Network Slicing: Recent Advances, Taxonomy, Requirements, and Open Research Challenges

Khan, Latif U.; Yaqoob, Ibrar; Tran, Nguyen H.; Han, Zhu; Hong, Choong Seon

doi:10.1109/access.2020.2975072

Cited by 151 publications

(110 citation statements)

References 88 publications

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“…Network slicing is a specific form of network virtualization that consolidates the transition from a static network infrastructure to a much more dynamic infrastructure. It provides the ability to build several independent logical networks, called network slices, that run on top of a common physical infrastructure [95]. Each network slice is tailored and dedicated to support a specific service with distinctive characteristics and requirements.…”

Section: Network Slicing In Mec 1) Network Slicing Definitionmentioning

confidence: 99%

Multi-Access Edge Computing: A Survey

et al. 2020

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Multi-access Edge Computing (MEC) is a key solution that enables operators to open their networks to new services and IT ecosystems to leverage edge-cloud benefits in their networks and systems. Located in close proximity from the end users and connected devices, MEC provides extremely low latency and high bandwidth while always enabling applications to leverage cloud capabilities as necessary. In this paper, we illustrate the integration of MEC into a current mobile networks' architecture as well as the transition mechanisms to migrate into a standard 5G network architecture. We also discuss SDN, NFV, SFC and network slicing as MEC enablers. Then, we provide a state-of-the-art study on the different approaches that optimize the MEC resources and its QoS parameters. In this regard, we classify these approaches based on the optimized resources and QoS parameters (i.e., processing, storage, memory, bandwidth, energy and latency). Finally, we propose an architectural framework for a MEC-NFV environment based on the standard SDN architecture.

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Section: Network Slicing In Mec 1) Network Slicing Definitionmentioning

confidence: 99%

Multi-Access Edge Computing: A Survey

et al. 2020

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“…The new paradigm, network slicing, is based on network virtualization. It has a considerable potential to achieve requirements over heterogeneous networks, especially for s-health applications that employ different network technologies in their communication [ 40 ]. Next, we present the most relevant works using network slicing in performance management context.…”

Section: Performance Management Approachesmentioning

confidence: 99%

“…Together, they enable dynamic resource monitoring, allocation, and isolation in the network infrastructure by a logically centralized software-based platform [ 47 ]. Thus, network slicing allocates bandwidth, spectrum, and computing capacity based on application requirements [ 40 ]. A tenant refers to a group of users accessing the shared resources with specific access privileges and access rights.…”

Section: Introductionmentioning

confidence: 99%

An Architecture for the Performance Management of Smart Healthcare Applications

Vergütz

Prates

Schwengber

et al. 2020

Sensors

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The sixth-generation (6G) network intends to revolutionize the healthcare sector. It will offer smart healthcare (s-health) treatments and allow efficient patient remote monitoring, exposing the high potential of 6G communication technology in telesurgery, epidemic, and pandemic. Healthcare relies on 6G communication technology, diminishing barriers as time and space. S-health applications require strict network requirements, for instance, 99.999% of service reliability and 1 ms of end-to-end latency. However, it is a challenging task to manage network resources and applications towards such performance requirements. Hence, significant attention focuses on performance management as a way of searching for efficient approaches to adjust and tune network resources to application needs, assisting in achieving the required performance levels. In the literature, performance management employs techniques such as resource allocation, resource reservation, traffic shaping, and traffic scheduling. However, they are dedicated to specific problems such as resource allocation for a particular device, ignoring the heterogeneity of network devices, and communication technology. Thus, this article presents PRIMUS, a performance management architecture that aims to meet the requirements of low-latency and high-reliability in an adaptive way for s-health applications. As network slicing is central to realizing the potential of 5G–6G networks, PRIMUS manages traffic through network slicing technologies. Unlike existing proposals, it supports device and service heterogeneity based on the autonomous knowledge of s-health applications. Emulation results in Mininet-WiFi show the feasibility of meeting the s-health application requirements in virtualized environments.

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“…The optimal r p k and µ l can be obtained using the first-order optimality condition for the per-link subproblem in (14). Here, we list KKT conditions as follows:…”

Section: Distributed Multi-path Routing In the Backhaulmentioning

confidence: 99%

“…Short term user demands are predicted by Long Short-Term Memory (LSTM) neural networks in [11]- [13]. Recurring resource reservations based on the short-term traffic variations incur reconfiguration costs, service interruptions, and overhead in networks [14]. The mean of user demands is used in [15] to balance the workload among a set of data centers in a network that consists of the backhaul and RAN such that the utilization of resources is maximized.…”

Section: Introductionmentioning

confidence: 99%

Resource Reservation in Backhaul and Radio Access Network with Uncertain User Demands

Reyhanian

Farmanbar

Luo

2020

2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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Resource reservation is an essential step to enable wireless data networks to support a wide range of user demands. In this paper, we consider the problem of joint resource reservation in the backhaul and Radio Access Network (RAN) based on the statistics of user demands and channel states, and also network availability. The goal is to maximize the sum of expected traffic flow rates, subject to link and access point budget constraints, while minimizing the expected outage of downlinks. The formulated problem turns out to be non-convex and difficult to solve to global optimality. We propose an efficient Block Coordinate Descent (BCD) algorithm to approximately solve the problem. The proposed BCD algorithm optimizes the link capacity reservation in the backhaul using a novel multipath routing algorithm that decomposes the problem down to link-level and parallelizes the computation across backhaul links, while the reservation of transmission resources in RAN is carried out via a novel scalable and distributed algorithm based on Block Successive Upper-bound Minimization (BSUM). We prove that the proposed BCD algorithm converges to a Karush-Kuhn-Tucker (KKT) solution. Simulation results verify the efficiency and the efficacy of our BCD approach against two heuristic algorithms.

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Network Slicing: Recent Advances, Taxonomy, Requirements, and Open Research Challenges

Cited by 151 publications

References 88 publications

Multi-Access Edge Computing: A Survey

Multi-Access Edge Computing: A Survey

An Architecture for the Performance Management of Smart Healthcare Applications

Resource Reservation in Backhaul and Radio Access Network with Uncertain User Demands

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