efficacy of our proposed schedulers through simulation by utilizing a channel data set collected from a real-world massive MIMO testbed. Our assessments demonstrate that resource sharing across slices using our approach can lead up to 60.9% reduction in RB usage compared to other approaches. Moreover, our proposed schedulers exhibit significantly enhanced operational efficiency, with significantly faster running time compared to exhaustive greedy approaches while meeting the stringent 5G sub-millisecond-level latency requirement.
... Most research on network slicing in the 5G era has been on either radio access network (RAN) slicing or core network (CN) slicing [3]. Research has been carried out on virtualizing and softwarizing the radio resources for RAN slicing [4], while studies known as end-to-end (E2E) network slicing investigates where Virtual Network Functions (VNFs) should be located with respect to the underlying actual infrastructure so that individual slices can operate autonomously [5]. A study was carried out on the creation of interfaces and protocols for inter-slice communication between RAN and CN [6]. ...
... The (3) constraint guarantees that the requested link capacity of a virtual node m ∈ N V can be met by the link capacity of a PI node i ∈ N P . Security level of the virtual node m ∈ N V can be satisfied by the security measures of the PI node i ∈ N P , according to constraint (4). The bandwidth of the PI node i ∈ N P must meet the bandwidth requirements of the virtual node m ∈ N V according to constraint (5). ...
Preprint
Full-text available
Network slicing is crucial to the 5G architecture because it enables the virtual-ization of network resources into a logical network. Network slices are created, isolated, and managed using software-defined networking (SDN) and network function virtualization (NFV). The virtual network function (VNF) manager must devise strategies for all stages of network slicing to ensure optimal allocation of physical infrastructure (PI) resources to high-acceptance virtual service requests (VSRs). This paper investigates two independent network slicing frameworks named as dual-slice isolation and management strategy (D-SIMS) and recommends the best of the two based on performance measurements. D-SIMS places VNFs for network slicing using self-sustained resource reservation (SSRR) and master-sliced resource reservation (MSRR), with some flexibility for the VNF manager to choose between them based on the degree to which the underlying physical infrastructure has been sliced. The present research work consists of two 1 phases: the first deals with the creation of slices, and the second with determining the most efficient way to distribute resources among them. A deep neural network (DNN) technique is used in the first stage to generate slices for both PI and VSR. Then, in the second stage, we propose D-SIMS for resource allocation, which uses both the fuzzy-PROMETHEE method for node mapping and Dijk-stra’s algorithm for link mapping. During the slice creation phase, the proposed DNN training method’s classification performance is evaluated using accuracy, precision, recall, and F1 score measures. To assess the success of resource allocation , metrics such as acceptance rate and resource effectiveness are used. The performance benefit is investigated under various network conditions and VSRs. Finally, to demonstrate the importance of the proposed work, we compare the simulation results to those in the academic literature.
... The (3) constraint guarantees that the requested link capacity of a virtual node m ∈ N V can be met by the link capacity of a PI node i ∈ N P . Security level of the virtual node m ∈ N V can be satisfied by the security measures of the PI node i ∈ N P , according to constraint (4). The bandwidth of the PI node i ∈ N P must meet the bandwidth requirements of the virtual node m ∈ N V according to constraint (5). ...
Article
Full-text available
Network slicing is crucial to the 5G architecture because it enables the virtualization of network resources into a logical network. Network slices are created, isolated, and managed using software-defined networking (SDN) and network function virtualization (NFV). The virtual network function (VNF) manager must devise strategies for all stages of network slicing to ensure optimal allocation of physical infrastructure (PI) resources to high-acceptance virtual service requests (VSRs). This paper investigates two independent network slicing frameworks named as dual-slice isolation and management strategy (D-SIMS) and recommends the best of the two based on performance measurements. D-SIMS places VNFs for network slicing using self-sustained resource reservation (SSRR) and master-sliced resource reservation (MSRR), with some flexibility for the VNF manager to choose between them based on the degree to which the underlying physical infrastructure has been sliced. The present research work consists of two phases: the first deals with the creation of slices, and the second with determining the most efficient way to distribute resources among them. A deep neural network (DNN) technique is used in the first stage to generate slices for both PI and VSR. Then, in the second stage, we propose D-SIMS for resource allocation, which uses both the fuzzy-PROMETHEE method for node mapping and Dijkstra’s algorithm for link mapping. During the slice creation phase, the proposed DNN training method’s classification performance is evaluated using accuracy, precision, recall, and F1 score measures. To assess the success of resource allocation, metrics such as acceptance rate and resource effectiveness are used. The performance benefit is investigated under various network conditions and VSRs. Finally, to demonstrate the importance of the proposed work, we compare the simulation results to those in the academic literature.
... The slice shape identifies the slots over which the K RBs allocated to the slice must be located. We define an allocation window of duration W (expressed in number of slots) that establishes the time period during which the allocation of RBs to slices must be maintained [17]. We denote L t as the number of RBs allocated to the RAN slice in slot t. ...
Conference Paper
5G and beyond networks will support the digitalization of smart manufacturing thanks to their capacity to simultaneously serve different types of traffic with distinct QoS requirements. This can be achieved using Network Slicing that creates different logical network partitions (or slices) over a common infrastructure, and each can be tailored to support a particular type of traffic. The configuration of the Radio Access Network (RAN) slices strongly impacts the capacity of 5G and beyond to support critical services with stringent QoS requirements, and in particular deterministic requirements. Existing RAN Slicing solutions only consider the transmission rate (or bandwidth) requirements of the different services to partition the radio resources. This study demonstrates that this approach is not suitable to guarantee the stringent latency requirements of deterministic aperiodic traffic that is characteristic of industrial critical applications. We then propose designing RAN slices using descriptors that consider both the services' transmission rate and latency requirements, and demonstrate that this approach can support critical services that generate deterministic aperiodic traffic.
... 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. ...
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Chapter
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