Adaptive network-traffic balancing on multi-core software networking devices

Buh, Toma; Trobec, Roman; Ciglič, Andrej

doi:10.1016/j.comnet.2014.04.015

Cited by 8 publications

(6 citation statements)

References 24 publications

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“…[8] investigates how load balancing, by adding a new layer in the system architecture that can become a bottleneck, must be carefully designed and could lead to performance penalties, while Ref. [9] defines a novel adaptive traffic distribution among the CPU cores on a per-packet basis trying to mitigate such issue. As we will better describe later, we call upscaling costs the performance degradation due to load balancing.…”

Section: A Processing-resource Sharingmentioning

confidence: 99%

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

Savi

Tornatore

Verticale

2021

IEEE Trans. Cloud Comput.

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Network Function Virtualization (NFV) provides higher flexibility for network operators and reduces the complexity in network service deployment. Using NFV, Virtual Network Functions (VNF) can be located in various network nodes and chained together in a Service Function Chain (SFC) to provide a specific service. Consolidating multiple VNFs in a smaller number of locations would allow decreasing capital expenditures. However, excessive consolidation of VNFs might cause additional latency penalties due to processing-resource sharing, and this is undesirable, as SFCs are bounded by service-specific latency requirements. In this paper, we identify two different types of penalties (referred as "costs") related to the processingresource sharing among multiple VNFs: the context switching costs and the upscaling costs. Context switching costs arise when multiple CPU processes (e.g., supporting different VNFs) share the same CPU and thus repeated loading/saving of their context is required. Upscaling costs are incurred by VNFs requiring multi-core implementations, since they suffer a penalty due to the load-balancing needs among CPU cores. These costs affect how the chained VNFs are placed in the network to meet the performance requirement of the SFCs. We evaluate their impact while considering SFCs with different bandwidth and latency requirements in a scenario of VNF consolidation.

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Section: A Processing-resource Sharingmentioning

confidence: 99%

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

Savi

Tornatore

Verticale

2021

IEEE Trans. Cloud Comput.

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“…Likewise, to find solutions in polynomial times, Khebbache et al [22] also used matrix-based optimization and a multi-stage graph approach. This work illustrated that at a VNF chain placement stage, utilizing a perfect 2 matching method is beneficial to boost the resolution time, and ultimately adopting this method guarantees scalability, as well.…”

Section: Related Work 21 Service Function Chainingmentioning

confidence: 99%

“…The following research also focuses on dynamic resource management using control theory, workload prediction and modeling of performance dynamics [28]- [33]. However, each VNF depends on the different quality of services.…”

Section: Related Work 21 Service Function Chainingmentioning

confidence: 99%

Knowledge defined networks on the edge for service function chaining and reactive traffic steering

et al. 2022

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“…In that case, there should be an efficient processing-resource allocation mechanism to accommodate VNF demands. 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).…”

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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Adaptive network-traffic balancing on multi-core software networking devices

Cited by 8 publications

References 24 publications

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

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

Knowledge defined networks on the edge for service function chaining and reactive traffic steering

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

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