Cloud-Native Network Slicing Using Software Defined Networking Based Multi-Access Edge Computing: A Survey

Shah, Syed Danial Ali; Gregory, Mark A; Li, Shuo

doi:10.1109/access.2021.3050155

Cited by 87 publications

(53 citation statements)

References 80 publications

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“…Thus, f t is the two-dimensional vector or Subsequently, the sequential output of the encoder layer is fitted into the temporal attention layer with the outputs of the decoder layer to construct the attention context vectors (4). Finally, the LSTM decoder processes the attention context vectors to output future usage of resources (5). The details of each layer are presented in the following sections.…”

Section: Proposed Attention-based Encoder-decoder Framework a Problem...mentioning

confidence: 99%

“…Using these key technologies, network slicing divides a network into multiple virtual networks consisting of virtual network functions (VNFs) and deploy them on virtual machines (VMs) or virtual containers (e.g docker) to reduce cost and energy consumption. Each VNF can be divided into sub-functions called VNF components [5]. In this paper, for simplicity, we assume that each VNF comprises a single VNF component.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Network Slice Scaling Assisted by Attention-Based Prediction in 5G Core Network

Nhu

Park

2022

IEEE Access

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Network slicing is a key technology in fifth-generation (5G) networks that allows network operators to create multiple logical networks over a shared physical infrastructure to meet the requirements of diverse use cases. Among core functions to implement network slicing, resource management and scaling are difficult challenges. Network operators must ensure the Service Level Agreement (SLA) requirements for latency, bandwidth, resources, etc for each network slice while utilizing the limited resources efficiently, i.e., optimal resource assignment and dynamic resource scaling for each network slice. Existing resource scaling approaches can be classified into reactive and proactive types. The former makes a resource scaling decision when the resource usage of virtual network functions (VNFs) exceeds a predefined threshold, and the latter forecasts the future resource usage of VNFs in network slices by utilizing classical statistical models or deep learning models. However, both have a trade-off between assurance and efficiency. For instance, the lower threshold in the reactive approach or more marginal prediction in the proactive approach can meet the requirements more certainly, but it may cause unnecessary resource wastage. To overcome the trade-off, we first propose a novel and efficient proactive resource forecasting algorithm. The proposed algorithm introduces an attention-based encoder-decoder model for multivariate time series forecasting to achieve high short-term and long-term prediction accuracies. It helps network slices be scaled up and down effectively and reduces the costs of SLA violations and resource overprovisioning. Using the attention mechanism, the model attends to every hidden state of the sequential input at every time step to select the most important time steps affecting the prediction results. We also designed an automated resource configuration mechanism responsible for monitoring resources and automatically adding or removing VNF instances of network slices, which helps network operators satisfy service requirements even when the traffic of end-user requests changes dynamically. Comprehensive experiments demonstrate that our proposed solution outperforms other solutions in terms of short-term and long-term predictions while reducing the cost of SLA violations and resource overprovisioning and enhancing the delay quality of network slices.

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Section: Proposed Attention-based Encoder-decoder Framework a Problem...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Network Slice Scaling Assisted by Attention-Based Prediction in 5G Core Network

Nhu

Park

2022

IEEE Access

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“…Many surveys and reviews have been published in the recent years. Many works focus on MEC (first mobile and then multiaccess), referring (at least partially) to the ETSI architecture [52]- [75]. Some works focus exclusively or jointly on fog computing [55], [59], [60], [62], [63], [65]- [67], [71], [73], [76]- [79].…”

Section: Related Workmentioning

confidence: 99%

“…Other works focus on specific environments: vehicular networks [81], [88], IoT [63], [82]- [84], industrial Internet [69]. Finally, other works focus on specific tasks or parts: location trade-off [53], orchestration [54], capabilities on computing, caching, and communication [55], communication [56], computation offload [57], service adoption and provision [61], infrastructure [64], optimization [72], tools and applications [73], integration with network slicing [75].…”

Section: Related Workmentioning

confidence: 99%

5G Multi-access Edge Computing: Security, Dependability, and Performance

Nencioni,

Garroppo,

Olimid

2021

Preprint

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The main innovation of the Fifth Generation (5G) of mobile networks is the ability to provide novel services with new and stricter requirements. One of the technologies that enable the new 5G services is the Multi-access Edge Computing (MEC). MEC is a system composed of multiple devices with computing and storage capabilities that are deployed at the edge of the network, i.e., close to the end users. MEC reduces latency and enables contextual information and real-time awareness of the local environment. MEC also allows cloud offloading and the reduction of traffic congestion. Performance is not the only requirement that the new 5G services have. New missioncritical applications also require high security and dependability. These three aspects (security, dependability, and performance) are rarely addressed together. This survey fills this gap and presents 5G MEC by addressing all these three aspects. First, we overview the background knowledge on MEC by referring to the current standardization efforts. Second, we individually present each aspect by introducing the related taxonomy (important for the not expert on the aspect), the state of the art, and the challenges on 5G MEC. Finally, we discuss the challenges of jointly addressing the three aspects.

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“…The total state transfer time is the time needed to transfer all states of the migrated flows, whereby the time to transfer the states of each flow is calculated by Eqn. (7) and the parameters to evaluate the transmission delays of the state information of the three considered applications follow the approach in [49]. The execution time is the time needed to execute each evaluated scheme.…”

Section: Performance Metricsmentioning

confidence: 99%

FAST: Flexible and Low-Latency State Transfer in Mobile Edge Computing

et al. 2021

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Mobile Edge Computing (MEC) brings the benefits of cloud computing, such as computation, networking, and storage resources, close to end users, thus reducing end-to-end latency and enabling various novel use cases, such as vehicle platooning, autonomous driving, and the tactile internet. However, frequent user mobility makes it challenging for the MEC to guarantee the close proximity to the users. To tackle this challenge, the underlying network has to be capable of seamlessly migrating applications between multiple MEC sites. This application migration requires the quick and flexible migration of the application states without service interruption, while minimizing the state transfer cost. In this article, we first study the state transfer optimization problem in the MEC. To solve this problem, we propose a metaheuristic algorithm based on Tabu search. We then propose Flexible and Low-Latency State Transfer in Mobile Edge Computing (FAST), the first programmable state forwarding framework. FAST flexibly and directly forwards states between source instance and destination instance based on Software-Defined Networking (SDN). Both simulation results and practical testbed results demonstrate the favorable performance of the proposed Tabu search algorithm and the FAST framework compared to the state-of-the-art schemes.INDEX TERMS Application state transfer, multi-access edge computing (MEC), network function virtualization (NFV), software-defined networking (SDN).

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Cloud-Native Network Slicing Using Software Defined Networking Based Multi-Access Edge Computing: A Survey

Cited by 87 publications

References 80 publications

Dynamic Network Slice Scaling Assisted by Attention-Based Prediction in 5G Core Network

Dynamic Network Slice Scaling Assisted by Attention-Based Prediction in 5G Core Network

5G Multi-access Edge Computing: Security, Dependability, and Performance

FAST: Flexible and Low-Latency State Transfer in Mobile Edge Computing

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