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|
---
title: NFV Service Density Benchmarking
# abbrev: nf-svc-density
docname: draft-mkonstan-nf-service-density-01
date: 2019-07-08
ipr: trust200902
area: ops
wg: Benchmarking Working Group
kw: Internet-Draft
cat: info
coding: us-ascii
pi: # can use array (if all yes) or hash here
# - toc
# - sortrefs
# - symrefs
toc: yes
sortrefs: # defaults to yes
symrefs: yes
author:
-
ins: M. Konstantynowicz
name: Maciek Konstantynowicz
org: Cisco Systems
role: editor
email: mkonstan@cisco.com
-
ins: P. Mikus
name: Peter Mikus
org: Cisco Systems
role: editor
email: pmikus@cisco.com
normative:
RFC2544:
RFC8174:
informative:
RFC8204:
TST009:
target: https://www.etsi.org/deliver/etsi_gs/NFV-TST/001_099/009/03.01.01_60/gs_NFV-TST009v030101p.pdf
title: "ETSI GS NFV-TST 009 V3.1.1 (2018-10), Network Functions Virtualisation (NFV) Release 3; Testing; Specification of Networking Benchmarks and Measurement Methods for NFVI"
date: 2018-10
BSDP:
target: https://fd.io/wp-content/uploads/sites/34/2019/03/benchmarking_sw_data_planes_skx_bdx_mar07_2019.pdf
title: "Benchmarking Software Data Planes Intel® Xeon® Skylake vs. Broadwell"
date: 2019-03
draft-vpolak-mkonstan-bmwg-mlrsearch:
target: https://tools.ietf.org/html/draft-vpolak-mkonstan-bmwg-mlrsearch
title: "Multiple Loss Ratio Search for Packet Throughput (MLRsearch)"
date: 2019-07
draft-vpolak-bmwg-plrsearch:
target: https://tools.ietf.org/html/draft-vpolak-bmwg-plrsearch
title: "Probabilistic Loss Ratio Search for Packet Throughput (PLRsearch)"
date: 2019-07
LFN-FDio-CSIT:
target: https://wiki.fd.io/view/CSIT
title: "Fast Data io, Continuous System Integration and Testing Project"
date: 2019-07
CNCF-CNF-Testbed:
target: https://github.com/cncf/cnf-testbed/
title: "Cloud native Network Function (CNF) Testbed"
date: 2019-07
TRex:
target: https://github.com/cisco-system-traffic-generator/trex-core
title: "TRex Low-Cost, High-Speed Stateful Traffic Generator"
date: 2019-07
CSIT-1904-testbed-2n-skx:
target: https://docs.fd.io/csit/rls1904/report/introduction/physical_testbeds.html#node-xeon-skylake-2n-skx
title: "FD.io CSIT Test Bed"
date: 2019-06
CSIT-1904-test-enviroment:
target: https://docs.fd.io/csit/rls1904/report/vpp_performance_tests/test_environment.html
title: "FD.io CSIT Test Environment"
date: 2019-06
CSIT-1904-nfv-density-methodology:
target: https://docs.fd.io/csit/rls1904/report/introduction/methodology_nfv_service_density.html
title: "FD.io CSIT Test Methodology: NFV Service Density"
date: 2019-06
CSIT-1904-nfv-density-results:
target: https://docs.fd.io/csit/rls1904/report/vpp_performance_tests/nf_service_density/index.html
title: "FD.io CSIT Test Results: NFV Service Density"
date: 2019-06
CNCF-CNF-Testbed-Results:
target: https://github.com/cncf/cnf-testbed/blob/master/comparison/doc/cncf-cnfs-results-summary.md
title: "CNCF CNF Testbed: NFV Service Density Benchmarking"
date: 2018-12
NFVbench:
target: https://opnfv-nfvbench.readthedocs.io/en/latest/testing/user/userguide/readme.html
title: NFVbench Data Plane Performance Measurement Features
date: 2019-07
--- abstract
Network Function Virtualization (NFV) system designers and operators
continuously grapple with the problem of qualifying performance of
network services realised with software Network Functions (NF) running
on Commercial-Off-The-Shelf (COTS) servers. One of the main challenges
is getting repeatable and portable benchmarking results and using them
to derive deterministic operating range that is production deployment
worthy.
This document specifies benchmarking methodology for NFV services that
aims to address this problem space. It defines a way for measuring
performance of multiple NFV service instances, each composed of multiple
software NFs, and running them at a varied service “packing” density on
a single server.
The aim is to discover deterministic usage range of NFV system. In
addition specified methodology can be used to compare and contrast
different NFV virtualization technologies.
