In-situ Flow Information Telemetry
draft-song-opsawg-ifit-framework-07
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draft-song-opsawg-ifit-framework-07
OPSAWG H. Song, Ed.
Internet-Draft Futurewei
Intended status: Informational F. Qin
Expires: May 7, 2020 China Mobile
H. Chen
China Telecom
J. Jin
LG U+
J. Shin
SK Telecom
November 4, 2019
In-situ Flow Information Telemetry
draft-song-opsawg-ifit-framework-07
Abstract
For efficient network operation, most network operators rely on
traditional Operation, Administration and Maintenance (OAM) methods,
which include proactive and reactive techniques, running in active
and passive modes. As networks increase in scale, they become more
susceptible to measurement accuracy and misconfiguration errors.
With the advent of programmable data-plane, emerging on-path
telemetry techniques provide unprecedented flow insight and fast
notification of network issues (e.g., jitter, increased latency,
packet loss, significant bit error variations, and unequal load-
balancing).
This document outlines an In-situ Flow Information Telemetry (iFIT)
reference framework, which enumerates several high level components
and describes how these components can be assembled to achieve a
complete and closed-loop working solution for on-path telemetry.
iFIT addresses several deployment challenges for on-path telemetry
techniques, especially in carrier networks. As an open framework, it
does not detail the implementation of the components as well as the
interface between the components.
Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in BCP
14 [RFC2119][RFC8174] when, and only when, they appear in all
capitals, as shown here.
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Status of This Memo
This Internet-Draft is submitted in full conformance with the
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This Internet-Draft will expire on May 7, 2020.
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This document is subject to BCP 78 and the IETF Trust's Legal
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Table of Contents
1. Requirements and Challenges . . . . . . . . . . . . . . . . . 3
2. Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3. iFIT Framework Overview . . . . . . . . . . . . . . . . . . . 6
3.1. Passport vs. Postcard . . . . . . . . . . . . . . . . . . 7
4. Architectural Components of iFIT . . . . . . . . . . . . . . 8
4.1. Smart Flow and Data Selection . . . . . . . . . . . . . . 8
4.1.1. Example: Sketch-guided Elephant Flow Selection . . . 9
4.1.2. Example: Adaptive Packet Sampling . . . . . . . . . . 9
4.2. Smart Data Export . . . . . . . . . . . . . . . . . . . . 9
4.2.1. Example: Event-based Anomaly Monitor . . . . . . . . 10
4.3. Dynamic Network Probe . . . . . . . . . . . . . . . . . . 10
4.3.1. Examples . . . . . . . . . . . . . . . . . . . . . . 11
4.4. Encapsulation and Tunneling . . . . . . . . . . . . . . . 11
4.5. On-demand Technique Selection and Integration . . . . . . 12
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5. iFIT Closed-Loop Architecture . . . . . . . . . . . . . . . . 12
5.1. Example: Intelligent Multipoint Performance Monitoring . 14
5.2. Example: Intent-based Network Monitoring . . . . . . . . 14
6. Summary and Future Work . . . . . . . . . . . . . . . . . . . 15
7. Security Considerations . . . . . . . . . . . . . . . . . . . 15
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 15
9. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 15
10. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 16
11. References . . . . . . . . . . . . . . . . . . . . . . . . . 16
11.1. Normative References . . . . . . . . . . . . . . . . . . 16
11.2. Informative References . . . . . . . . . . . . . . . . . 16
11.3. URIs . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 18
1. Requirements and Challenges
The sheer complexity of today's networks requires radical rethinking
of existing methods used for network monitoring and troubleshooting.
Current dynamic networks require "on-path" fault monitoring and
traffic measurement solutions for a wide range of use cases which
include intelligent management of existing network traffic, and
better traffic visibility of emerging applications such as large
scale Virtual Server (VS) mobility, fluid content distribution, and
elastic bandwidth allocation.
Furthermore, the ability to expedite failure detection, fault
localization, and recovery mechanisms, particularly in the case of
soft failures or path degradation are experienced, without causing
extreme or obvious disruption. This is extremely important for since
these types of network issues are often difficult to localize with
existing Operation, Administration and Maintenance (OAM) methods and
reduce overall network efficiency.
Future networks must also support application-aware networking.
