BMWG R. Rosa, Ed.
Internet-Draft C. Rothenberg
Intended status: Informational UNICAMP
Expires: December 26, 2019 M. Peuster
H. Karl
UPB
June 24, 2019
Methodology for VNF Benchmarking Automation
draft-rosa-bmwg-vnfbench-04
Abstract
This document describes a common methodology for the automated
benchmarking of Virtualized Network Functions (VNFs) executed on
general-purpose hardware. Specific cases of automated benchmarking
methodologies for particular VNFs can be derived from this document.
Two open source reference implementations are reported as running
code embodiments of the proposed, automated benchmarking methodology.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
working documents as Internet-Drafts. The list of current Internet-
Drafts is at https://datatracker.ietf.org/drafts/current/.
Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on December 26, 2019.
Copyright Notice
Copyright (c) 2019 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(https://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents
carefully, as they describe your rights and restrictions with respect
Rosa, et al. Expires December 26, 2019 [Page 1]
Internet-Draft VNFBench June 2019
to this document. Code Components extracted from this document must
include Simplified BSD License text as described in Section 4.e of
the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 4
3. Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
4. Considerations . . . . . . . . . . . . . . . . . . . . . . . 4
4.1. VNF Testing Methods . . . . . . . . . . . . . . . . . . . . 5
4.2. Benchmarking Procedures . . . . . . . . . . . . . . . . . . 5
4.2.1. Phase I: Deployment . . . . . . . . . . . . . . . . . . . 6
4.2.2. Phase II: Configuration . . . . . . . . . . . . . . . . . 6
4.2.3. Phase III: Execution . . . . . . . . . . . . . . . . . . 6
4.2.4. Phase IV: Report . . . . . . . . . . . . . . . . . . . . 7
5. Generic VNF Benchmarking Architectural Framework . . . . . . 7
5.1. Deployment Scenarios . . . . . . . . . . . . . . . . . . . 10
6. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . 10
6.1. VNF Benchmarking Descriptor (VNF-BD) . . . . . . . . . . . 12
6.1.1. Descriptor Headers . . . . . . . . . . . . . . . . . . . 12
6.1.2. Target Information . . . . . . . . . . . . . . . . . . . 12
6.1.3. Experiments . . . . . . . . . . . . . . . . . . . . . . . 12
6.1.4. Environment . . . . . . . . . . . . . . . . . . . . . . . 12
6.1.5. Scenario . . . . . . . . . . . . . . . . . . . . . . . . 13
6.1.6. Proceedings . . . . . . . . . . . . . . . . . . . . . . . 14
6.2. VNF Performance Profile (VNF-PP) . . . . . . . . . . . . . 14
6.2.1. Execution Environment . . . . . . . . . . . . . . . . . . 14
6.2.2. Measurement Results . . . . . . . . . . . . . . . . . . . 15
6.3. Procedures . . . . . . . . . . . . . . . . . . . . . . . . 16
6.3.1. Pre-Execution . . . . . . . . . . . . . . . . . . . . . . 16
6.3.2. Automated Execution . . . . . . . . . . . . . . . . . . . 17
6.3.3. Post-Execution . . . . . . . . . . . . . . . . . . . . . 18
6.4. Particular Cases . . . . . . . . . . . . . . . . . . . . . 18
6.4.1. Capacity . . . . . . . . . . . . . . . . . . . . . . . . 18
6.4.2. Isolation . . . . . . . . . . . . . . . . . . . . . . . . 19
6.4.3. Failure Handling . . . . . . . . . . . . . . . . . . . . 19
6.4.4. Elasticity and Flexibility . . . . . . . . . . . . . . . 19
6.4.5. Handling Configurations . . . . . . . . . . . . . . . . . 19
6.4.6. White Box VNF . . . . . . . . . . . . . . . . . . . . . . 19
7. Open Source Reference Implementations . . . . . . . . . . . . 20
7.1. Gym . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
7.2. tng-bench . . . . . . . . . . . . . . . . . . . . . . . . . 21
8. Security Considerations . . . . . . . . . . . . . . . . . . . 22
9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 22
10. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 22
11. References . . . . . . . . . . . . . . . . . . . . . . . . . 22
Rosa, et al. Expires December 26, 2019 [Page 2]
Internet-Draft VNFBench June 2019
11.1. Normative References . . . . . . . . . . . . . . . . . . . 22
11.2. Informative References . . . . . . . . . . . . . . . . . . 23
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 24
1. Introduction
The Benchmarking Methodology Working Group (BMWG) already presented
considerations for benchmarking of VNFs and their infrastructure in
[RFC8172]. Similar to the motivation given in [RFC8172], the
following aspects justify the need for VNF benchmarking: (i) pre-
deployment infrastructure dimensioning to realize associated VNF
performance profiles; (ii) comparison factor with physical network
functions; (iii) and output results for analytical VNF development.
Even if many methodologies already described by the BMWG, e.g., self-
contained black-box benchmarking, can be applied to VNF benchmarking
scenarios, further considerations have to be made. This is, on the
one hand, because VNFs, which are software components, do not have
strict and clear execution boundaries and depend on underlying
virtualization environment parameters as well as management and
orchestration decisions [ETS14a]. On the other hand, can and should
the flexible, software-based nature of VNFs be exploited to fully
automate the entire benchmarking procedure end-to-end. This is an
inherent need to align VNF benchmarking with the agile methods
enabled by the concept of Network Functions Virtualization (NFV)
[ETS14e]. More specifically it allows: (i) the development of agile
performance-focused DevOps methodologies for Continuous Integration
and Delivery (CI/CD) of VNFs; (ii) the creation of on-demand VNF test
descriptors for upcoming execution environments; (iii) the path for
precise-analytics of automated catalogues of VNF performance
profiles; (iv) and run-time mechanisms to assist VNF lifecycle
orchestration/management workflows, e.g., automated resource
dimensioning based on benchmarking insights.
