Optimal Slice Allocation in 5G Core Networks

Sattar, Danish; Matrawy, Ashraf

doi:10.1109/lnet.2019.2908351

Cited by 91 publications

(59 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For every new value of K r el , a new optimization solution is calculated, and slices are reallocated accordingly before the corresponding experiments are run. In our previous work [19], we discussed the overall CPU and bandwidth utilization of the system, and average solver runtime for different levels of intra-slice isolation.…”

Section: Evaluation Methodsologymentioning

confidence: 99%

Towards Secure Slicing: Using Slice Isolation to Mitigate DDoS Attacks on 5G Core Network Slices

Sattar

Matrawy

2019

2019 IEEE Conference on Communications and Network Security (CNS)

Self Cite

View full text Add to dashboard Cite

In this paper, we propose a solution to proactively mitigate Distributed Denial-of-Service attacks in 5G core network slicing using slice isolation. Network slicing is one of the key technologies that allow 5G networks to offer dedicated resources to different industries (services). However, a Distributed Denialof-Service attack could severely impact the performance and availability of the slices as they could share the same physical resources in a multi-tenant virtualized networking infrastructure. Slice isolation is an essential requirement for 5G network slicing.In this paper, we use network isolation to tackle the challenging problem of Distributed Denial-of-Service attacks in 5G network slicing. We propose the use of a mathematical model that can provide on-demand slice isolation as well as guarantee end-to-end delay for 5G core network slices. We evaluate the proposed work with a mix of simulation and experimental work. Our results show that the proposed isolation could mitigate Distributed Denial-of-Service attacks as well as increase the availability of the slices. We believe this work will encourage further research in securing 5G network slicing.

show abstract

Section: Evaluation Methodsologymentioning

confidence: 99%

Towards Secure Slicing: Using Slice Isolation to Mitigate DDoS Attacks on 5G Core Network Slices

Sattar

Matrawy

2019

2019 IEEE Conference on Communications and Network Security (CNS)

Self Cite

View full text Add to dashboard Cite

show abstract

“…These parameters will be varied later to evaluate the impacts of the immediate reward on the decisions of the RMO. Each slice request requires 1 GB of storage resources, 2 CPUs for computing, and 100 Mbps of radio resources [44]. Importantly, the architecture of the deep neural network requires thoughtful design as it greatly affects the performance of the algorithm.…”

Section: Performance Evaluation a Parameter Settingmentioning

confidence: 99%

Optimal and Fast Real-Time Resource Slicing With Deep Dueling Neural Networks

Huynh

Hoang

Nguyen

et al. 2019

IEEE J. Select. Areas Commun.

108

View full text Add to dashboard Cite

Effective network slicing requires an infrastructure/network provider to deal with the uncertain demand and real-time dynamics of network resource requests. Another challenge is the combinatorial optimization of numerous resources, e.g., radio, computing, and storage. This article develops an optimal and fast real-time resource slicing framework that maximizes the long-term return of the network provider while taking into account the uncertainty of resource demand from tenants. Specifically, we first propose a novel system model which enables the network provider to effectively slice various types of resources to different classes of users under separate virtual slices. We then capture the real-time arrival of slice requests by a semi-Markov decision process. To obtain the optimal resource allocation policy under the dynamics of slicing requests, e.g., uncertain service time and resource demands, a Q-learning algorithm is often adopted in the literature. However, such an algorithm is notorious for its slow convergence, especially for problems with large state/action spaces. This makes Q-learning practically inapplicable to our case in which multiple resources are simultaneously optimized. To tackle it, we propose a novel network slicing approach with an advanced deep learning architecture, called deep dueling that attains the optimal average reward much faster than the conventional Q-learning algorithm. This property is especially desirable to cope with real-time resource requests and the dynamic demands of users. Extensive simulations show that the proposed framework yields up to 40% higher long-term average return while being few thousand times faster, compared with state of the art network slicing approaches. {Hoang.Dinh, Diep.Nguyen, and Eryk.Dutkiewicz}@uts.edu.au. and development. In order to enhance operators' products for vertical enterprises and provide service customization for emerging massive connections, as well as to give more control to enterprises and mobile virtual network operators, the concept of network slicing has been recently introduced to allow the independent usage of a part of network resources by a group of mobile terminals with special requirements.Network slicing was introduced by Next Generation Mobile Networks Alliance [2], and it has quickly received a lot of attention from both academia and industry. In general, network slicing is a novel virtualization paradigm that enables multiple logical networks, i.e., slices, to be created according to specific technical or commercial demands and simultaneously run on top of the physical network infrastructure. The core idea of the network slicing is using software-defined networking (SDN) and network functions virtualization (NFV) technologies for virtualizing the physical infrastructure and controlling network operations. In particular, SDN provides a separation between the network control and data planes, improving the flexibility of network function management and efficiency of data transfer. Meanwhile, NFV allows various network functio...

show abstract

“…However, dynamic slicing comes at a cost of service quality degradation. Danish Sattar and Ashraf Matrawy [25] proposed an optimal slice allocation strategy for the 5G core network concerning the intra-slice isolation and delay requirement of slices. They formulated the problem as a MILP model and solved it with CPLEX.…”

Section: Resource Allocation In Network Slicingmentioning

confidence: 99%

Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes

Guo²,

Xu³

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

Efficient provisioning of 5G network slices is a major challenge for 5G network slicing technology. Previous slice provisioning methods have only considered network resource attributes and ignored network topology attributes. These methods may result in a decrease in the slice acceptance ratio and the slice provisioning revenue. To address these issues, we propose a two-stage heuristic slice provisioning algorithm, called RT-CSP, for the 5G core network by jointly considering network resource attributes and topology attributes in this paper. The first stage of our method is called the slice node provisioning stage, in which we propose an approach to scoring and ranking nodes using network resource attributes (i.e., CPU capacity and bandwidth) and topology attributes (i.e., degree centrality and closeness centrality). Slice nodes are then provisioned according to the node ranking results. In the second stage, called the slice link provisioning stage, the k-shortest path algorithm is implemented to provision slice links. To further improve the performance of RT-CSP, we propose RT-CSP+, which uses our designed strategy, called minMaxBWUtilHops, to select the best physical path to host the slice link. The strategy minimizes the product of the maximum link bandwidth utilization of the candidate physical path and the number of hops in it to avoid creating bottlenecks in the physical path and reduce the bandwidth cost. Using extensive simulations, we compared our results with those of the state-of-the-art algorithms. The experimental results show that our algorithms increase slice acceptance ratio and improve the provisioning revenue-to-cost ratio.

show abstract

Optimal Slice Allocation in 5G Core Networks

Cited by 91 publications

References 5 publications

Towards Secure Slicing: Using Slice Isolation to Mitigate DDoS Attacks on 5G Core Network Slices

Towards Secure Slicing: Using Slice Isolation to Mitigate DDoS Attacks on 5G Core Network Slices

Optimal and Fast Real-Time Resource Slicing With Deep Dueling Neural Networks

Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes

Contact Info

Product

Resources

About