Application-aware networking is an emerging industry term and
typically used to describe the capacity of an intelligent network to
maintain current information about user and application connections
that use network resources and, as a result, the operator can
optimize the network resource usage and monitoring to ensure
application and traffic optimality.
Application-aware network operation is important for user SLA
compliance, service path enforcement, fault diagnosis, and network
resource optimization. A family of on-path flow telemetry
techniques, including In-situ OAM (IOAM)
[I-D.brockners-inband-oam-data], Postcard Based Telemetry (PBT)
[I-D.song-ippm-postcard-based-telemetry], In-band Flow Analyzer (IFA)
[I-D.kumar-ippm-ifa], Enhanced Alternate Marking (EAM)
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[I-D.zhou-ippm-enhanced-alternate-marking], and Hybrid Two Steps
(HTS) [I-D.mirsky-ippm-hybrid-two-step], are emerging, which can
provide flow information on the entire forwarding path on a per-
packet basis in real time. These on-path flow telemetry techniques
are very different from the previous active and passive OAM schemes
in that they directly modify the user packets. Given the unique
characteristics of the aforementioned techniques, we may categorize
these on-path telemetry techniques as the hybrid OAM type III,
supplementing the classification defined in [RFC7799].
These techniques are invaluable for application-aware network
operations not only in data center and enterprise networks but also
in carrier networks which may cross multiple domains. Carrier
network operators have shown strong interest in utilizing such
techniques for various purposes. For example, it is vital for the
operators who offer bandwidth intensive, latency and loss sensitive
services such as video streaming and gaming to closely monitor the
relevant flows in real time as the indispensable first step for any
further measure.
However, successfully applying such techniques in carrier networks
needs to consider performance, deployability, and flexibility.
Specifically, several practical challenges need to be addressed:
o C1: On-path flow telemetry incurs extra packet processing which
may strain the network data plane. The potential impact on the
forwarding performance creates an unfavorable "observer effect"
which not only damages the fidelity of the measurement but also
defies the purpose of the measurement.
o C2: On-path flow telemetry can generate a huge amount of OAM data
which may claim too much transport bandwidth and inundate the
servers for data collection, storage, and analysis. Increasing
the data handling capacity is technically viable but expensive.
For example, assume IOAM is applied to all the traffic. One node
will collect a few tens of bytes as telemetry data for each
packet. The whole forwarding path might accumulate a data trace
with a size similar to the average size of the original packets.
Exporting the telemetry data will consume almost half of the
network bandwidth.
o C3: The collectible data defined currently are essential but
limited. As the network operation evolves to be declarative
(intent-based) and automated, and the trends of network
virtualization, network convergence, and packet-optical
integration continue, more data will be needed in an on-demand and
interactive fashion. Flexibility and extensibility on data
defining, acquisition, and filtering, must be considered.
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o C4: If we were to apply some on-path telemetry technique in
today's carrier networks, we must provide solutions to tailor the
provider's network deployment base and support an incremental
deployment strategy. That is, we need to support established
encapsulation schemes for various predominant protocols such as
Ethernet, IPv4, and MPLS with backward compatibility and properly
handle various transport tunnels.
o C5: Applying only a single underlying telemetry technique may lead
to defective result. For example, packet drop can cause the loss
of the flow telemetry data and the packet drop location and reason
remains unknown if only In-situ OAM trace option is used. A
comprehensive solution needs the flexibility to switch between
different underlying techniques and adjust the configurations and
parameters at runtime.
o C6: Development of simplified on-path telemetry primitives and
models, including: telemetry data (e.g., nodes, links, ports,
paths, flows, timestamps) query primitives. These may be used by
an API-based telemetry service for external applications, for
monitoring end-to-end latency measurement of network paths and
application latency calculation.
2. Glossary
This section defines and explains some terms used in this document.
On-path Telemetry: Acquiring data about a packets on its forwarding
path. The term refers to a class of data plane telemetry
techniques which collect data about user flows and packets along
their forwarding paths. IOAM, PBT, IFA, EAM, and HTS are all on-
path telemetry techniques. Such techniques may need to mark user
packets, or insert instruction or data to the headers of user
packets.
iFIT: In-situ Flow Information Telemetry
iFIT Framework: A reference framework that supports network OAM
applications to apply dataplane on-path telemetry techniques.
iFIT Application: A network OAM application that applies the iFIT
framework.
iFIT Domain: The network domain that participates in an iFIT
application.
iFIT Node: A network node that is in an iFIT domain and is capable
of iFIT-specific functions.