This document describes basic methodologies and guidelines to fully
automate VNF benchmarking procedures, without limiting the automated
process to a specific benchmark or infrastructure. After presenting
initial considerations, the document first describes a generic
architectural framework to setup automated benchmarking experiments.
Second, the automation methodology is discussed, with a particular
focus on experiment and procedure description approaches to support
reproducibility of the automated benchmarks, a key challenge in VNF
benchmarking. Finally, two independent, open-source reference
implementations are presented. The document addresses state-of-the-
art work on VNF benchmarking from scientific publications and current
developments in other standardization bodies (e.g., [ETS14c] and
[RFC8204]) wherever possible.
Rosa, et al. Expires December 26, 2019 [Page 3]
Internet-Draft VNFBench June 2019
2. Terminology
Common benchmarking terminology contained in this document is derived
from [RFC1242]. The reader is assumed to be familiar with the
terminology as defined in the European Telecommunications Standards
Institute (ETSI) NFV document [ETS14b]. Some of these terms, and
others commonly used in this document, are defined below.
NFV: Network Function Virtualization - the principle of separating
network functions from the hardware they run on by using virtual
hardware abstraction.
VNF: Virtualized Network Function - a software-based network
function. A VNF can be either represented by a single entity or
be composed by a set of smaller, interconnected software
components, called VNF components (VNFCs) [ETS14d]. Those VNFs
are also called composed VNFs.
VNFC: Virtualized Network Function Component - a software component
that implements (parts of) the VNF functionality. A VNF can
consist of a single VNFC or multiple, interconnected VNFCs
[ETS14d]
VNFD: Virtualised Network Function Descriptor - configuration
template that describes a VNF in terms of its deployment and
operational behaviour, and is used in the process of VNF on-
boarding and managing the life cycle of a VNF instance.
NS: Network Service - a collection of interconnected VNFs forming a
end-to-end service. The interconnection is often done using
chaining of functions.
3. Scope
This document assumes VNFs as black boxes when defining their
benchmarking methodologies. White box approaches are assumed and
analysed as a particular case under the proper considerations of
internal VNF instrumentation, later discussed in this document.
This document outlines a methodology for VNF benchmarking,
specifically addressing its automation.
4. Considerations
VNF benchmarking considerations are defined in [RFC8172].
Additionally, VNF pre-deployment testing considerations are well
explored in [ETS14c].
Rosa, et al. Expires December 26, 2019 [Page 4]
Internet-Draft VNFBench June 2019
4.1. VNF Testing Methods
Following ETSI's model in [ETS14c], we distinguish three methods for
VNF evaluation:
Benchmarking: Where parameters (e.g., CPU, memory, storage) are
provided and the corresponding performance metrics (e.g., latency,
throughput) are obtained. Note, such evaluations might create
multiple reports, for example, with minimal latency or maximum
throughput results.
Verification: Both parameters and performance metrics are provided
and a stimulus verifies if the given association is correct or
not.
Dimensioning: Performance metrics are provided and the corresponding
parameters obtained. Note, multiple deployments may be required,
or if possible, underlying allocated resources need to be
dynamically altered.
Note: Verification and Dimensioning can be reduced to Benchmarking.
Therefore, we focus on Benchmarking in the rest of the document.
4.2. Benchmarking Procedures
A (automated) benchmarking procedure can be divided into three sub-
procedures:
Trial: Is a single process or iteration to obtain VNF performance
metrics from benchmarking measurements. A Test should always run
multiple Trials to get statistical confidence about the obtained
measurements.
Test: Defines unique structural and functional parameters (e.g.,
configurations, resource assignment) for benchmarked components to
perform one or multiple Trials. Each Test must be executed
following a particular benchmarking scenario composed by a Method.
Proper measures must be taken to ensure statistical validity
(e.g., independence across Trials of generated load patterns).
Method: Consists of one or more Tests to benchmark a VNF. A Method
can explicitly list ranges of parameter values for the
configuration of a benchmarking scenario and its components. Each
value of such a range is to be realized in a Test. I.e., Methods
can define parameter studies.
In general, automated VNF benchmarking Tests must capture relevant
causes of performance variability. To dissect a VNF benchmarking
Rosa, et al. Expires December 26, 2019 [Page 5]
Internet-Draft VNFBench June 2019
Test, in the sections that follow different benchmarking phases are
categorized defining generic operations that may be automated. When
automating a VNF benchmarking methodology, all the influencing
aspects on the performance of a VNF must be carefully analyzed and
comprehensively reported, in each phase of the overall benchmarking
process.
4.2.1. Phase I: Deployment
The placement (i.e., assignment and allocation of resources) and the
interconnection, physical and/or virtual, of network function(s) and
benchmarking components can be realized by orchestration platforms
(e.g., OpenStack, Kubernetes, Open Source MANO). In automated
manners, the realization of a benchmarking testbed/scenario through
those means usually rely on network service templates (e.g., TOSCA,
Heat, YANG). Such descriptors have to capture all relevant details
of the execution environment to allow the benchmarking framework to
correctly instantiate the SUT as well as helper functions required
for a Test.
4.2.2. Phase II: Configuration
The configuration of benchmarking components and VNFs (e.g., populate
routing table, load PCAP source files in source of traffic stimulus)
to execute the Test settings can be realized by programming
interfaces in an automated way. In the scope of NFV, there might
exist management interfaces to control a VNF during a benchmarking
Test. Likewise, infrastructure or orchestration components can
establish the proper configuration of an execution environment to
realize all the capabilities enabling the description of the
benchmarking Test. Each configuration registry, its deployment
timestamp and target, must all be contained in the VNF benchmarking
report.
4.2.3. Phase III: Execution
In the execution of a benchmarking Test, the VNF configuration can be
programmed to be changed by itself or by a VNF management platform.
