Impact of Processing-Resource Sharing on the Placement of Chained Virtual Network Functions

Savi, Marco; Tornatore, Massimo; Verticale, Giacomo

doi:10.1109/tcc.2019.2914387

Cited by 43 publications

(51 citation statements)

References 46 publications

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“…λ c i,u includes the time taken by the VSNF to process the packet and the overhead of the virtualization technology (VMware, KVM, QEMU virtual machines, Docker containers, etc.). For simplicity, we do not model the delays due to the CPU scheduler operations implemented on the physical node [26]. Based on the observations in [27], [28], [29], λ c i,u is modeled as a convex function of the traffic load of the chain, and its value is computed by considering the impact of other VSNFs co-located on the same physical node.…”

Section: B Constraintsmentioning

confidence: 99%

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Dynamic and Application-Aware Provisioning of Chained Virtual Security Network Functions

Doriguzzi-Corin

Scott-Hayward

Siracusa

et al. 2020

IEEE Trans. Netw. Serv. Manage.

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A promising area of application for Network Function Virtualization is in network security, where chains of Virtual Security Network Functions (VSNFs), i.e., security-specific virtual functions such as firewalls or Intrusion Prevention Systems, can be dynamically created and configured to inspect, filter or monitor the network traffic. However, the traffic handled by VSNFs could be sensitive to specific network requirements, such as minimum bandwidth or maximum end-to-end latency. Therefore, the decision on which VSNFs should apply for a given application, where to place them and how to connect them, should take such requirements into consideration. Otherwise, security services could affect the quality of service experienced by customers.In this paper we propose PESS (Progressive Embedding of Security Services), a solution to efficiently deploy chains of virtualised security functions based on the security requirements of individual applications and operators' policies, while optimizing resource utilization. We provide the PESS mathematical model and heuristic solution.Simulation results show that, compared to state-of-the-art application-agnostic VSNF provisioning models, PESS reduces computational resource utilization by up to 50%, in different network scenarios. This result ultimately leads to a higher number of provisioned security services and to up to a 40% reduction in end-to-end latency of application traffic.

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Section: B Constraintsmentioning

confidence: 99%

“…The first one that satisfies Constraint (14) is the accepted solution (lines [18][19]. Finally, the algorithm updates the values of γ i and β i by removing the resources consumed with the accepted solution and stores the mapping of its chains in the set C that records all the active chains in the network (lines [26][27].…”

Section: The Pess Heuristic Algorithmmentioning

confidence: 99%

Dynamic and Application-Aware Provisioning of Chained Virtual Security Network Functions

Doriguzzi-Corin

Scott-Hayward

Siracusa

et al. 2020

IEEE Trans. Netw. Serv. Manage.

Self Cite

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“…Buh et al [32] defined a novel adaptive traffic distribution among multi-core architectures, this method was experimentally validated by tests on Linux Bridge networking devices. Savi et al [33] considered two penalties include context switching costs (causing by repeated context loading/saving on the same CPU) and upscaling costs (causing by load-balancing needs of VNFs among multiple CPU cores). Both of them were verified to affect the embedding of SFCs.…”

Section: Related Workmentioning

confidence: 99%

Resource Aware Chaining and Adaptive Capacity Scaling for Service Function Chains in Distributed Cloud Network

et al. 2019

IEEE Access

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With the development of network technology such as software-defined network (SDN) and network function virtualization (NFV), Internet service providers (ISPs) are increasingly placing the virtual network function(VNF) instances at the network edge to provide network service. However, there are some issues to be tackled in the distributed SDN/NFV enabled cloud. Firstly, VNF instances require to be chained in predefined order to provide network services. It is a challenge to optimally select and chain VNF instances from the multi-instances. Moreover, due to the capacity limitation of the distributed edge nodes. The capacity of the Virtual Machines (VMs) that host VNFs should be proactively adjusted to cope with traffic demands. Since most existing works ignore the vertical capacity scaling problem in routing commodities with Service Function Chain (SFC) requests. In this paper, a fine-grained scheduling scheme at VM-level is proposed. Firstly, we formulate the SFC chaining problem as an Integer Linear Programming (ILP) model aiming to embed SFC requests with minimum estimated latency cost. Furthermore, we formulate the adaptive VNF resource allocation (VNF-AR) problem as a convex optimization. The theoretical optimal capacity for each VM can be derived from the Karush-Kuhn Tucker (KKT) conditions. At last, a novel joint optimization approach of VNF chaining and adaptive scaling (VNF-CAS) is proposed to efficiently embed the SFC requests. Performance evaluation shows that VNF-CAS can achieve better performance in SFC requests acceptance rate, average effective throughput, average load utilization and VM load balancing when it is compared with other algorithms in existing works. INDEX TERMS Network function virtualization, service function chain, distributed cloud network, resource optimization.

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“…A second example is provided in Figure 5, which represents the SFCs required for the deployment of an adaptive wireless video streaming service and its associated SRD graph taken from [35]. Figure 5a represents the VNFs for the user-plane of the 5G-RAN (RU, DU, CU-UP), the 5G-Core (UPF), and the server and Video Optimization Controller (VOC) placed in the data network.…”

Section: B Srd Modelmentioning

confidence: 99%

“…Details of each resource type as well as associated SRD graph are given in Table III. Numerical values in Table III have been adapted from [35]. In the following, we consider multiple demands from different SPs for this type of slice.…”

Section: A Resource Provisioning Vs Direct Embeddingmentioning

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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Impact of Processing-Resource Sharing on the Placement of Chained Virtual Network Functions

Cited by 43 publications

References 46 publications

Dynamic and Application-Aware Provisioning of Chained Virtual Security Network Functions

Dynamic and Application-Aware Provisioning of Chained Virtual Security Network Functions

Resource Aware Chaining and Adaptive Capacity Scaling for Service Function Chains in Distributed Cloud Network

Aggregated Resource Provisioning for Network Slices

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