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iFIT Head Node: The entry node to an iFIT domain. Usually the
instruction header encapsulation, if needed, happens here.
iFIT End Node: The exit node of an iFIT domain. Usually the
instruction header decapsulation, if needed, happens here.
3. iFIT Framework Overview
To address the aforementioned challenges, we propose an architectural
framework based on multiple network operators' requirements and
common industry practice, which can help to build a workable on-path
flow telemetry solution. We name the framework "In-situ Flow
Information Telemetry" (iFIT) to reflect the fact that this framework
is dedicated to the on-path telemetry data about user/application
flow experience. As an architectural framework for building a
complete solution, iFIT works a level higher than specific data plane
OAM techniques, be it active, passive, or hybrid. The framework is
built up on a few high level architectural components (Section 4).
By assembling these components, a closed-loop can be formed to
provide a complete solution for static, dynamic, and interactive
telemetry applications (Section 5).
iFIT is an open framework. It does not enforce any specific
implementation on each component, neither does it define interfaces
(e.g., API, protocol) between components. The choice of underlying
on-path telemetry techniques and other implementation details is
determined by application implementer.
The network architecture that applies iFIT is shown in Figure 1. The
iFIT domain is confined between the iFIT head nodes and the iFIT end
nodes. An iFIT domain may cross multiple network domains. An iFIT
application uses a controller to configure all the iFIT nodes. The
configuration determines what telemetry data are collected. After
the telemetry data processing and analyzing, the iFIT application may
instruct the controller to modify the iFIT node configuration and
affect the future telemetry data collection. How applications
communicate with the controller is out of scope for this document
iFIT supports two basic on-path telemetry data collection modes:
passport mode (e.g., IOAM trace option and IFA), in which telemetry
data are carried in user packets and exported at the iFIT end nodes,
and postcard mode (e.g., PBT), in which each node in the iFIT domain
may export telemetry data through independent OAM packets. Note that
the boundary between the two modes can be blurry. An application
only need to mix the two modes.
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+-------------------------------------+
| iFIT Application |
| +------------+ +-----------+ |
| | | | | |
| | Controller |<-------| Collector | |
| | | | | |
| +-----:------+ +-----------+ |
| : ^ |
+-------:---------------------|-------+
:configuration |telemetry data
: |
...............:.....................|..........
: : : | :
: +---------:---+-------------:---++---------:---+
: | : | : | : |
V | V | V | V |
+------+-+ +-----+--+ +------+-+ +------+-+
packets| iFIT | | Path | | Path | | iFIT |
==>| Head |====>| Node |==//==>| Node |====>| End |==>
| Node | | A | | B | | Node |
+--------+ +--------+ +--------+ +--------+
|<--- iFIT Domain --->|
Figure 1: iFIT Network Architecture
3.1. Passport vs. Postcard
[passport-postcard] first uses the analogy of passport and postcard
to describe how the packet trace data can be collected and exported.
In the passport mode, each node on the path adds the telemetry data
to the user packets. The accumulated data trace is exported at a
configured end node. In the postcard mode, each node directly
exports the telemetry data using an independent packet while the user
packets are intact.
A prominent advantage of the passport mode is that it naturally
retains the telemetry data correlation along the entire path. The
passport mode also reduces the number of data export packets and the
bandwidth consumed by the data export packets. These can help to
make the data collector and analyzer's work easier. On the other
hand, the passport mode requires more processing on the user packets
and increases the size of user packets, which can cause various
problems. Some other issues are documented in
[I-D.song-ippm-postcard-based-telemetry].
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The postcard mode provides a perfect complement to the passport mode.
It addresses most of the issues faced by the passport mode, at a cost
of needing extra effort to correlate the postcard packets.
4. Architectural Components of iFIT
The high level components of iFIT are listed as follows:
o Smart flow and data selection policy to address the challenge C1
described in Section 1.
o Smart data export to address the challenge C2.
o Dynamic network probe to address C3.
o Encapsulation and tunneling to address C4.
o On-demand technique selection and integration to address C5.
Note that this document does not directly address the challenge C6
which is left to be a concern for iFIT application implementers.
Next we provide a detailed description of each component.