It means that during a Trial execution, particular behaviors of a VNF
can be automatically triggered, e.g., auto-scaling of its internal
components. Those must be captured in the detailed procedures of the
VNF execution and its performance report. I.e., the execution of a
Trial can determine arrangements of internal states inside a VNF,
which can interfere in observed benchmarking metrics. For instance,
in a particular benchmarking case where the monitoring measurements
of the VNF and/or execution environment are available for extraction,
Tests should be run to verify if the monitoring of the VNF and/or
execution environment can impact the VNF performance metrics.
Rosa, et al. Expires December 26, 2019 [Page 6]
Internet-Draft VNFBench June 2019
4.2.4. Phase IV: Report
The report of a VNF benchmarking Method might contain generic metrics
(e.g., CPU and memory consumption) and VNF-specific traffic
processing metrics (e.g., transactions or throughput), which can be
stored and processed in generic or specific ways (e.g., by statistics
or machine learning algorithms). If automated procedures are applied
over the generation of a benchmarking report, those must be detailed
in the report itself, jointly with their input raw measurements and
output processed data. I.e., any algorithm used in the generation of
processed metrics must be disclosed in the report.
5. Generic VNF Benchmarking Architectural Framework
A generic VNF benchmarking architectural framework, shown in
Figure 1, establishes the disposal of essential components and
control interfaces, explained below, that enable the automation of
VNF benchmarking methodologies.
Rosa, et al. Expires December 26, 2019 [Page 7]
Internet-Draft VNFBench June 2019
+---------------+
| Manager |
Control | (Coordinator) |
Interface +---+-------+---+
+--------+-----------+ +-------------------+
| | |
| | +-------------------------+ |
| | | System Under Test | |
| | | | |
| | | +-----------------+ | |
| +--+------- + VNF | | |
| | | | | |
| | | +----+ +----+ | | |
| | | |VNFC|...|VNFC| | | |
| | | +----+ +----+ | | |
| | +----.---------.--+ | |
+-----+---+ | Monitor | : : | +-----+----+
| Agent | |{listeners}|----^---------V--+ | | Agent |
|(Sender) | | | Execution | | |(Receiver)|
| | | | Environment | | | |
|{Probers}| +-----------| | | |{Probers} |
+-----.---+ | +----.---------.--+ | +-----.----+
: +---------^---------V-----+ :
V : : :
:................>.....: :............>..:
Stimulus Traffic Flow
Figure 1: Generic VNF Benchmarking Setup
Virtualized Network Function (VNF) -- consists of one or more
software components, so called VNF components (VNFC), adequate for
performing a network function according to allocated virtual
resources and satisfied requirements in an execution environment.
A VNF can demand particular configurations for benchmarking
specifications, demonstrating variable performance based on
available virtual resources/parameters and configured enhancements
targeting specific technologies (e.g., NUMA, SR-IOV, CPU-Pinning).
Execution Environment -- defines a virtualized and controlled
composition of capabilities necessary for the execution of a VNF.
An execution environment stands as a general purpose level of
virtualization with abstracted resources available for one or more
VNFs. It can also define specific technology habilitation,
incurring in viable settings for enhancing the performance of
VNFs.
Rosa, et al. Expires December 26, 2019 [Page 8]
Internet-Draft VNFBench June 2019
Agent -- executes active stimulus using probers, i.e., benchmarking
tools, to benchmark and collect network and system performance
metrics. A single Agent can perform localized benchmarks in
execution environments (e.g., stress tests on CPU, memory, disk I/
O) or can generate stimulus traffic and the other end be the VNF
itself where, for example, one-way latency is evaluated. The
interaction among distributed Agents enable the generation and
collection of end-to-end metrics (e.g., frame loss rate, latency)
measured from stimulus traffic flowing through a VNF. An Agent
can be defined by a physical or virtual network function.
Prober -- defines an abstraction layer for a software or hardware
tool able to generate stimulus traffic to a VNF or perform
stress tests on execution environments. Probers might be
specific or generic to an execution environment or a VNF. For
an Agent, a Prober must provide programmable interfaces for its
life cycle management, e.g., configuration of operational
parameters, execution of stilumus, parsing of extracted
metrics, and debugging options. Specific Probers might be
developed to abstract and to realize the description of
particular VNF benchmarking methodologies.
Monitor -- when possible is instantiated inside the System Under
Test, VNF and/or infrastructure (e.g., as a plug-in process in a
virtualized execution environment), to perform the passive
monitoring, using Listeners, for the extraction of metrics while
Agents` stimuli takes place. Monitors observe particular
properties according to the execution environment and VNFs
capabilities, i.e., exposed passive monitoring interfaces.
Multiple Listeners can be executed at once in synchrony with a
Prober' stimulus on a SUT. A Monitor can be defined as a
virtualized network function.
Listener -- defines one or more software interfaces for the
extraction of metrics monitored in a target VNF and/or
execution environment. A Listener must provide programmable
interfaces for its life cycle management workflows, e.g.,
configuration of operational parameters, execution of passive
monitoring captures, parsing of extracted metrics, and
debugging options. Varied methods of passive performance
monitoring might be implemented as a Listener, depending on the
interfaces exposed by the VNF and/or execution environment.
Rosa, et al. Expires December 26, 2019 [Page 9]
Internet-Draft VNFBench June 2019
Manager -- performs (i) the discovery of available Agents/Monitors
and their respective features (i.e., available Probers/Listeners
and execution environment capabilities), (ii) the coordination and
synchronization of activities of Agents and Monitors to perform a
benchmarking Test, (iii) the collection, processing and
aggregation of all VNF benchmarking measurements that correlates
the VNF stimuli and the, possible, SUT monitored metrics. A
Manager executes the main configuration, operation, and management
actions to deliver the VNF benchmarking report. A Manager can be
defined as a physical or virtualized network function.