--- middle
# Terminology
* NFV: Network Function Virtualization, a general industry term
describing network functionality implemented in software.
* NFV service: a software based network service realized by a topology
of interconnected constituent software network function applications.
* NFV service instance: a single instantiation of NFV service.
* Data-plane optimized software: any software with dedicated threads
handling data-plane packet processing e.g. FD.io VPP (Vector Packet
Processor), OVS-DPDK.
* Packet Loss Ratio (PLR): ratio of packets received relative to packets
transmitted over the test trial duration, calculated using formula:
PLR = ( pkts_transmitted - pkts_received ) / pkts_transmitted.
For bi-directional throughput tests aggregate PLR is calculated based
on the aggregate number of packets transmitted and received.
* Packet Throughput Rate: maximum packet offered load DUT/SUT forwards
within the specified Packet Loss Ratio (PLR). In many cases the rate
depends on the frame size processed by DUT/SUT. Hence packet
throughput rate MUST be quoted with specific frame size as received by
DUT/SUT during the measurement. For bi-directional tests, packet
throughput rate should be reported as aggregate for both directions.
Measured in packets-per-second (pps) or frames-per-second (fps),
equivalent metrics.
* Non Drop Rate (NDR): maximum packet/bandwith throughput rate sustained
by DUT/SUT at PLR equal zero (zero packet loss) specific to tested
frame size(s). MUST be quoted with specific packet size as received by
DUT/SUT during the measurement. Packet NDR measured in
packets-per-second (or fps), bandwidth NDR expressed in
bits-per-second (bps).
* Partial Drop Rate (PDR): maximum packet/bandwith throughput rate
sustained by DUT/SUT at PLR greater than zero (non-zero packet loss)
specific to tested frame size(s). MUST be quoted with specific packet
size as received by DUT/SUT during the measurement. Packet PDR
measured in packets-per-second (or fps), bandwidth PDR expressed in
bits-per-second (bps).
* Maximum Receive Rate (MRR): packet/bandwidth rate regardless of PLR
sustained by DUT/SUT under specified Maximum Transmit Rate (MTR)
packet load offered by traffic generator. MUST be quoted with both
specific packet size and MTR as received by DUT/SUT during the
measurement. Packet MRR measured in packets-per-second (or fps),
bandwidth MRR expressed in bits-per-second (bps).
# Motivation
## Problem Description
Network Function Virtualization (NFV) system designers and operators
continuously grapple with the problem of qualifying performance of
network services realised with software Network Functions (NF) running
on Commercial-Off-The-Shelf (COTS) servers. One of the main challenges
is getting repeatable and portable benchmarking results and using them
to derive deterministic operating range that is production deployment
worthy.
Lack of well defined and standardised NFV centric performance
methodology and metrics makes it hard to address fundamental questions
that underpin NFV production deployments:
1. What NFV service and how many instances can run on a single compute
node?
2. How to choose the best compute resource allocation scheme to maximise
service yield per node?
3. How do different NF applications compare from the service density
perspective?
4. How do the virtualisation technologies compare e.g. Virtual Machines,
Containers?
Getting answers to these points should allow designers to make data
based decisions about the NFV technology and service design best suited
to meet requirements of their use cases. Thereby obtained benchmarking
data would aid in selection of the most appropriate NFV infrastructure
design and platform and enable more accurate capacity planning, an
important element for commercial viability of the NFV service.
## Proposed Solution
The primary goal of the proposed benchmarking methodology is to focus on
NFV technologies used to construct NFV services. More specifically to i)
measure packet data-plane performance of multiple NFV service instances
while running them at varied service “packing” densities on a single
server and ii) quantify the impact of using multiple NFs to construct
each NFV service instance and introducing multiple packet processing
hops and links on each packet path.
The overarching aim is to discover a set of deterministic usage ranges
that are of interest to NFV system designers and operators. In addition,
specified methodology can be used to compare and contrast different NFV
virtualisation technologies.
In order to ensure wide applicability of the benchmarking methodology,
the approach is to separate NFV service packet processing from the
shared virtualisation infrastructure by decomposing the software
technology stack into three building blocks:
+-------------------------------+
| NFV Service |
+-------------------------------+
| Virtualization Technology |
+-------------------------------+
| Host Networking |
+-------------------------------+
Figure 1. NFV software technology stack.
Proposed methodology is complementary to existing NFV benchmarking
industry efforts focusing on vSwitch benchmarking [RFC8204], [TST009]
and extends the benchmarking scope to NFV services.