4.1. Smart Flow and Data Selection
In most cases, it is impractical to enable the data collection for
all the flows and for all the packets in a flow due to the potential
performance and bandwidth impact. Therefore, a workable solution
must select only a subset of flows and flow packets to enable the
data collection, even though this means the loss of some information.
In the data plane, the Access Control List (ACL) provides an ideal
means to determine the subset of flow(s).
[I-D.song-ippm-ioam-data-validation-option] describes how one can set
a sample rate or probability to a flow to allow only a subset of flow
packets to be monitored, how one can collect a different set of data
for different packets, and how one can disable or enable data
collection on any specific network node. The document further
introduces an enhancement to IOAM to allow any node to accept or deny
the data collection in full or partially.
Based on these flexible mechanisms, iFIT allows applications to apply
smart flow and data selection policies to suit the requirements. The
applications can dynamically change the policies at any time based on
the network load, processing capability, focus of interest, and any
other criteria.
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4.1.1. Example: Sketch-guided Elephant Flow Selection
Network operators are usually more interested in elephant flows which
consume more resource and are sensitive to changes in network
conditions. A CountMin Sketch [CMSketch] can be used on the data
path of the head nodes, which identifies and reports the elephant
flows periodically. The controller maintains a current set of
elephant flows and dynamically enables the on-path telemetry for only
these flows.
4.1.2. Example: Adaptive Packet Sampling
Applying on-path telemetry on all packets of selected flows can still
be out of reach. A sample rate should be set for these flows and
only enable telemetry on the sampled packets. However, the head
nodes have no clue on the proper sampling rate. An overly high rate
would exhaust the network resource and even cause packet drops; An
overly low rate, on the contrary, would result in the loss of
information and inaccuracy of measurements.
An adaptive approach can be used based on the network conditions to
dynamically adjust the sampling rate. Every node gives user traffic
forwarding higher priority than telemetry data export. In case of
network congestion, the telemetry can sense some signals from the
data collected (e.g., deep buffer size, long delay, packet drop, and
data loss). The controller may use these signals to adjust the
packet sampling rate. In each adjustment period (i.e., RTT of the
feedback loop), the sampling rate is either decreased or increased in
response of the signals. An AIMD policy similar to the TCP flow
control mechanism for the rate adjustment can be used.
4.2. Smart Data Export
The flow telemetry data can catch the dynamics of the network and the
interactions between user traffic and network. Nevertheless, the
data inevitably contain redundancy. It is advisable to remove the
redundancy from the data in order to reduce the data transport
bandwidth and server processing load.
In addition to efficient export data encoding (e.g., IPFIX [RFC7011]
or protobuf [1]), iFIT nodes have several other ways to reduce the
export data by taking advantage of network device's capability and
programmability. iFIT nodes can cache the data and send the
accumulated data in batch if the data is not time sensitive. Various
deduplication and compression techniques can be applied on the batch
data.
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From the application perspective, an application may only be
interested in some special events which can be derived from the
telemetry data. For example, in case that the forwarding delay of a
packet exceeds a threshold, or a flow changes its forwarding path is
of interest, it is unnecessary to send the original raw data to the
data collecting and processing servers. Rather, iFIT takes advantage
of the in-network computing capability of network devices to process
the raw data and only push the event notifications to the subscribing
applications.
Such events can be expressed as policies. An policy can request data
export only on change, on exception, on timeout, or on threshold.
4.2.1. Example: Event-based Anomaly Monitor
Network operators are interested in the anomalies such as path
change, network congestion, and packet drop. Such anomalies are
hidden in raw telemetry data (e.g., path trace, timestamp). Such
anomalies can be described as events and programmed into the device
data plane. Only the triggered events are exported. For example, if
a new flow appears at any node, a path change event is triggered; if
the packet delay exceeds a predefined threshold in a node, the
congestion event is triggered; if a packet is dropped due to buffer
overflow, a packet drop event is triggered.
The export data reduction due to such optimization is substantial.
For example, given a single 5-hop 10Gbps path, assume a moderate
number of 1 million packets per second are monitored, and the
telemetry data plus the export packet overhead consume less than 30
bytes per hop. Without such optimization, the bandwidth consumed by
the telemetry data can easily exceed 1Gbps (>10% of the path
bandwidth), When the optimization is used, the bandwidth consumed by
the telemetry data is negligible. Moreover, the pre-processed
telemetry data greatly simplify the work of data analyzers.