5.1. Deployment Scenarios
A deployment scenario realizes the actual instantiation of physical
and/or virtual components of a Generic VNF Benchmarking Architectural
Framework needed to habilitate the execution of an automated VNF
benchmarking methodology. The following considerations hold for a
deployment scenario:
o Not all components are mandatory for a Test, possible to be
disposed in varied settings.
o Components can be composed in a single entity and be defined as
black or white boxes. For instance, Manager and Agents could
jointly define one hardware/software entity to perform a VNF
benchmarking Test and present measurement results.
o Monitor can be defined by multiple instances of software
components, each addressing a VNF or execution environment.
o Agents can be disposed in varied topology setups, included the
possibility of multiple input and output ports of a VNF being
directly connected each in one Agent.
o All benchmarking components defined in a deployment scenario must
perform the synchronization of clocks.
6. Methodology
Portability is an intrinsic characteristic of VNFs and allows them to
be deployed in multiple environments. This enables various
benchmarking setups in varied deployment scenarios. A VNF
benchmarking methodology must be described in a clear and objective
manner following four basic principles:
o Comparability: Output of Tests shall be simple to understand and
process, in a human-readable format, coherent, and easily reusable
(e.g., inputs for analytic applications).
Rosa, et al. Expires December 26, 2019 [Page 10]
Internet-Draft VNFBench June 2019
o Repeatability: A Test setup shall be comprehensively defined
through a flexible design model that can be interpreted and
executed by the testing platform repeatedly but supporting
customization.
o Configurability: Open interfaces and extensible messaging models
shall be available between benchmarking components for flexible
composition of Test descriptors and platform configurations.
o Interoperability: Tests shall be ported to different environments
using lightweight components.
______________
+--------+ | |
| | | Automated |
| VNF-BD |--(defines)-->| Benchmarking |
| | | Methodology |
+--------+ |______________|
V
|
(generates)
|
v
+-------------------------+
| VNF-BR |
| +--------+ +--------+ |
| | | | | |
| | VNF-BD | | VNF-PP | |
| | {copy} | | | |
| +--------+ +--------+ |
+-------------------------+
Figure 2: VNF benchmarking process inputs and outputs
As shown in Figure 2, the outcome of an automated VNF benchmarking
methodology, must be captured in a VNF Benchmarking Report (VNF-BR),
consisting of two parts:
VNF Benchmarking Descriptor (VNF-BD) -- contains all required
definitions and requirements to deploy, configure, execute, and
reproduce VNF benchmarking tests. VNF-BDs are defined by the
developer of a benchmarking methodology and serve as input to the
benchmarking process, before being included in the generated VNF-
BR.
Rosa, et al. Expires December 26, 2019 [Page 11]
Internet-Draft VNFBench June 2019
VNF Performance Profile (VNF-PP) -- contains all measured metrics
resulting from the execution of a benchmarking. Additionally, it
might also contain additional recordings of configuration
parameters used during the execution of the benchmarking scenario
to facilitate comparability of VNF-BRs.
A VNF-BR correlates structural and functional parameters of VNF-BD
with extracted VNF benchmarking metrics of the obtained VNF-PP. The
content of each part of a VNF-BR is described in the following
sections.
6.1. VNF Benchmarking Descriptor (VNF-BD)
VNF Benchmarking Descriptor (VNF-BD) -- an artifact that specifies a
Method of how to measure a VNF Performance Profile. The
specification includes structural and functional instructions and
variable parameters at different abstraction levels (e.g., topology
of the deployment scenario, benchmarking target metrics, parameters
of benchmarking components). A VNF-BD may be specific to a VNF or
applicable to several VNF types. It can be used to elaborate a VNF
benchmark deployment scenario aiming at the extraction of particular
VNF performance metrics.
The following items define the VNF-BD contents.
6.1.1. Descriptor Headers
The definition of parameters concerning the descriptor file, e.g.,
its version, identidier, name, author and description.
6.1.2. Target Information
General information addressing the target VNF(s) the VNF-BD is
applicable, with references to any specific characteristics, i.e.,
the VNF type, model, version/release, author, vendor, architectural
components, among any other particular features.
6.1.3. Experiments
The specification of the number of executions for Trials, Tests and
Method. The execution of a VNF-BD corresponds to the execution of
the specified Method.
6.1.4. Environment
The details referring to the name, description, and information
associated with the interfaces needed for the orchestration, if
necessary, of the specified VNF-BD scenario. I.e., it refers to a
Rosa, et al. Expires December 26, 2019 [Page 12]
Internet-Draft VNFBench June 2019
specific interface that receives the VNF-BD scenario information and
converts it to the template needed for an orchestration platform. In
this case, the means to the Manager component interface such
orchestration platform must be provided, as well as its outcome
orchestration status information (e.g., management interfaces of
deployed components).
6.1.5. Scenario
This section contains all information needed to describe the
deployment of all involved functional components mandatory for the
execution of the benchmarking Tests addressed by the VNF-BD.
6.1.5.1. Nodes
Information about each component in a benchmarking setup (see
Section 5). It contains the identification, name, image, role (i.e.,
agent, monitor, sut), connection-points and resource requirements
(i.e., allocation of cpu, memory, disk).
The lifecycle specification of a node lists all the workflows that
must be realized on it during a Test. For instance, main workflows
include: create, start, stop, delete. Particular workflows can be
specified containing the required parameters and implementation.
Those details must reflect the actions taken on or by a node that
might affect the VNF performance profile.
6.1.5.2. Links
Links contain information about the data plane links interconnecting
the components of a benchmarking setup. Links refer to two or more
connection-points of a node. A link might refer to be part of a
network. Depending on the link type, the network might be
implemented as a layer 2 mesh, or as directional-oriented traffic
forwarding flow entries. Links also detain resource requirements,
specifying the minimum bandwidth, latency, and frame loss rate for
the execution of benchmarking Tests.