This document does not describe a complete benchmarking methodology,
instead it is focusing on the system under test configuration. Each of
the compute node configurations identified in this document is
to be evaluated for NFV service data-plane performance using existing
and/or emerging network benchmarking standards. This may include
methodologies specified in [RFC2544], [TST009],
[draft-vpolak-mkonstan-bmwg-mlrsearch] and/or
[draft-vpolak-bmwg-plrsearch].
# NFV Service
It is assumed that each NFV service instance is built of one or more
constituent NFs and is described by: topology, configuration and
resulting packet path(s).
Each set of NFs forms an independent NFV service instance, with multiple
sets present in the host.
## Topology
NFV topology describes the number of network functions per service
instance, and their inter-connections over packet interfaces. It
includes all point-to-point virtual packet links within the compute
node, Layer-2 Ethernet or Layer-3 IP, including the ones to host
networking data-plane.
Theoretically, a large set of possible NFV topologies can be realised
using software virtualisation topologies, e.g. ring, partial -/full-
mesh, star, line, tree, ladder. In practice however, only a few
topologies are in the actual use as NFV services mostly perform either
bumps-in-a-wire packet operations (e.g. security filtering/inspection,
monitoring/telemetry) and/or inter-site forwarding decisions (e.g.
routing, switching).
Two main NFV topologies have been identified so far for NFV service
density benchmarking:
1. Chain topology: a set of NFs connect to host data-plane with minimum
of two virtual interfaces each, enabling host data-plane to
facilitate NF to NF service chain forwarding and provide connectivity
with external network.
2. Pipeline topology: a set of NFs connect to each other in a line
fashion with edge NFs homed to host data-plane. Host data-plane
provides connectivity with external network.
In both cases multiple NFV service topologies are running in parallel.
Both topologies are shown in figures 2. and 3. below.
NF chain topology:
+-----------------------------------------------------------+
| Host Compute Node |
| |
| SmNF1 SmNF2 SmNFn Service-m |
| ... ... ... ... |
| S2NF1 S2NF2 S2NFn Service-2 |
| +--------+ +--------+ +--------+ |
| | S1NF1 | | S1NF2 | | S1NFn | |
| | | | | .... | | Service-1 |
| | | | | | | |
| +-+----+-+ +-+----+-+ + + +-+----+-+ |
| | | | | | | | | Virtual |
| | |<-CS->| |<-CS->| |<-CS->| | Interfaces |
| +-+----+------+----+------+----+------+----+-+ |
| | | CS: Chain |
| | | Segment |
| | Host Data-Plane | |
| +-+--+----------------------------------+--+-+ |
| | | | | |
+-----------------------------------------------------------+
| | | | Physical
| | | | Interfaces
+---+--+----------------------------------+--+--------------+
| |
| Traffic Generator |
| |
+-----------------------------------------------------------+
Figure 2. NF chain topology forming a service instance.
NF pipeline topology:
+-----------------------------------------------------------+
| Host Compute Node |
| |
| SmNF1 SmNF2 SmNFn Service-m |
| ... ... ... ... |
| S2NF1 S2NF2 S2NFn Service-2 |
| +--------+ +--------+ +--------+ |
| | S1NF1 | | S1NF2 | | S1NFn | |
| | +--+ +--+ .... +--+ | Service1 |
| | | | | | | |
| +-+------+ +--------+ +------+-+ |
| | | Virtual |
| |<-Pipeline Edge Pipeline Edge->| Interfaces |
| +-+----------------------------------------+-+ |
| | | |
| | | |
| | Host Data-Plane | |
| +-+--+----------------------------------+--+-+ |
| | | | | |
+-----------------------------------------------------------+
| | | | Physical
| | | | Interfaces
+---+--+----------------------------------+--+--------------+
| |
| Traffic Generator |
| |
+-----------------------------------------------------------+
Figure 3. NF pipeline topology forming a service instance.
## Configuration
NFV configuration includes all packet processing functions in NFs
including Layer-2, Layer-3 and/or Layer-4-to-7 processing as appropriate
to specific NF and NFV service design. L2 sub- interface encapsulations
(e.g. 802.1q, 802.1ad) and IP overlay encapsulation (e.g. VXLAN, IPSec,
GRE) may be represented here too as appropriate, although in most cases
they are used as external encapsulation and handled by host networking
data-plane.
NFV configuration determines logical network connectivity that is
Layer-2 and/or IPv4/IPv6 switching/routing modes, as well as NFV service
specific aspects. In the context of NFV density benchmarking methodology
the initial focus is on logical network connectivity between the NFs,
and no NFV service specific configurations. NF specific functionality is
emulated using IPv4/IPv6 routing.
Building on the two identified NFV topologies, two common NFV
configurations are considered:
1. Chain configuration:
* Relies on chain topology to form NFV service chains.