4.3. Dynamic Network Probe
Due to limited data plane resource and network bandwidth, it is
unlikely one can monitor all the data all the time. On the other
hand, the data needed by applications may be arbitrary but ephemeral.
It is critical to meet the dynamic data requirements with limited
resource.
Fortunately, data plane programmability allows iFIT to dynamically
load new data probes. These on-demand probes are called Dynamic
Network Probes (DNP) [I-D.song-opsawg-dnp4iq]. DNP is the technique
to enable probes for customized data collection in different network
planes. When working with IOAM or PBT, DNP is loaded to the data
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plane through incremental programming or configuration. The DNP can
effectively conduct data generation, processing, and aggregation.
DNP introduces enough flexibility and extensibility to iFIT. It can
implement the optimizations for export data reduction motioned in the
previous section. It can also generate custom data as required by
today and tomorrow's applications.
4.3.1. Examples
Following are some possible DNPs that can be dynamically deployed to
support iFIT applications.
On-demand Flow Sketch: A flow sketch is a compact online data
structure for approximate flow statistics which can be used to
facilitate flow selection. The aforementioned CountMin Sketch is
such an example. Since a sketch consumes data plane resources, it
should only be deployed when needed.
Smart Flow Filter: The policies that choose flows and packet
sampling rate can change during the lifetime of an application.
Smart Statistics: An application may need to interactively count
flows based on different flow granularity or maintain hit counters
for selected flow table entries.
Smart Data Reduction: DNP can be used to program the events that
conditionally trigger data export.
4.4. Encapsulation and Tunneling
Since the introduction of IOAM, the IOAM option header encapsulation
schemes in various network protocols have been proposed with the
omission of some protocols, such as MPLS and IPv4, which are still
prevalent in carrier networks. iFIT provides solutions to apply the
on-path flow telemetry techniques in such networks. PBT-M
[I-D.song-ippm-postcard-based-telemetry] does not introduce new
headers to the packets so the trouble of encapsulation for a new
header is avoided. In case a technique that requires a new header is
preferred, [I-D.song-mpls-extension-header] provides a means to
encapsulate the extra header using an MPLS extension header. As for
IPv4, it is possible to encapsulate the new header in an IP option.
For example, RAO [RFC2113] can be used to indicate the presence of
the new header. A recent proposal [I-D.herbert-ipv4-eh] that
introduces the IPv4 extension header may lead to a long term
solution.
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In carrier networks, it is common for user traffic to traverse
various tunnels for QoS, traffic engineering, or security. iFIT
supports both the uniform mode and the pipe mode for tunnel support
as described in [I-D.song-ippm-ioam-tunnel-mode]. With such
flexibility, the operator can either gain a true end-to-end
visibility or apply a hierarchical approach which isolates the
monitoring domain between customer and provider.
4.5. On-demand Technique Selection and Integration
With multiple underlying data collection and export techniques at its
disposal, iFIT can flexibly adapt to different network conditions and
different application requirements.
For example, depending on the types of data that are of interest,
iFIT may choose either IOAM or PBT to collect the data; if an
application needs to track down where the packets are lost, it may
switch from IOAM to PBT.
iFIT can further integrate multiple data plane monitoring and
measurement techniques together and present a comprehensive data
plane telemetry solution to network operating applications.
5. iFIT Closed-Loop Architecture
The iFIT architectural components can work together to form closed-
loop applications, as shown in Figure 2.
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+---------------------+
| |
+------+ iFIT Applications |<------+
| | | |
| +---------------------+ |
| Technique Selection |
| and Integration |
| |
|Smart Flow Smart |
|and Data closed-loop Data |
|Selection Export|
| |
| +----+----+
V +---------+|
+----------+ Encapsulation +---------+||
| iFIT | and Tunneling | iFIT |||
| Head |----------------------->| ||+
| Node | | Nodes |+
+----------+ +---------+
DNP DNP
Figure 2: iFIT Closed-Loop Architecture
An iFIT application may pick a suite of telemetry techniques based on
its requirements and apply an initial technique to the data plane.
It then configures the iFIT head nodes to decide the initial target
flows/packets and telemetry data set, the encapsulation and tunneling
scheme based on the underlying network architecture, and the iFIT-
capable nodes to decide the initial telemetry data export policy.