6.1.5.3. Policies
Involves the definition of execution environment policies to run the
Tests. Policies might specify the (anti-)affinity placement rules
for each component in the topology, min/max allocation of resources,
and specific enabling technologies (e.g., DPDK, SR-IOV, PCIE) needed
for each component.
Rosa, et al. Expires December 26, 2019 [Page 13]
Internet-Draft VNFBench June 2019
6.1.6. Proceedings
This information is utilized by the Manager component to execute the
benchmarking Tests. It consists of agent(s) and monitor(s) settings,
detailing their prober(s)/listener(s) specification and running
parameters.
Agents: Defines a list containing the Agent(s) needed for the VNF-
BD tests. The information of each Agent contains its host
environment, making reference to a node specified in the VNF-BD
scenario (Section 6.1.5). In addition, each Agent also is defined
with the the configured toolset of the Prober(s) and their running
parameters fulfilled (e.g., stimulus workload, traffic format/
trace, configurations to enable hardware capabilities, if
existent). In each Prober, it is also detailed the output metrics
to be extracted from it when running the benchmarking Tests.
Monitors: Defines a list containing the Monitor(s) needed for the
VNF-BD tests. The information of each Monitor contains its host
environment, making reference to a node specified in the VNF-BD
scenario (Section 6.1.5) and detailing the placement settings of
it (e.g., internal or external with the target VNF and/or
execution environment). In addition, each Monitor also is defined
with the the configured toolset of the Listener(s) and their
running parameters fulfilled (e.g., tap interfaces, period of
monitoring, interval among the measurements). In each Listener,
it is also detailed the output metrics to be extracted from it
when running the benchmarking Tests.
6.2. VNF Performance Profile (VNF-PP)
VNF Performance Profile (VNF-PP) -- defines a mapping between
resources allocated to a VNF (e.g., CPU, memory) as well as assigned
configurations (e.g., routing table used by the VNF) and the VNF
performance metrics (e.g., throughput, latency, CPU, memory) obtained
in a benchmarking Test conducted using a VNF-BD. Logically, packet
processing metrics are presented in a specific format addressing
statistical significance (e.g., median, standard deviation,
percentiles) where a correspondence among VNF parameters and the
delivery of a measured VNF performance exists.
The following items define the VNF-PP contents.
6.2.1. Execution Environment
Execution environment information has to be included in every VNF-PP
and is required to describe the environment on which a benchmark Test
was actually executed.
Rosa, et al. Expires December 26, 2019 [Page 14]
Internet-Draft VNFBench June 2019
Ideally, any person who has a VNF-BD and its complementing VNF-PP
with its execution environment information available, must be able to
reproduce the same deployment scenario and VNF benchmarking Tests to
obtain identical VNF-PP measurement results.
If not already defined by the VNF-BD deployment scenario requirements
(Section 6.1.5), for each component in the deployment scenario of the
VNF benchmarking setup, the following topics must be detailed:
Hardware Specs: Contains any information associated with the
underlying hardware capabilities offered and used by the component
during the benchmarking Tests. Examples of such specification
include allocated CPU architecture, connected NIC specs, allocated
memory DIMM, etc. In addition, any information concerning details
of resource isolation must also be described in this part of the
VNF-PP.
Software Specs: Contains any information associated with the
software apparatus offered and used during the benchmarking Tests.
Examples include versions of operating systems, kernels,
hypervisors, container image versions, etc.
Optionally, a VNF-PP execution environment might contain references
to an orchestration description document (e.g., HEAT template) to
clarify technological aspects of the execution environment and any
specific parameters that it might contain for the VNF-PP.
6.2.2. Measurement Results
Measurement results concern the extracted metrics, output of
benchmarking procedures, classified into:
VNF Processing/Active Metrics: Concerns metrics explicitly defined
by or extracted from direct interactions of Agents with a VNF.
Those can be defined as generic metric related to network packet
processing (e.g., throughput, latency) or metrics specific to a
particular VNF (e.g., HTTP confirmed transactions, DNS replies).
VNF Monitored/Passive Metrics: Concerns the Monitors' metrics
captured from a VNF execution, classified according to the
virtualization level (e.g., baremetal, VM, container) and
technology domain (e.g., related to CPU, memory, disk) from where
they were obtained.
Depending on the configuration of the benchmarking setup and the
planned use cases for the resulting VNF-PPs, measurement results can
be stored as raw data, e.g., time series data about CPU utilization
of the VNF during a throughput benchmark. In the case of VNFs
Rosa, et al. Expires December 26, 2019 [Page 15]
Internet-Draft VNFBench June 2019
composed of multiple VNFCs, those resulting data should be
represented as vectors, capturing the behavior of each VNFC, if
available from the used monitoring systems. Alternatively, more
compact representation formats can be used, e.g., statistical
information about a series of latency measurements, including
averages and standard deviations. The exact output format to be used
is defined in the complementing VNF-BD (Section 6.1).
The representation format of a VNF-PP must be easily translated to
address the combined set of classified items in the 3x3 Matrix
Coverage defined in [RFC8172].
6.3. Procedures
The methodology for VNF Benchmarking Automation encompasses the
process defined in Figure 2, i.e., the procedures that translate a
VNF-BD into a VNF-PP composing a VNF-BR by the means of the
components specified in Figure 1. This section details the sequence
of events that realize such process.
6.3.1. Pre-Execution
Before the execution of benchmarking Tests, some procedures must be
performed:
1. A VNF-BD must be defined to be later instantiated into a
deployment scenario and have executed its Tests. Such a
description must contain all the structural and functional
settings defined in Section 6.1. At the end of this step, the
complete Method of benchmarking the target VNF is defined.
2. The VNF target image must be prepared to be benchmarked, having
all its capabilities fully described. In addition all the probers
and listeners defined in the VNF-BD must be implemented to realize
the benchmark Tests. At the end of this step, the complete set of
components of the benchmarking VNF-BD deployment scenario is
defined.
