Dynamic network slicing and resource allocation for heterogeneous wireless services

Kwak, Jeongho; Moon, Joonyoung; Lee, Hyang-Won; Le, Long Bao

doi:10.1109/pimrc.2017.8292663

Cited by 22 publications

(13 citation statements)

References 7 publications

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“…The numerical simulation results verify that our algorithm outperforms BSRA (Bandwidth Slicing and Resource Allocation algorithm) [12], ACS (Alternative Concave Search algorithm) [13] and Loading Balancing [14] ones in terms of hard latency and power consumption. Moreover, the proposed algorithm can implement a similar performance compared with the semi-offline method which ideally assumes that the future IoT service patterns and network channel information can be learned in advance.…”

Section: Introductionmentioning

confidence: 68%

“…A common approach is to directly process the current load task with the queue, allocating resources accurately without future wireless channel states. A related dynamic resource allocation model often considered for heterogeneous services is the so-called "Bandwidth Slicing and Resource Allocation (BSRA) algorithm" [12]. The algorithm proposes an algorithm for power allocation and quality decision in a coexistence scenario of uplink IoT services and downstream video streaming services.…”

Section: Related Workmentioning

confidence: 99%

“…During the whole RAN transmission process, the generate traffic such as traffic control and video information are transferred to/from data centers in the core networks through a BS (Base station). The power of IoT devices and the quality of video then can be controlled in response to the network condition [12]. We reserve some radio resources for uRLLC and eMBB slices in advance, while sharing others according to the queue backlog to customize the frequency bandwidth slice.…”

Section: A Transmission Rate For Hybrid Servicesmentioning

confidence: 99%

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Dynamic Resource Allocation With RAN Slicing and Scheduling for uRLLC and eMBB Hybrid Services

Feng

et al. 2020

IEEE Access

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To cope with the limited radio and power resources, designing energy-and cost-efficient resource allocation strategy with RAN slicing and scheduling is important in ensuring the extreme QoS of differentiated Internet of things (IoT) services. In this regard, we focus on guaranteeing the latency and reliability of sporadic uRLLC uplink traffic while improving the quality of continuous eMBB services (e.g., quality of the video) together in this paper. Firstly, a dynamic optimization model considering power consumption and service quality is used to construct the cost function in both time domain and frequency bandwidth for heterogeneous services, subject to the latency constraint. Secondly, given its complexity, a novel two-timescale algorithm with employing Lyapunov optimization is designed, including two sub-algorithms: long-timescale bandwidth allocation and short-timescale service control. In further, the theoretical optimality is analyzed according to the relationships between control parameters and service performances. The utility of our approach and its hard latency guarantee are also illustrated through simulation results under tolerable power consumption. INDEX TERMS 5G, network slicing, radio access network, resource allocation, heterogeneous services.

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Section: Introductionmentioning

confidence: 68%

Section: Related Workmentioning

confidence: 99%

Section: A Transmission Rate For Hybrid Servicesmentioning

confidence: 99%

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Dynamic Resource Allocation With RAN Slicing and Scheduling for uRLLC and eMBB Hybrid Services

Feng

et al. 2020

IEEE Access

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“…In [26], the authors proposed a dynamic network slicing and resource allocation scheme for video streaming and IoT applications, which is based on the Lyapunov Optimisation in a single cell scenario. However, the URLLC use case, which is highly latency-dependent and requires extreme-reliability, was not considered.…”

Section: B Related Workmentioning

confidence: 99%

Latency-Aware Dynamic Resource Allocation Scheme for Multi-Tier 5G Network: A Network Slicing-Multitenancy Scenario

Oladejo

Falowo

2020

IEEE Access

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In 5G slice networks, the multi-tenant, multi-tier heterogeneous network will be critical in meeting the quality of service (QoS) requirement of the different slice use cases and in reduction of the capital expenditure (CAPEX) and operational expenditure (OPEX) of mobile network operators. Hence, 5G slice networks should be as flexible as possible to accommodate different network dynamics such as user location and distribution, different slice use case QoS requirements, cell load, intra-cluster interference, delay bound, packet loss probability, and service level agreement (SLA) of mobile virtual network operators (MVNO). Motivated by this condition, this paper addresses a latency-aware dynamic resource allocation problem for 5G slice networks in a multi-tenant, multi-tier heterogeneous environment, for efficient radio resource management. The latency-aware dynamic resource allocation problem is formulated as a maximum utility optimisation problem. The optimisation problem is transformed and the hierarchical decomposition technique is adopted to reduce the complexities in solving the optimisation problem. Furthermore, we propose a genetic algorithm (GA) intelligent latency-aware resource allocation scheme (GI-LARE). We compare GI-LARE with the static slicing (SS) resource allocation; the spatial branch and bound-based scheme; and, an optimal resource allocation algorithm (ORA) via Monte Carlo simulation. Our findings reveal that GI-LARE outperformed these other schemes.

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“…Nguyen [34] tackles the resource allocation for the critical EE in 5G wireless networks, which considers the EE in small cells, massive multiple-input multiple-output (MIMO) [35,36] and cell-free networks [37]. In order to dynamically allocate wireless resources [19] such as cell slices and virtual base stations, Kwak et al [38] study bandwidth slicing and resource allocation problems for supporting the mixture of IoT and video streaming services. However, most of them do not consider the ultra-low latency services especially.…”

Section: Network Slicing and Resource Allocationmentioning

confidence: 99%

Latency-Optimal Virtual Network Functions Resource Allocation for 5G Backhaul Transport Network Slicing

Feng

et al. 2019

Applied Sciences

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The concept of network slicing (NS) has been proposed for flexible resource provisioning where a physical resource is partitioned into logically independent networks on demand. The NS resource allocation implies the definition of a feasible path in the infrastructure network with adequate resource availability. However, due to complex structural characteristics of the backhaul transport network, a number of issues arise when fast deploying the end-to-end (E2E) slices onto network infrastructures. In this paper, a pair-decision resource allocation model is firstly formulated to construct the mapping relationship between logical networks and substrate networks in a coordinated way. In order to improve extreme quality of service (QoS) and user experiment, latency-optimal virtual resource allocation problem is defined, subject to the backhaul capacity and bandwidth constraints. The problem is formulated as an integer linear programming (ILP) and solved with the branch-and-bound scheme, whose resolution yields an optimal virtual network function (VNF) placement and traffic routing policy. Numerical results reveal that the proposed scheme can enable the transport network latency optimization with a reduction of up to 30% and 41.6% compared to the Network Slice Design Problem (NSDP) and Random Fit Placement Algorithm (RFPA) schemes respectively. In the meanwhile, the network load balance and serviceability have been improved efficiently with better resource utilization as well.

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Dynamic network slicing and resource allocation for heterogeneous wireless services

Cited by 22 publications

References 7 publications

Dynamic Resource Allocation With RAN Slicing and Scheduling for uRLLC and eMBB Hybrid Services

Dynamic Resource Allocation With RAN Slicing and Scheduling for uRLLC and eMBB Hybrid Services

Latency-Aware Dynamic Resource Allocation Scheme for Multi-Tier 5G Network: A Network Slicing-Multitenancy Scenario

Latency-Optimal Virtual Network Functions Resource Allocation for 5G Backhaul Transport Network Slicing

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