* NF packet forwarding designs:
* IPv4/IPv6 routing.
* Requirements for host data-plane:
* L2 switching with L2 forwarding context per each NF chain
segment, or
* IPv4/IPv6 routing with IP forwarding context per each NF chain
segment or per NF chain.
2. Pipeline configuration:
* Relies on pipeline topology to form NFV service pipelines.
* Packet forwarding designs:
* IPv4/IPv6 routing.
* Requirements for host data-plane:
* L2 switching with L2 forwarding context per each NF pipeline
edge link, or
* IPv4/IPv6 routing with IP forwarding context per each NF pipeline
edge link or per NF pipeline.
## Packet Path(s)
NFV packet path(s) describe the actual packet forwarding path(s) used
for benchmarking, resulting from NFV topology and configuration. They
are aimed to resemble true packet forwarding actions during the NFV
service lifecycle.
Based on the specified NFV topologies and configurations two NFV packet
paths are taken for benchmarking:
1. Snake packet path
* Requires chain topology and configuration.
* Packets enter the NFV chain through one edge NF and progress to the
other edge NF of the chain.
* Within the chain, packets follow a zigzagging "snake" path entering
and leaving host data-plane as they progress through the NF chain.
* Host data-plane is involved in packet forwarding operations between
NIC interfaces and edge NFs, as well as between NFs in the chain.
2. Pipeline packet path
* Requires pipeline topology and configuration.
* Packets enter the NFV chain through one edge NF and progress to the
other edge NF of the pipeline.
* Within the chain, packets follow a straight path entering and
leaving subsequent NFs as they progress through the NF pipeline.
* Host data-plane is involved in packet forwarding operations between
NIC interfaces and edge NFs only.
Both packet paths are shown in figures below.
Snake packet path:
+-----------------------------------------------------------+
| Host Compute Node |
| |
| SmNF1 SmNF2 SmNFn Service-m |
| ... ... ... ... |
| S2NF1 S2NF2 S2NFn Service-2 |
| +--------+ +--------+ +--------+ |
| | S1NF1 | | S1NF2 | | S1NFn | |
| | | | | .... | | Service1 |
| | XXXX | | XXXX | | XXXX | |
| +-+X--X+-+ +-+X--X+-+ +X X+ +-+X--X+-+ |
| |X X| |X X| |X X| |X X| Virtual |
| |X X| |X X| |X X| |X X| Interfaces |
| +-+X--X+------+X--X+------+X--X+------+X--X+-+ |
| | X XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX X | |
| | X X | |
| | X Host Data-Plane X | |
| +-+X-+----------------------------------+-X+-+ |
| |X | | X| |
+----X--------------------------------------X---------------+
|X | | X| Physical
|X | | X| Interfaces
+---+X-+----------------------------------+-X+--------------+
| |
| Traffic Generator |
| |
+-----------------------------------------------------------+
Figure 4. Snake packet path thru NF chain topology.
Pipeline packet path:
+-----------------------------------------------------------+
| Host Compute Node |
| |
| SmNF1 SmNF2 SmNFn Service-m |
| ... ... ... ... |
| S2NF1 S2NF2 S2NFn Service-2 |
| +--------+ +--------+ +--------+ |
| | S1NF1 | | S1NF2 | | S1NFn | |
| | +--+ +--+ .... +--+ | Service1 |
| | XXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXX | |
| +--X-----+ +--------+ +-----X--+ |
| |X X| Virtual |
| |X X| Interfaces |
| +-+X--------------------------------------X+-+ |
| | X X | |
| | X X | |
| | X Host Data-Plane X | |
| +-+X-+----------------------------------+-X+-+ |
| |X | | X| |
+----X--------------------------------------X---------------+
|X | | X| Physical
|X | | X| Interfaces
+---+X-+----------------------------------+-X+--------------+
| |
| Traffic Generator |
| |
+-----------------------------------------------------------+
Figure 5. Pipeline packet path thru NF pipeline topology.
In all cases packets enter NFV system via shared physical NIC interfaces
controlled by shared host data-plane, are then associated with specific
NFV service (based on service discriminator) and subsequently are cross-
connected/switched/routed by host data-plane to and through NF
topologies per one of the above listed schemes.
# Virtualization Technology
NFV services are built of composite isolated NFs, with virtualisation
technology providing the workload isolation. Following virtualisation
technology types are considered for NFV service density benchmarking:
1. Virtual Machines (VMs)
* Relying on host hypervisor technology e.g. KVM, ESXi, Xen.
* NFs running in VMs are referred to as VNFs.
2. Containers
* Relying on Linux container technology e.g. LXC, Docker.
* NFs running in Containers are referred to as CNFs.