3. The environment needed for a VNF-BD must be defined to realize
its deployment scenario, in an automated or manual method. This
step might count on the instantiation of orchestration platforms
and the composition of specific topology descriptors needed by
those platforms to realize the VNF-BD deployment scenario. At the
end of this step, the whole environment needed to instantiate the
components of a VNF-BD deployment scenario is defined.
Rosa, et al. Expires December 26, 2019 [Page 16]
Internet-Draft VNFBench June 2019
6.3.2. Automated Execution
Satisfied all the pre-execution procedures, the automated execution
of the Tests specified by the VNF-BD follow:
1. Upon the parsing of a VNF-BD, the Manager must detect the VNF-BD
variable input field (e.g., list of resources values) and compose
the all the permutations of parameters. For each permutation, the
Manager must elaborate a VNF-BD instance. Each VNF-BD instance
defines a Test, and it will have its deployment scenario
instantiated accordingly. I.e., the Manager must interface an
orchestration platform to realize the automated instantiation of
each deployment scenario defined by a VNF-BD instance (i.e., a
Test). The Manager must iterate through all the VNF-BD instances
to finish the whole set of Tests defined by all the permutations
of the VNF-BD input fields.
2. Given a VNF-BD instance, the Manager, using the VNF-BD
environment settings, must interface an orchestrator platform
requesting the deployment of a scenario to realize a Test. To
perform such step, The Manager might interface a management
function responsible to properly parse the deployment scenario
specifications into the orchestration platform interface format.
3. An orchestration platform must deploy the scenario requested by
the Manager, assuring the requirements and policies specified on
it. In addition, the orchestration platform must acknowledge the
deployed scenario to the Manager specifying the management
interfaces of the VNF and the other components in the running
instances for the benchmarking Test.
4. Agent(s) and Monitor(s) (if existing) and the target VNF must be
configured by the Manager according to the components settings
defined in the VNF-BD instance. After this step, the whole VNF-BD
Test will be ready to be executed.
5. Manager must interface Agent(s) and Monitor(s) (if existing) via
management interfaces to require the execution of the benchmarking
probers (and listeners, if existing), and retrieve expected
metrics captured during or at the end of each Trial. I.e., for a
single Test, according to the VNF-BD execution settings, the
Manager must guarantee that one or more Trials realize the
required measurements to characterize the performance behavior of
a VNF.
6. Output measurements from each obtained benchmarking Test, and
its possible Trials, must be collected by the Manager, until all
the Tests are finished. In the execution settings of the parsed
Rosa, et al. Expires December 26, 2019 [Page 17]
Internet-Draft VNFBench June 2019
VNF-BD, the Manager must check the Method repetition, and perform
the whole set of VNF-BD Tests (i.e., since step 1), until all
methods specified are finished.
7. Collected all measurements from the VNF-BD (Trials, Tests and
Methods) execution, the intended metrics, as described in the VNF-
BD, must be parsed, extracted and combined to create the
corresponding VNF-PP. The combination of used VNF-BD and
generated VNF-PP compose the resulting VNF benchmark report (VNF-
BR).
6.3.3. Post-Execution
After the process of a VNF-BD execution, some automated procedures,
not necessarily mandatory, can be performed to improve the quality
and utility of a VNF-BR:
1. Archive the raw output contained in the VNF-PP, perform
statistical analysis on it, or train machine learning models with
the collected data.
2. Evaluate the analysis output to the detection of any possible
cause-effect factors and/or intrinsic correlations in the VNF-BR
(e.g., outliers).
3. Review the input VNF-BD and modify it to realize the proper
extraction of the target VNF metrics based on the performed
research. Iterate in the previous steps until composing a stable
and representative VNF-BR.
6.4. Particular Cases
As described in [RFC8172], VNF benchmarking might require to change
and adapt existing benchmarking methodologies. More specifically,
the following cases need to be considered.
6.4.1. Capacity
VNFs are usually deployed inside containers or VMs to build an
abstraction layer between physical resources and the resources
available to the VNF. According to [RFC8172], it may be more
representative to design experiments in a way that the VMs hosting
the VNFs are operating at maximum of 50% utilization and split the
workload among several VMs, to mitigateside effects of overloaded
VMs. Those cases are supported by the presented automation
methodologies through VNF-BDs that enable direct control over the
resource assignments and topology layouts used for a benchmarking
experiment.
Rosa, et al. Expires December 26, 2019 [Page 18]
Internet-Draft VNFBench June 2019
6.4.2. Isolation
One of the main challenges of NFV is to create isolation between
VNFs. Benchmarking the quality of this isolation behavior can be
achieved by Agents that take the role of a noisy neighbor, generating
a particular workload in synchrony with a benchmarking procedure over
a VNF. Adjustments of the Agent's noisy workload, frequency,
virtualization level, among others, must be detailed in the VNF- BD.
6.4.3. Failure Handling
Hardware and software components will fail or have errors and thus
trigger healing actions of the benchmarked VNFs (self-healing).
Benchmarking procedures must also capture the dynamics of this VNF
behavior, e.g., if a container or VM restarts because the VNF
software crashed. This results in offline periods that must be
captured in the benchmarking reports, introducing additional metrics,
e.g., max. time-to-heal. The presented concept, with a flexible VNF-
PP structure to record arbitrary metrics, enables automation of this
case.
6.4.4. Elasticity and Flexibility
Having software based network functions and the possibility of a VNF
to be composed by multiple components (VNFCs), internal events of the
VNF might trigger changes in VNF behavior, e.g.,activating
functionalities associated with elasticity such as automated scaling.
These state changes and triggers (e.g. the VNF's scaling state) must
be captured in the benchmarking results (VNF-PP) to provide a
detailed characterization of the VNF's performance behavior in
different states.