Based on the network condition and the analysis results of the
telemetry data, the iFIT application can change the telemetry
technique, the flow/data selection policy, and the data export
approach in real time without breaking the normal network operation.
Many of such dynamic changes can be done through loading and
unloading DNPs.
We should avoid confusion between this closed telemetry loop and the
closed control loop. The latter term is often used in the context of
network automation. In such a closed control loop, telemetry also
plays an important role. Based on the telemetry results,
applications can automatically change the network policy or
configuration. In such a context, iFIT is just a part of the loop.
The closed-loop nature of the iFIT framework allows numerous new
applications which enable future network operation architecture.
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5.1. Example: Intelligent Multipoint Performance Monitoring
[I-D.ietf-ippm-multipoint-alt-mark] describes an intelligent
performance management based on the network condition. The idea is
to split the monitoring network into clusters. The cluster partition
that can be applied to every type of network graph and the
possibility to combine clusters at different levels enable the so-
called Network Zooming. It allows a controller to calibrate the
network telemetry, so that it can start without examining in depth
and monitor the network as a whole. In case of necessity (packet
loss or too high delay), an immediate detailed analysis can be
reconfigured. In particular, the controller, that is aware of the
network topology, can set up the most suited cluster partition by
changing the traffic filter or activate new measurement points and
the problem can be localized with a step-by-step process.
An iFIT application on top of the controllers can manage such
mechanism and the iFIT closed-loop architecture allows its dynamic
and flexible operation.
5.2. Example: Intent-based Network Monitoring
User Intents
|
V Per-packet
+------------+ Telemetry
ACL | | Data
+--------+ Controller |<--------+
| | | |
| +--+---------+ |
| | ^ |
| |DNPs |Network |
| | |Infor |
| V | |
+------+-------------------+-----------+---+
| | |
| V +------+ |
| +-------+ +------+| |
| | iFIT | iFIT Domain +------+|| |
| | Head | |iFIT ||+ |
| | Node | |Nodes |+ |
| +-------+ +------+ |
+------------------------------------------+
Figure 3: Intent-based Monitoring
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In this example, a user can express high level intents for network
monitoring. The controller translates an intent and configure the
corresponding DNPs in iFIT nodes which collect necessary network
information. Based on the realtime information feedback, the
controller runs a local algorithm to determine the suspicious flows.
It then deploys ACLs to the iFIT head node to initiate the high
precision per-packet on-path telemetry for these flows.
6. Summary and Future Work
iFIT is an open framework for applying on-path telemetry techniques.
Combining with algorithmic and architectural schemes that fit into
the framework components, iFIT framework enables a practical
telemetry solution based on two basic on-path traffic data collection
modes: passport and postcard.
The operation of iFIT differs from both active OAM and passive OAM as
defined in [RFC7799]. It does not generate any active probe packets
or passively observe unmodified user packets. Instead, it modifies
selected user packets to collect useful information about them.
Therefore, the iFIT operation can be considered the hybrid type III
mode, which can provide more flexible and accurate network OAM.
More challenges and corresponding solutions for iFIT may need to be
covered. For example, how iFIT can fit in the big picture of
autonomous networking and support closed control loops. A complete
iFIT framework should also consider the cross-domain operations. We
leave these topics for future revisions.
7. Security Considerations
No specific security issues are identified other than those have been
discussed in the drafts on on-path flow information telemetry.
8. IANA Considerations
This document includes no request to IANA.
9. Contributors
Other major contributors of this document include Giuseppe Fioccola,
Daniel King, Zhenqiang Li, Zhenbin Li, Tianran Zhou, and James
Guichard.
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10. Acknowledgments
We thank Shwetha Bhandari, Joe Clarke, and Frank Brockners for their
constructive suggestions for improving this document.
11. References
11.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC7799] Morton, A., "Active and Passive Metrics and Methods (with
Hybrid Types In-Between)", RFC 7799, DOI 10.17487/RFC7799,
May 2016, <https://www.rfc-editor.org/info/rfc7799>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
11.2. Informative References
[CMSketch]
Cormode, G. and S. Muthukrishnan, "An improved data stream
summary: the count-min sketch and its applications", 2005,
<http://dx.doi.org/10.1016/j.jalgor.2003.12.001>.
