On the Trade-Off Between Computational Load and Reliability for Network Function Virtualization

Kang, Jinkyu; Simeone, Osvaldo; Kang, Joonhyuk

doi:10.1109/lcomm.2017.2698040

Cited by 37 publications

(11 citation statements)

References 9 publications

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“…3) The third method is based on the idea of joint optimization of various aspects of the wireless networks, including reliability, availability, communication resources, and radio resources [28]- [31]. The joint optimization of the SFC is carried out by ensuring indicators such as end-to-end QoS are satisfied.…”

Section: B Optimization Of Sfcmentioning

confidence: 99%

On Credibility-Based Service Function Chain Deployment

Fan

Cui

et al. 2021

IEEE Open J. Comput. Soc.

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With the advancements of Software Defined Networking and Network Function Virtualization technologies, users can access the software-based service function chain (SFC), which is composed of multiple sequential virtual network function (VNF) nodes. Although SFC is more flexible and adaptive in terms of design and deployment, the security risks should not be underestimated. At present, there is a lack of security or risk assessment for SFC, and SFC deployments rarely take their security into account. However, vulnerabilities and risks can cause VNF node failure during operation, which can lead to issues such as disruptions in SFC service and user data leakage. This paper proposes the concept of SFC credibility, which quantifies the authenticity, availability, and reliability of the VNF nodes from both time and space dimensions. Then, a hierarchical credibility evaluation model is built such that VNF nodes can be selected for the user based on their trustworthiness. A credibility-based deployment strategy is further designed for SFC and the corresponding VNF forwarding graph. Furthermore, a comparative study with three existing deployment strategies has shown the advantages of the proposed method. The extensive experimental results demonstrate the improved trust degree and the acceptance rate of SFC with a limited budget.

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Section: B Optimization Of Sfcmentioning

confidence: 99%

On Credibility-Based Service Function Chain Deployment

Fan

Cui

et al. 2021

IEEE Open J. Comput. Soc.

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“…Their work is one of the first works on this topic, and recently Li et al [25] proposed onsite and off-site backup mechanisms by developing efficient scheduling algorithms for the problem through adopting the primal and dual update technique. Kang et al [23] studied the tradeoff between the reliability of a network service, as measured by the probability that the service is correctly executed, and the computational load of servers. Engelmann et al [13] studied the end-to-end service reliability in data center networks (DCNs).…”

Section: Related Workmentioning

confidence: 99%

Reliability-Aware Virtualized Network Function Services Provisioning in Mobile Edge Computing

Huang

Liang

Shen

et al. 2020

IEEE Trans. on Mobile Comput.

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Along with Network Function Virtualization (NFV), Mobile Edge Computing (MEC) is becoming a new computing paradigm that enables accommodating innovative applications and services with stringent response delay and resource requirements, including autonomous vehicles and augmented reality. Provisioning reliable network services for users is the top priority of most network service providers, as unreliable services or severe service failures can result in tremendous losses of users, particularly for their mission-critical applications. In this paper, we study reliability-aware VNF instances provisioning in an MEC, where different users request different network services with different reliability requirements through paying their requested services with the aim to maximize the network throughput. To this end, we first formulate a novel reliability-aware VNF instance placement problem by provisioning primary and secondary VNF instances at different cloudlets in MEC for each user while meeting the specified reliability requirement of the user request. We then show that the problem is NP-hard and formulate an Integer Linear Programming (ILP) solution. Due to the NP-hardness of the problem, we instead devise an approximation algorithm with a logarithmic approximation ratio for the problem. Moreover, we also consider two special cases of the problem. For one special case where each request only requests one primary and one secondary VNF instances, the problem is still NP-hard, and we devise a constant approximation algorithm for it. For another special case where different VNFs have the same amounts of computing resource demands, we show that it is polynomial-time solvable by developing a dynamic programming solution for it. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising, and the empirical results of the algorithms outperform their analytical counterparts as theoretical estimations usually are very conservative.

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“…For example, in [17,18], computing, storage, and aggregated wireless resource demands of SFCs are considered. The minimization of the SFC embedding cost is formulated either as an Integer Linear Programming (ILP) [18,19,20] or as a Mixed Integer Linear Programming (MILP) problem [13,21], which are known to be NP-hard [22]. In [23], the VNF placement problem is expressed as an Integer Quadratic Programming (IQP) problem with a set of energy consumption constraints, and then is transformed to a solvable linear form.…”

Section: B Related Workmentioning

confidence: 99%

“…must be satisfied ∀σ ∈ S. The constraints (19)(20)(21) can be gathered in the following single constraint…”

Section: B Accounting For Other Srd Constraintsmentioning

confidence: 99%

Aggregated Resource Provisioning for Network Slices

Luu

Kieffer

Mouradian

et al. 2018

2018 IEEE Global Communications Conference (GLOBECOM)

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Network slicing appears as a key enabler for the future 5G networks. Mobile Network Operators create various slices for Service Providers (SP) to accommodate customized services. As network slices are operated on a common network infrastructure owned by some Infrastructure Provider (InP), sharing the resources across a set of network slices is important for future deployment. Moreover, in many situations, slices have to be deployed over some geographical area: coverage as well as minimum per-user rate constraints have then to be taken into account.Usually, the various Service Function Chains (SFCs) belonging to a slice are deployed on a best-effort basis. Nothing ensures that the InP will be able to allocate enough resources to cope with the increasing demands of some SP. This paper takes the InP perspective and proposes a slice resource provisioning approach to cope with multiple slice demands in terms of computing, storage, coverage, and rate constraints.The resource requirements of the various Service Function Chains to be deployed within a slice are aggregated within a graph of Slice Resource Demands (SRD). Coverage and rate constraints are also taken into account in the SRD. Infrastructure nodes and links have then to be provisioned so as to satisfy all types of resource demands. This problem leads to a Mixed Integer Linear Programming formulation. A two-step deployment approach is considered, with several variants, depending on whether the constraints of each slide to be deployed are taken into account sequentially or jointly. Once provisioning has been performed, any slice deployment strategy may be considered on the reduced-size infrastructure graph representing the nodes and links on which resources have been provisioned.Simulation results demonstrate the effectiveness of the proposed approach compared to a more classical direct slice embedding approach.

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On the Trade-Off Between Computational Load and Reliability for Network Function Virtualization

Cited by 37 publications

References 9 publications

On Credibility-Based Service Function Chain Deployment

On Credibility-Based Service Function Chain Deployment

Reliability-Aware Virtualized Network Function Services Provisioning in Mobile Edge Computing

Aggregated Resource Provisioning for Network Slices

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