6.4.5. Handling Configurations
As described in [RFC8172], does the sheer number of test conditions
and configuration combinations create a challenge for VNF
benchmarking. As suggested, machine readable output formats, as they
are presented in this document, will allow automated benchmarking
procedures to optimize the tested configurations. Approaches for
this are, e.g., machine learning-based configuration space sub-
sampling methods, such as [Peu-c].
6.4.6. White Box VNF
A benchmarking setup must be able to define scenarios with and
without monitoring components inside the VNFs and/or the hosting
container or VM. If no monitoring solution is available from within
the VNFs, the benchmark is following the black-box concept. If, in
Rosa, et al. Expires December 26, 2019 [Page 19]
Internet-Draft VNFBench June 2019
contrast, those additional sources of information from within the VNF
are available, VNF-PPs must be able to handle these additional VNF
performance metrics.
7. Open Source Reference Implementations
Currently, technical motivating factors in favor of the automation of
VNF benchmarking methodologies comprise: (i) the facility to run
high-fidelity and commodity traffic generators by software; (ii) the
existent means to construct synthetic traffic workloads purely by
software (e.g., handcrafted pcap files); (iii) the increasing
availability of datasets containing actual sources of production
traffic able to be reproduced in benchmarking tests; (iv) the
existence of a myriad of automating tools and open interfaces to
programmatically manage VNFs; (v) the varied set of orchestration
platforms enabling the allocation of resources and instantition of
VNFs through automated machineries based on well-defined templates;
(vi) the ability to utilize a large tool set of software components
to compose pipelines that mathematically analyze benchmarking metrics
in automated ways.
In simple terms, network softwarization enables automation. There
are two open source reference implementations that are build to
automate benchmarking of Virtualized Network Functions (VNFs).
7.1. Gym
The software, named Gym, is a framework for automated benchmarking of
Virtualized Network Functions (VNFs). It was coded following the
initial ideas presented in a 2015 scientific paper entitled "VBaaS:
VNF Benchmark-as-a-Service" [Rosa-a]. Later, the evolved design and
prototyping ideas were presented at IETF/IRTF meetings seeking impact
into NFVRG and BMWG.
Gym was built to receive high-level test descriptors and execute them
to extract VNFs profiles, containing measurements of performance
metrics - especially to associate resources allocation (e.g., vCPU)
with packet processing metrics (e.g., throughput) of VNFs. From the
original research ideas [Rosa-a], such output profiles might be used
by orchestrator functions to perform VNF lifecycle tasks (e.g.,
deployment, maintenance, tear-down).
In [Rosa-b] Gym was utilized to benchmark a decomposed IP Multimedia
Subsystem VNF. And in [Rosa-c], a virtual switch (Open vSwitch -
OVS) was the target VNF of Gym for the analysis of VNF benchmarking
automation. Such articles validated Gym as a prominent open source
reference implementation for VNF benchmarking tests. Such articles
Rosa, et al. Expires December 26, 2019 [Page 20]
Internet-Draft VNFBench June 2019
set important contributions as discussion of the lessons learned and
the overall NFV performance testing landscape, included automation.
Gym stands as one open source reference implementation that realizes
the VNF benchmarking methodologies presented in this document. Gym
is released as open source tool under Apache 2.0 license [gym].
7.2. tng-bench
Another software that focuses on implementing a framework to
benchmark VNFs is the "5GTANGO VNF/NS Benchmarking Framework" also
called "tng-bench" (previously "son-profile") and was developed as
part of the two European Union H2020 projects SONATA NFV and 5GTANGO
[tango]. Its initial ideas were presented in [Peu-a] and the system
design of the end-to-end prototype was presented in [Peu-b].
Tng-bench aims to be a framework for the end-to-end automation of VNF
benchmarking processes. Its goal is to automate the benchmarking
process in such a way that VNF-PPs can be generated without further
human interaction. This enables the integration of VNF benchmarking
into continuous integration and continuous delivery (CI/CD) pipelines
so that new VNF-PPs are generated on-the-fly for every new software
version of a VNF. Those automatically generated VNF-PPs can then be
bundled with the VNFs and serve as inputs for orchestration systems,
fitting to the original research ideas presented in [Rosa-a] and
[Peu-a].
Following the same high-level VNF testing purposes as Gym, namely:
Comparability, repeatability, configurability, and interoperability,
tng- bench specifically aims to explore description approaches for
VNF benchmarking experiments. In [Peu-b] a prototype specification
for VNF-BDs is presented which not only allows to specify generic,
abstract VNF benchmarking experiments, it also allows to describe
sets of parameter configurations to be tested during the benchmarking
process, allowing the system to automatically execute complex
parameter studies on the SUT, e.g., testing a VNF's performance under
different CPU, memory, or software configurations.
Tng-bench was used to perform a set of initial benchmarking
experiments using different VNFs, like a Squid proxy, an Nginx load
balancer, and a Socat TCP relay in [Peu-b]. Those VNFs have not only
been benchmarked in isolation, but also in combined setups in which
up to three VNFs were chained one after each other. These
experiments were used to test tng-bench for scenarios in which
composed VNFs, consisting of multiple VNF components (VNFCs), have to
be benchmarked. The presented results highlight the need to
benchmark composed VNFs in end-to-end scenarios rather than only
Rosa, et al. Expires December 26, 2019 [Page 21]
Internet-Draft VNFBench June 2019
benchmark each individual component in isolation, to produce
meaningful VNF- PPs for the complete VNF.
Tng-bench is actively developed and released as open source tool
under Apache 2.0 license [tng-bench].
8. Security Considerations
Benchmarking tests described in this document are limited to the
performance characterization of VNFs in a lab environment with
isolated network.
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.
Special capabilities SHOULD NOT exist in the VNF benchmarking
deployment scenario specifically for benchmarking purposes. Any
implications for network security arising from the VNF benchmarking
deployment scenario SHOULD be identical in the lab and in production
networks.
9. IANA Considerations
This document does not require any IANA actions.