Different virtual interface types are available to VNFs and CNFs:
1. VNF
* virtio-vhostuser: fully user-mode based virtual interface.
* virtio-vhostnet: involves kernel-mode based backend.
2. CNF
* memif: fully user-mode based virtual interface.
* af_packet: involves kernel-mode based backend.
* (add more common ones)
# Host Networking
Host networking data-plane is the central shared resource that underpins
creation of NFV services. It handles all of the connectivity to external
physical network devices through physical network connections using
NICs, through which the benchmarking is done.
Assuming that NIC interface resources are shared, here is the list of
widely available host data-plane options for providing packet
connectivity to/from NICs and constructing NFV chain and pipeline
topologies and configurations:
* Linux Kernel-Mode Networking.
* Linux User-Mode vSwitch.
* Virtual Machine vSwitch.
* Linux Container vSwitch.
* SRIOV NIC Virtual Function - note: restricted support for chain and
pipeline topologies, as it requires hair-pinning through the NIC and
oftentimes also through external physical switch.
Analysing properties of each of these options and their Pros/Cons for
specified NFV topologies and configurations is outside the scope of this
document.
From all listed options, performance optimised Linux user-mode vswitch
deserves special attention. Linux user-mode switch decouples NFV service
from the underlying NIC hardware, offers rich multi-tenant functionality
and most flexibility for supporting NFV services. But in the same time
it is consuming compute resources and is harder to benchmark in NFV
service density scenarios.
Following sections focus on using Linux user-mode vSwitch, focusing on
its performance benchmarking at increasing levels of NFV service
density.
# NFV Service Density Matrix
In order to evaluate performance of multiple NFV services running on a
compute node, NFV service instances are benchmarked at increasing
density, allowing to construct an NFV Service Density Matrix. Table
below shows an example of such a matrix, capturing number of NFV service
instances (row indices), number of NFs per service instance (column
indices) and resulting total number of NFs (values).
NFV Service Density - NF Count View
SVC 001 002 004 006 008 00N
001 1 2 4 6 8 1*N
002 2 4 8 12 16 2*N
004 4 8 16 24 32 4*N
006 6 12 24 36 48 6*N
008 8 16 32 48 64 8*N
00M M*1 M*2 M*4 M*6 M*8 M*N
RowIndex: Number of NFV Service Instances, 1..M.
ColumnIndex: Number of NFs per NFV Service Instance, 1..N.
Value: Total number of NFs running in the system.
In order to deliver good and repeatable network data-plane performance,
NFs and host data-plane software require direct access to critical
compute resources. Due to a shared nature of all resources on a compute
node, a clearly defined resource allocation scheme is defined in the
next section to address this.
In each tested configuration host data-plane is a gateway between the
external network and the internal NFV network topologies. Offered packet
load is generated and received by an external traffic generator per
usual benchmarking practice.
It is proposed that benchmarks are done with the offered packet load
distributed equally across all configured NFV service instances.
This approach should provide representative benchmarking data for each
tested topology and configuraiton, and a good guesstimate of maximum
performance required for capacity planning.
Following sections specify compute resource allocation, followed by
examples of applying NFV service density methodology to VNF and CNF
benchmarking use cases.
# Compute Resource Allocation
Performance optimized NF and host data-plane software threads require
timely execution of packet processing instructions and are very
sensitive to any interruptions (or stalls) to this execution e.g. cpu
core context switching, or cpu jitter. To that end, NFV service density
methodology treats controlled mapping ratios of data plane software
threads to physical processor cores with directly allocated cache
hierarchies as the first order requirement.
Other compute resources including memory bandwidth and PCIe bandwidth
have lesser impact and as such are subject for further study. For more
detail and deep-dive analysis of software data plane performance and
impact on different shared compute resources is available in [BSDP].
It is assumed that NFs as well as host data-plane (e.g. vswitch) are
performance optimized, with their tasks executed in two types of
software threads:
* data-plane - handling data-plane packet processing and forwarding,
time critical, requires dedicated cores. To scale data-plane
performance, most NF apps use multiple data-plane threads and rely on
NIC RSS (Receive Side Scaling), virtual interface multi-queue and/or
integrated software hashing to distribute packets across the data
threads.
* main-control - handling application management, statistics and
control-planes, less time critical, allows for core sharing. For most
NF apps this is a single main thread, but often statistics (counters)
and various control protocol software are run in separate threads.
Core mapping scheme described below allocates cores for all threads of
specified type belonging to each NF app instance, and separately lists
number of threads to a number of logical/physical core mappings for
processor configurations with enabled/disabled Symmetric Multi-
Threading (SMT) (e.g. AMD SMT, Intel Hyper-Threading).