[I-D.brockners-inband-oam-data]
Brockners, F., Bhandari, S., Pignataro, C., Gredler, H.,
Leddy, J., Youell, S., Mizrahi, T., Mozes, D., Lapukhov,
P., Chang, R., and d. daniel.bernier@bell.ca, "Data Fields
for In-situ OAM", draft-brockners-inband-oam-data-07 (work
in progress), July 2017.
[I-D.herbert-ipv4-eh]
Herbert, T., "IPv4 Extension Headers and Flow Label",
draft-herbert-ipv4-eh-01 (work in progress), May 2019.
[I-D.ietf-ippm-multipoint-alt-mark]
Fioccola, G., Cociglio, M., Sapio, A., and R. Sisto,
"Multipoint Alternate Marking method for passive and
hybrid performance monitoring", draft-ietf-ippm-
multipoint-alt-mark-02 (work in progress), July 2019.
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[I-D.kumar-ippm-ifa]
Kumar, J., Anubolu, S., Lemon, J., Manur, R., Holbrook,
H., Ghanwani, A., Cai, D., Ou, H., and L. Yizhou, "Inband
Flow Analyzer", draft-kumar-ippm-ifa-01 (work in
progress), February 2019.
[I-D.mirsky-ippm-hybrid-two-step]
Mirsky, G., Lingqiang, W., and G. Zhui, "Hybrid Two-Step
Performance Measurement Method", draft-mirsky-ippm-hybrid-
two-step-04 (work in progress), October 2019.
[I-D.song-ippm-ioam-data-validation-option]
Song, H. and T. Zhou, "In-situ OAM Data Validation
Option", draft-song-ippm-ioam-data-validation-option-02
(work in progress), April 2018.
[I-D.song-ippm-ioam-tunnel-mode]
Song, H., Li, Z., Zhou, T., and Z. Wang, "In-situ OAM
Processing in Tunnels", draft-song-ippm-ioam-tunnel-
mode-00 (work in progress), June 2018.
[I-D.song-ippm-postcard-based-telemetry]
Song, H., Zhou, T., Li, Z., Shin, J., and K. Lee,
"Postcard-based On-Path Flow Data Telemetry", draft-song-
ippm-postcard-based-telemetry-06 (work in progress),
October 2019.
[I-D.song-mpls-extension-header]
Song, H., Li, Z., Zhou, T., and L. Andersson, "MPLS
Extension Header", draft-song-mpls-extension-header-02
(work in progress), February 2019.
[I-D.song-opsawg-dnp4iq]
Song, H. and J. Gong, "Requirements for Interactive Query
with Dynamic Network Probes", draft-song-opsawg-dnp4iq-01
(work in progress), June 2017.
[I-D.zhou-ippm-enhanced-alternate-marking]
Zhou, T., Fioccola, G., Li, Z., Lee, S., and M. Cociglio,
"Enhanced Alternate Marking Method", draft-zhou-ippm-
enhanced-alternate-marking-04 (work in progress), October
2019.
[passport-postcard]
Handigol, N., Heller, B., Jeyakumar, V., Mazieres, D., and
N. McKeown, "Where is the debugger for my software-defined
network?", 2012,
<https://doi.org/10.1145/2342441.2342453>.
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[RFC2113] Katz, D., "IP Router Alert Option", RFC 2113,
DOI 10.17487/RFC2113, February 1997,
<https://www.rfc-editor.org/info/rfc2113>.
[RFC7011] Claise, B., Ed., Trammell, B., Ed., and P. Aitken,
"Specification of the IP Flow Information Export (IPFIX)
Protocol for the Exchange of Flow Information", STD 77,
RFC 7011, DOI 10.17487/RFC7011, September 2013,
<https://www.rfc-editor.org/info/rfc7011>.
11.3. URIs
[1] https://developers.google.com/protocol-buffers/
Authors' Addresses
Haoyu Song (editor)
Futurewei
2330 Central Expressway
Santa Clara
USA
Email: haoyu.song@futurewei.com
Fengwei Qin
China Mobile
No. 32 Xuanwumenxi Ave., Xicheng District
Beijing, 100032
P.R. China
Email: qinfengwei@chinamobile.com
Huanan Chen
China Telecom
P. R. China
Email: chenhuan6@chinatelecom.cn
Jaehwan Jin
LG U+
South Korea
Email: daenamu1@lguplus.co.kr
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Jongyoon Shin
SK Telecom
South Korea
Email: jongyoon.shin@sk.com
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