10. Acknowledgement
The authors would like to thank the support of Ericsson Research,
Brazil. Parts of this work have received funding from the European
Union's Horizon 2020 research and innovation programme under grant
agreement No. H2020-ICT-2016-2 761493 (5GTANGO: https://5gtango.eu).
11. References
11.1. Normative References
[ETS14a] ETSI, "Architectural Framework - ETSI GS NFV 002 V1.2.1",
Dec 2014, <http://www.etsi.org/deliver/etsi\_gs/
NFV/001\_099/002/01.02.01-\_60/gs\_NFV002v010201p.pdf>.
[ETS14b] ETSI, "Terminology for Main Concepts in NFV - ETSI GS NFV
003 V1.2.1", Dec 2014,
<http://www.etsi.org/deliver/etsi_gs/NFV/001_099-
/003/01.02.01_60/gs_NFV003v010201p.pdf>.
Rosa, et al. Expires December 26, 2019 [Page 22]
Internet-Draft VNFBench June 2019
[ETS14c] ETSI, "NFV Pre-deployment Testing - ETSI GS NFV TST001
V1.1.1", April 2016,
<http://docbox.etsi.org/ISG/NFV/Open/DRAFTS/TST001_-_Pre-
deployment_Validation/NFV-TST001v0015.zip>.
[ETS14d] ETSI, "Network Functions Virtualisation (NFV); Virtual
Network Functions Architecture - ETSI GS NFV SWA001
V1.1.1", December 2014,
<https://docbox.etsi.org/ISG/NFV/Open/Publications_pdf/
Specs-Reports/NFV-SWA%20001v1.1.1%20-%20GS%20-%20Virtual%2
0Network%20Function%20Architecture.pdf>.
[ETS14e] ETSI, "Report on CI/CD and Devops - ETSI GS NFV TST006
V0.0.9", April 2018,
<https://docbox.etsi.org/isg/nfv/open/drafts/
TST006_CICD_and_Devops_report>.
[RFC1242] S. Bradner, "Benchmarking Terminology for Network
Interconnection Devices", July 1991,
<https://www.rfc-editor.org/info/rfc1242>.
[RFC8172] A. Morton, "Considerations for Benchmarking Virtual
Network Functions and Their Infrastructure", July 2017,
<https://www.rfc-editor.org/info/rfc8172>.
[RFC8204] M. Tahhan, B. O'Mahony, A. Morton, "Benchmarking Virtual
Switches in the Open Platform for NFV (OPNFV)", September
2017, <https://www.rfc-editor.org/info/rfc8204>.
11.2. Informative References
[gym] "Gym Framework Source Code",
<https://github.com/intrig-unicamp/gym>.
[Peu-a] M. Peuster, H. Karl, "Understand Your Chains: Towards
Performance Profile-based Network Service Management",
Fifth European Workshop on Software Defined Networks
(EWSDN) , 2016,
<http://ieeexplore.ieee.org/document/7956044/>.
[Peu-b] M. Peuster, H. Karl, "Profile Your Chains, Not Functions:
Automated Network Service Profiling in DevOps
Environments", IEEE Conference on Network Function
Virtualization and Software Defined Networks (NFV-SDN) ,
2017, <http://ieeexplore.ieee.org/document/8169826/>.
Rosa, et al. Expires December 26, 2019 [Page 23]
Internet-Draft VNFBench June 2019
[Peu-c] M. Peuster, H. Karl, "Understand your chains and keep your
deadlines: Introducing time-constrained profiling for
NFV", IEEE/IFIP 14th International Conference on Network
and Service Management (CNSM) , 2018,
<https://ris.uni-paderborn.de/record/6016>.
[Rosa-a] R. V. Rosa, C. E. Rothenberg, R. Szabo, "VBaaS: VNF
Benchmark-as-a-Service", Fourth European Workshop on
Software Defined Networks , Sept 2015,
<http://ieeexplore.ieee.org/document/7313620>.
[Rosa-b] R. Rosa, C. Bertoldo, C. Rothenberg, "Take your VNF to the
Gym: A Testing Framework for Automated NFV Performance
Benchmarking", IEEE Communications Magazine Testing
Series , Sept 2017,
<http://ieeexplore.ieee.org/document/8030496>.
[Rosa-c] R. V. Rosa, C. E. Rothenberg, "Taking Open vSwitch to the
Gym: An Automated Benchmarking Approach", IV Workshop pre-
IETF/IRTF, CSBC Brazil, July 2017,
<https://intrig.dca.fee.unicamp.br/wp-
content/plugins/papercite/pdf/rosa2017taking.pdf>.
[tango] "5GTANGO: Development and validation platform for global
industry-specific network services and apps",
<https://5gtango.eu>.
[tng-bench]
"5GTANGO VNF/NS Benchmarking Framework",
<https://github.com/sonata-nfv/tng-sdk-benchmark>.
Authors' Addresses
Raphael Vicente Rosa (editor)
University of Campinas
Av. Albert Einstein, 400
Campinas, Sao Paulo 13083-852
Brazil
Email: rvrosa@dca.fee.unicamp.br
URI: https://intrig.dca.fee.unicamp.br/raphaelvrosa/
Rosa, et al. Expires December 26, 2019 [Page 24]
Internet-Draft VNFBench June 2019
Christian Esteve Rothenberg
University of Campinas
Av. Albert Einstein, 400
Campinas, Sao Paulo 13083-852
Brazil
Email: chesteve@dca.fee.unicamp.br
URI: http://www.dca.fee.unicamp.br/~chesteve/
Manuel Peuster
Paderborn University
Warburgerstr. 100
Paderborn 33098
Germany
Email: manuel.peuster@upb.de
URI: http://go.upb.de/peuster
Holger Karl
Paderborn University
Warburgerstr. 100
Paderborn 33098
Germany
Email: holger.karl@upb.de
URI: https://cs.uni-paderborn.de/cn/
Rosa, et al. Expires December 26, 2019 [Page 25]