If NFV service density benchmarking is run on server nodes with
Symmetric Multi-Threading (SMT) (e.g. AMD SMT, Intel Hyper-Threading)
for higher performance and efficiency, logical cores allocated to data-
plane threads should be allocated as pairs of sibling logical cores
corresponding to the hyper-threads running on the same physical core.
Separate core ratios are defined for mapping threads of vSwitch and NFs.
In order to get consistent benchmarking results, the mapping ratios are
enforced using Linux core pinning.
| application | thread type | app:core ratio | threads/pcores (SMT disabled) | threads/lcores map (SMT enabled) |
|:-----------:|:-----------:|:--------------:|:-------------------------------:|:----------------------------------:|
| vSwitch-1c | data | 1:1 | 1DT/1PC | 2DT/2LC |
| | main | 1:S2 | 1MT/S2PC | 1MT/1LC |
| | | | | |
| vSwitch-2c | data | 1:2 | 2DT/2PC | 4DT/4LC |
| | main | 1:S2 | 1MT/S2PC | 1MT/1LC |
| | | | | |
| vSwitch-4c | data | 1:4 | 4DT/4PC | 8DT/8LC |
| | main | 1:S2 | 1MT/S2PC | 1MT/1LC |
| | | | | |
| NF-0.5c | data | 1:S2 | 1DT/S2PC | 1DT/1LC |
| | main | 1:S2 | 1MT/S2PC | 1MT/1LC |
| | | | | |
| NF-1c | data | 1:1 | 1DT/1PC | 2DT/2LC |
| | main | 1:S2 | 1MT/S2PC | 1MT/1LC |
| | | | | |
| NF-2c | data | 1:2 | 2DT/2PC | 4DT/4LC |
| | main | 1:S2 | 1MT/S2PC | 1MT/1LC |
* Legend to table
* Header row
* application - network application with optimized data-plane, a
vSwitch or Network Function (NF) application.
* thread type - either "data", short for data-plane; or "main",
short for all main-control threads.
* app:core ratio - ratio of per application instance threads of
specific thread type to physical cores.
* threads/pcores (SMT disabled) - number of threads of specific
type (DT for data-plane thread, MT for main thread) running on a
number of physical cores, with SMT disabled.
* threads/lcores map (SMT enabled) - number of threads of specific
type (DT, MT) running on a number of logical cores, with SMT
enabled. Two logical cores per one physical core.
* Content rows
* vSwitch-(1c|2c|4c) - vSwitch with 1 physical core (or 2, or 4)
allocated to its data-plane software worker threads.
* NF-(0.5c|1c|2c) - NF application with half of a physical core (or
1, or 2) allocated to its data-plane software worker threads.
* Sn - shared core, sharing ratio of (n).
* DT - data-plane thread.
* MT - main-control thread.
* PC - physical core, with SMT/HT enabled has many (mostly 2 today)
logical cores associated with it.
* LC - logical core, if more than one lc get allocated in sets of
two sibling logical cores running on the same physical core.
* SnPC - shared physical core, sharing ratio of (n).
* SnLC - shared logical core, sharing ratio of (n).
Maximum benchmarked NFV service densities are limited by a number of
physical cores on a compute node.
A sample physical core usage view is shown in the matrix below.
NFV Service Density - Core Usage View
vSwitch-1c, NF-1c
SVC 001 002 004 006 008 010
001 2 3 6 9 12 15
002 3 6 12 18 24 30
004 6 12 24 36 48 60
006 9 18 36 54 72 90
008 12 24 48 72 96 120
010 15 30 60 90 120 150
RowIndex: Number of NFV Service Instances, 1..10.
ColumnIndex: Number of NFs per NFV Service Instance, 1..10.
Value: Total number of physical processor cores used for NFs.
# NFV Service Data-Plane Benchmarking
NF service density scenarios should have their data-plane performance
benchmarked using existing and/or emerging network benchmarking
standards as noted earlier.
Following metrics should be measured (or calculated) and reported:
* Packet throughput rate (packets-per-second)
* Specific to tested packet size or packet sequence (e.g. some type of
packet size mix sent in recurrent sequence).
* Applicable types of throughput rate: NDR, PDR, MRR.
* (Calculated) Bandwidth throughput rate (bits-per-second) corresponding
to the measured packet throughput rate.
* Packet one-way latency (seconds)
* Measured at different packet throughput rates load e.g. light,
medium, heavy.
Listed metrics should be itemized per service instance and per direction
(e.g. forward/reverse) for latency.
