NFVnice: Dynamic Backpressure and Scheduling for NFV Service Chains

Kulkarni, Sameer; Zhang, Wei; Hwang, Jinho; Rajagopalan, Shriram; Ramakrishnan, K. K.; Wood, Timothy; Arumaithurai, Mayutan; Fu, Xiaoming

doi:10.1109/tnet.2020.2969971

Cited by 47 publications

(41 citation statements)

References 27 publications

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“…For all the tested VNFs we consider CPU as the most important resource metric, and assume CPU is more likely to become a bottleneck resource than memory. This is also confirmed in [7].…”

Section: Measured Network Functionssupporting

confidence: 70%

Profile-Based Resource Allocation for Virtualized Network Functions

Rossem

Tavernier

Colle

et al. 2019

IEEE Trans. Netw. Serv. Manage.

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The virtualization of compute and network resources enables an unseen flexibility for deploying network services. A wide spectrum of emerging technologies allows an ever-growing range of orchestration possibilities in cloud-based environments. But in this context it remains challenging to rhyme dynamic cloud configurations with deterministic performance. The service operator must somehow map the performance specification in the Service Level Agreement (SLA) to an adequate resource allocation in the virtualized infrastructure. We propose the use of a VNF profile to alleviate this process. This is illustrated by profiling the performance of four example network functions (a virtual router, switch, firewall and cache server) under varying workloads and resource configurations. We then compare several methods to derive a model from the profiled datasets. We select the most accurate method to further train a model which predicts the services' performance, in function of incoming workload and allocated resources. Our presented method can offer the service operator a recommended resource allocation for the targeted service, in function of the targeted performance and maximum workload specified in the SLA. This helps to deploy the softwarized service with an optimal amount of resources to meet the SLA requirements, thereby avoiding unnecessary scaling steps.

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“…For all the tested VNFs we consider CPU as the most important resource metric, and assume CPU is more likely to become a bottleneck resource than memory. This is also confirmed in [7].…”

Section: Measured Network Functionssupporting

confidence: 70%

Profile-Based Resource Allocation for Virtualized Network Functions

Rossem

Tavernier

Colle

et al. 2019

IEEE Trans. Netw. Serv. Manage.

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show abstract

“…(3) Scheduling decisions are made at packet-level vs flow-level, making our solution more adaptive to traffic dynamics. Some recent works [31], [49]- [51] consider optimizations and scheduling at the level of a single server or CPU core. In particular, NFVnice is a VNF framework for CPUs that aims for fair and efficient resource allocation of chains, considering the impact of different VNFs on resource usage.…”

Section: Related Workmentioning

confidence: 99%

Letting off STEAM: Distributed Runtime Traffic Scheduling for Service Function Chaining

Blöcher

Khalili

Wang

et al. 2020

IEEE INFOCOM 2020 - IEEE Conference on Computer Communications

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Network function virtualization has introduced a high degree of flexibility for orchestrating service functions. The provisioning of chains of service functions requires making decisions on both (1) placement of service functions and (2) scheduling of traffic through them. The placement problem (1) can be tackled during the planning phase, by exploiting coarsegrained traffic information, and has been studied extensively. However, runtime traffic scheduling (2) for optimizing system utilization and service quality, as required for future edge cloud and mobile carrier scenarios, has not been addressed so far. We fill this gap by presenting a queuing-based system model to characterize the runtime traffic scheduling problem for service function chaining. We propose a throughput-optimal scheduling policy, called integer allocation maximum pressure policy (IA-MPP). To ensure practicality in large distributed settings, we propose multi-site cooperative IA-MPP (STEAM), fulfilling runtime requirements while achieving near-optimal performance. We examine our policies in various settings representing realworld scenarios. STEAM closely matches IA-MPP in terms of throughput, and significantly outperforms (possible adaptations of) existing static or coarse-grained dynamic solutions, requiring 30%-60% less server capacity for similar service quality. Our STEAM prototype shows feasibility running on a standard server.

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“…NFVnice [14] is a user-space scheduler on a host that decides whether a packet is delivered to its desired VNF. It also monitors all VNFs in the system.…”

Section: Selected Sfc-related Activitiesmentioning

confidence: 99%

Service Function Chaining Based on Segment Routing Using P4 and SR-IOV (P4-SFC)

Stockmayer

Hinselmann

Häberle

et al. 2020

Lecture Notes in Computer Science

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In this paper we describe P4-SFC to support service function chaining (SFC) based on a single P4-capable switch and off-the-shelf components. It utilizes MPLS-based segment routing for traffic forwarding in the network and SR-IOV for efficient packet handling on hosts. We describe the P4-SFC architecture and demonstrate its feasibility by a prototype using the Tofino Edgecore Wedge 100BF-32X as P4 switch. Performance test show that L2 throughput for VNFs on a host is significantly larger when connected via SR-IOV with the host’s network interface card instead of over a software switch.

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NFVnice: Dynamic Backpressure and Scheduling for NFV Service Chains

Cited by 47 publications

References 27 publications

Profile-Based Resource Allocation for Virtualized Network Functions

Profile-Based Resource Allocation for Virtualized Network Functions

Letting off STEAM: Distributed Runtime Traffic Scheduling for Service Function Chaining

Service Function Chaining Based on Segment Routing Using P4 and SR-IOV (P4-SFC)

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