Semantically Enhanced Mapping Algorithm for Affinity-Constrained Service Function Chain Requests

Bouten, Niels; Mijumbi, Rashid; Serrat, Joan; Famaey, Jeroen; Latré, Steven; Turck, Filip De

doi:10.1109/tnsm.2017.2681025

Cited by 26 publications

(19 citation statements)

References 29 publications

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“…In general, a distributed cyber-security framework needs coordination in selecting, instantiating and placing security functions, and in delivering the set of collected data, measurements, and events among them. This should guarantee consistency with the detection needs, as well as respect the overall resource constraints and allocation policies for the service [4,8].…”

Section: Challenges and Benefits For Distributed Cybersecurity Frameworkmentioning

confidence: 99%

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An Autonomous Cybersecurity Framework for Next-generation Digital Service Chains

et al. 2021

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Today, the digital economy is pushing new business models, based on the creation of value chains for data processing, through the interconnection of processes, products, services, software, and things across different domains and organizations. Despite the growing availability of communication infrastructures, computing paradigms, and software architectures that already effectively support the implementation of distributed multi-domain value chains, a comprehensive architecture is still missing that effectively fulfills all related security issues: mutual trustworthiness of entities in partially unknown topologies, identification and mitigation of advanced multi-vector threats, identity management and access control, management and propagation of sensitive data. In order to fill this gap, this work proposes a new methodological approach to design and implement heterogeneous security services for distributed systems that combine together digital resources and components from multiple domains. The framework is designed to support both existing and new security services, and focuses on three novel aspects: (i) full automation of the processes that manage the whole system, i.e., threat detection, collection of information and reaction to attacks and system anomalies; (ii) dynamic adaptation of operations and security tasks to newest attack patterns, and (iii) real-time adjustment of the level of detail of inspection and monitoring processes. The overall architecture as well as the functions and relationships of its logical components are described in detail, presenting also a concrete use case as an example of application of the proposed framework.

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Section: Challenges and Benefits For Distributed Cybersecurity Frameworkmentioning

confidence: 99%

“…The better the suitability of the feature extracted, the more effective the security service in its detection and analysis operations (security services will be described in detail in Sect. 4…”

Section: Context Brokermentioning

confidence: 99%

An Autonomous Cybersecurity Framework for Next-generation Digital Service Chains

et al. 2021

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“…1) Infrastructure Topology: As in [17,37], a k-ary fattree infrastructure topology is considered, see Figure 8. The leaf nodes represent the RRHs.…”

Section: A Resource Provisioning Vs Direct Embeddingmentioning

confidence: 99%

Aggregated Resource Provisioning for Network Slices

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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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“…Numerous studies are relevant to big-data-based NFV, including how to determine and place network functions. N Bouten et al [2] presented a set of affinity and anti-affinity constraints that can be used by TSPs to define big-data-based placement constraints. They proposed a semantic conflict mechanism to evaluate SFC requests that filters invalid mechanisms to reduce the mapping time.…”

Section: Related Workmentioning

confidence: 99%

“…Telecommunication service providers (TSPs) desire flexible and cost-efficient methods for dispatching network services as market demands increase. NFV provides an opportunity to efficiently and dynamically deploy service function chains (SFCs) [1][2][3] without modifying dedicated infrastructure, which is costly and has become complex over time. The basic idea behind NFV is to decouple these network functions (e.g., firewall, WAN optimizers, and proxies) from the underlying customized devices and accomplish equivalent network functions via software-based functions running in virtual machines deployed on devices.…”

Section: Introductionmentioning

confidence: 99%