# Sample NFV Service Density Benchmarks
To illustrate defined NFV service density applicability, following
sections describe three sets of NFV service topologies and
configurations that have been benchmarked in open-source: i) in
[LFN-FDio-CSIT], a continuous testing and data-plane benchmarking
project, ii) as part of CNCF CNF Testbed initiative [CNCF-CNF-Testbed]
and iii) in OPNFV NFVbench project.
In the first two cases each NFV service instance definition is based on
the same set of NF applications, and varies only by network addressing
configuration to emulate multi-tenant operating environment.
OPNFV NFVbench project is focusing on benchmarking the actual production
deployments that are aligned with OPNFV specifications.
## Intrepreting the Sample Results
TODO How to interpret and avoid misreading included results? And how to
avoid falling into the trap of using these results to draw generilized
conclusions about performance of different virtualization technologies,
e.g. VM and Containers, irrespective of deployment scenarios and what
VNFs and CNFs are in the actual use.
## Benchmarking MRR Throughput
Initial NFV density throughput benchmarks have been performed using
Maximum Receive Rate (MRR) test methodology defined and used in FD.io
CSIT.
MRR tests measure the packet forwarding rate under specified Maximum
Transmit Rate (MTR) packet load offered by traffic generator over a set
trial duration, regardless of packet loss ratio (PLR). MTR for specified
Ethernet frame size was set to the bi-directional link rate, 2x 10GbE in
referred results.
Tests were conducted with two traffic profiles: i) continuous stream of
64B frames, ii) continuous stream of IMIX sequence of (7x 64B, 4x 570B,
1x 1518B), all sizes are L2 untagged Ethernet.
NFV service topologies tested include: VNF service chains, CNF service
chains and CNF service pipelines.
## VNF Service Chain
VNF Service Chain (VSC) topology is tested with KVM hypervisor (Ubuntu
18.04-LTS), with NFV service instances consisting of NFs running in VMs
(VNFs). Host data-plane is provided by FD.io VPP vswitch. Virtual
interfaces are virtio-vhostuser. Snake forwarding packet path is tested
using [TRex] traffic generator, see figure.
+-----------------------------------------------------------+
| Host Compute Node |
| |
| +--------+ +--------+ +--------+ |
| | S1VNF1 | | S1VNF2 | | S1VNFn | |
| | | | | .... | | Service1 |
| | XXXX | | XXXX | | XXXX | |
| +-+X--X+-+ +-+X--X+-+ +-+X--X+-+ |
| |X X| |X X| |X X| Virtual |
| |X X| |X X| |X X| |X X| Interfaces |
| +-+X--X+------+X--X+------+X--X+------+X--X+-+ |
| | X XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX X | |
| | X X | |
| | X FD.io VPP vSwitch X | |
| +-+X-+----------------------------------+-X+-+ |
| |X | | X| |
+----X--------------------------------------X---------------+
|X | | X| Physical
|X | | X| Interfaces
+---+X-+----------------------------------+-X+--------------+
| |
| Traffic Generator (TRex) |
| |
+-----------------------------------------------------------+
Figure 6. VNF service chain test setup.
## CNF Service Chain
CNF Service Chain (CSC) topology is tested with Docker containers
(Ubuntu 18.04-LTS), with NFV service instances consisting of NFs running
in Containers (CNFs). Host data-plane is provided by FD.io VPP vswitch.
Virtual interfaces are memif. Snake forwarding packet path is tested
using [TRex] traffic generator, see figure.
+-----------------------------------------------------------+
| Host Compute Node |
| |
| +--------+ +--------+ +--------+ |
| | S1CNF1 | | S1CNF2 | | S1CNFn | |
| | | | | .... | | Service1 |
| | XXXX | | XXXX | | XXXX | |
| +-+X--X+-+ +-+X--X+-+ +-+X--X+-+ |
| |X X| |X X| |X X| Virtual |
| |X X| |X X| |X X| |X X| Interfaces |
| +-+X--X+------+X--X+------+X--X+------+X--X+-+ |
| | X XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX X | |
| | X X | |
| | X FD.io VPP vSwitch X | |
| +-+X-+----------------------------------+-X+-+ |
| |X | | X| |
+----X--------------------------------------X---------------+
|X | | X| Physical
|X | | X| Interfaces
+---+X-+----------------------------------+-X+--------------+
| |
| Traffic Generator (TRex) |
| |
+-----------------------------------------------------------+
Figure 7. CNF service chain test setup.
## CNF Service Pipeline
CNF Service Pipeline (CSP) topology is tested with Docker containers
(Ubuntu 18.04-LTS), with NFV service instances consisting of NFs running
in Containers (CNFs). Host data-plane is provided by FD.io VPP vswitch.
Virtual interfaces are memif. Pipeline forwarding packet path is tested
using [TRex] traffic generator, see figure.
+-----------------------------------------------------------+
| Host Compute Node |
| |
| +--------+ +--------+ +--------+ |
| | S1NF1 | | S1NF2 | | S1NFn | |
| | +--+ +--+ .... +--+ | Service1 |
| | XXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXX | |
| +--X-----+ +--------+ +-----X--+ |
| |X X| Virtual |
| |X X| Interfaces |
| +-+X--------------------------------------X+-+ |
| | X X | |
| | X X | |
| | X FD.io VPP vSwitch X | |
| +-+X-+----------------------------------+-X+-+ |
| |X | | X| |
+----X--------------------------------------X---------------+
|X | | X| Physical
|X | | X| Interfaces
+---+X-+----------------------------------+-X+--------------+
| |
| Traffic Generator (TRex) |
| |
+-----------------------------------------------------------+
Figure 8. CNF service chain test setup.
## Sample Results: FD.io CSIT
FD.io CSIT project introduced NFV density benchmarking in release
CSIT-1904 and published results for the following NFV service topologies
and configurations:
1. VNF Service Chains
* VNF: DPDK-L3FWD v19.02
* IPv4 forwarding
* NF-1c
* vSwitch: VPP v19.04-release
* L2 MAC switching
* vSwitch-1c, vSwitch-2c
* frame sizes: 64B, IMIX
2. CNF Service Chains
* CNF: VPP v19.04-release
* IPv4 routing
* NF-1c
* vSwitch: VPP v19.04-release
* L2 MAC switching
* vSwitch-1c, vSwitch-2c
* frame sizes: 64B, IMIX
3. CNF Service Pipelines
* CNF: VPP v19.04-release
* IPv4 routing
* NF-1c
* vSwitch: VPP v19.04-release
* L2 MAC switching
* vSwitch-1c, vSwitch-2c
* frame sizes: 64B, IMIX
More information is available in FD.io CSIT-1904 report, with specific
references listed below:
* Testbed: [CSIT-1904-testbed-2n-skx]
* Test environment: [CSIT-1904-test-enviroment]
* Methodology: [CSIT-1904-nfv-density-methodology]
* Results: [CSIT-1904-nfv-density-results]
## Sample Results: CNCF/CNFs
CNCF CI team introduced a CNF testbed initiative focusing on benchmaring
NFV density with open-source network applications running as VNFs and
CNFs. Following NFV service topologies and configurations have been
tested to date:
1. VNF Service Chains
* VNF: VPP v18.10-release
* IPv4 routing
* NF-1c
* vSwitch: VPP v18.10-release
* L2 MAC switching
* vSwitch-1c, vSwitch-2c
* frame sizes: 64B, IMIX
2. CNF Service Chains
* CNF: VPP v18.10-release
* IPv4 routing
* NF-1c
* vSwitch: VPP v18.10-release
* L2 MAC switching
* vSwitch-1c, vSwitch-2c
* frame sizes: 64B, IMIX
3. CNF Service Pipelines
* CNF: VPP v18.10-release
* IPv4 routing
* NF-1c
* vSwitch: VPP v18.10-release
* L2 MAC switching
* vSwitch-1c, vSwitch-2c
* frame sizes: 64B, IMIX
More information is available in CNCF CNF Testbed github, with summary
test results presented in summary markdown file, references listed
below:
* Results: [CNCF-CNF-Testbed-Results]
## Sample Results: OPNFV NFVbench
TODO Add short NFVbench based test description, and NFVbench sweep chart
with single VM per service instance: Y-axis packet throughput rate or
bandwidth throughput rate, X-axis number of concurrent service
instances.
# IANA Considerations
No requests of IANA.
# Security Considerations
Benchmarking activities as described in this memo are limited to
technology characterization of a DUT/SUT using controlled stimuli in a
laboratory environment, with dedicated address space and the constraints
specified in the sections above.
The benchmarking network topology will be an independent test setup and
MUST NOT be connected to devices that may forward the test traffic into
a production network or misroute traffic to the test management network.
Further, benchmarking is performed on a "black-box" basis, relying
solely on measurements observable external to the DUT/SUT.
Special capabilities SHOULD NOT exist in the DUT/SUT specifically for
benchmarking purposes. Any implications for network security arising
from the DUT/SUT SHOULD be identical in the lab and in production
networks.
# Acknowledgements
Thanks to Vratko Polak of FD.io CSIT project and Michael Pedersen of the
CNCF Testbed initiative for their contributions and useful suggestions.
Extended thanks to Alec Hothan of OPNFV NFVbench project for numerous
comments, suggestions and references to his/team work in the
OPNFV/NVFbench project.
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