| Network Working Group | H. Song, Ed. |
| Internet-Draft | T. Zhou |
| Intended status: Informational | ZB. Li |
| Expires: January 3, 2019 | Huawei |
| G. Fioccola | |
| Telecom Italia | |
| ZQ. Li | |
| China Mobile | |
| P. Martinez-Julia | |
| NICT | |
| L. Ciavaglia | |
| Nokia | |
| A. Wang | |
| China Telecom | |
| July 2, 2018 |
Toward a Network Telemetry Framework
draft-song-ntf-02
This document suggests the necessity of an architectural framework for network telemetry in order to meet the current and future network operation requirements. The defining characteristics of network telemetry shows a clear distinction from the conventional network OAM concept; hence the network telemetry demands new techniques and protocols. This document clarifies the terminologies and classifies the categories and components of a network telemetry framework. The requirements, challenges, existing solutions, and future directions are discussed for each category. The network telemetry framework and the taxonomy help to set a common ground for the collection of related works and put future technique and standard developments into perspective.
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in RFC 2119.
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The advance of AI/ML technologies gives networks an unprecedented opportunity to realize network autonomy with closed control loops. An intent-driven autonomous network is the logical next step for network evolution following SDN, aiming to reduce (or even eliminate) human labor, make the most efficient use of network resources, and provide better services more aligned with customer requirements. Although we still have a long way to reach the ultimate goal, the journey has started nevertheless.
The storage and computing technologies are already mature enough to be able to retain and process a huge amount of data and make real-time inference. Tools based on machine learning technologies and big data analytics are powerful in detecting and reacting on network faults, anomalies, and policy violations. In turn, the network policy updates for planning, intrusion prevention, optimization, and self-healing can be applied. Some tools can even predict future events based on historical data.
However, the networks fail to keep pace with such data need. The current network architecture, protocol suite, and system design are not ready yet to provide enough quality data. In the remaining of this section, first we identify a few key network operation use cases that network operators need the most. These use cases are also the essential functions of the future autonomous networks. Next, we show why the current network OAM techniques and protocols are not sufficient to meet the requirements of these use cases. The discussion underlines the need of a new brood of techniques and protocols which we put under an umbrella term - network telemetry.
All these use cases involves the data extracted from the network data plane and sometimes from the network control plane and management plane.
The conventional OAM techniques, as described in [RFC7276], are not sufficient to support the above use cases for the following reasons:
Before further discussion, we list some key terminology and acronyms used in this documents. We make an intended distinction between network telemetry and network OAM.
For a long time, network operators have relied upon protocols such as SNMP to monitor the network. SNMP can only provide limited information about the network. Since SNMP is poll-based, it incurs low data rate and high processing overhead. Such drawbacks make SNMP unsuitable for today's automatic network applications.
Network telemetry has emerged as a mainstream technical term to refer to the newer techniques of data collection and consumption, distinguishing itself form the convention techniques for network OAM. It is expected that network telemetry can provide the necessary network visibility for autonomous networks, address the shortcomings of conventional OAM techniques, and allow for the emergence of new techniques bearing certain characterisitcs.
One key difference between the network telemetry and the network OAM is that the network telemetry assumes an intelligent machine in the center of a closed control loop, while the network OAM assumes the human network operators in the middle of an open control loop. The network telemetry can directly trigger the automated network operation; The conventional OAM tools only help human operators to monitor and diagnose the networks and guide manual network operations. The different assumptions lead to very different techniques.
Although the network telemetry techniques are just emerging and subject to continuous evolution, several defining characteristics of network telemetry have been well accepted:
In addition, the ideal network telemetry solution should also support the following features:
Big data analytics and machine-learning based AI technologies are applied for network operation automation, relying on abundant data from networks. The single-sourced and static data acquisition cannot meet the data requirements. It is desirable to have a framework that integrates multiple telemetry approaches from different layers, and allows flexible combinations for different applications. The framework will benefit application development for the following reasons.
So far, some telemetry related work has been done within IETF. However, this work is fragmented and scattered in different working groups. The lack of coherence makes it difficult to assemble a comprehensive network telemetry system and causes repetitive and redundant work.
A formal network telemetry framework is needed for constructing a working system. The framework should cover the concepts and components from the standardization perspective. This document clarifies the layers on which the telemetry is exerted and decomposes the telemetry system into a set of distinct components that the existing and future work can easily map to.
Telemetry can be applied on the data plane, the control plane, and the management plane in a network, as well as other sources out of the network, as shown in Figure 1.
+------------------------------+
| |
| Network Operation |<-------+
| Applications | |
| | |
+------------------------------+ |
^ ^ ^ |
| | | |
V | V V
+-----------|---+--------------+ +-----------+
| | | | | |
| Control Pl|ane| | | External |
| Telemetry | <---> | | Data and |
| | | | | Event |
| ^ V | Management | | Telemetry |
+------|--------+ Plane | | |
| V | Telemetry | +-----------+
| | |
| Data Plane <---> |
| Telemetry | |
| | |
+---------------+--------------+
Figure 1: Layer Category of the Network Telemetry Framework
Note that the interaction with the network operation applications can be indirect. For example, in the management plane telemetry, the management plane may need to acquire data from the data plane. On the other hand, an application may involve more than one plane simultaneously. For example, an SLA compliance application may require both the data plane telemetry and the control plane telemetry.
At each plane, the telemetry can be further partitioned into five distinct components:
+------------------------------+
| |
| Data Analysis/Storage |
| |
+------------------------------+
| ^
| |
V |
+---------------+--------------+
| | |
| Data | Data |
| Subscription | Export |
| | |
+---------------+--------------|
| |
| Data Generation |
| |
+------------------------------|
| |
| Data Source |
| |
+------------------------------+
Figure 2: Components in the Network Telemetry Framework
Since most existing standard-related work belongs to the first four components, in the remainder of the document, we focus on these components only.
The following table provides a non-exhaustive list of existing works (mainly published in IETF and with the emphasis on the latest new technologies) and shows their positions in the framework.
+-----------+--------------+---------------+--------------+
| | Management | Control | Data |
| | Plane | Plane | Plane |
+-----------+--------------+---------------+--------------+
| | YANG Data | Control Proto.| Flow/Packet |
| Data | Store | Network State | Statistics |
| Source | | | States |
| | | | DPI |
+-----------+--------------+---------------+--------------+
| | gPRC | NETCONF/YANG | NETCONF/YANG |
| Data | YANG PUSH | BGP | YANG FSM |
| Subscribe | | | |
| | | | |
+-----------+--------------+---------------+--------------+
| | Soft DNP | Soft DNP | In-situ OAM |
| Data | | | IPFPM |
| Generation| | | Hard DNP |
| | | | |
+-----------+--------------+---------------+--------------+
| | gRPC | BMP | IPFIX |
| Data | YANG PUSH | | UDP |
| Export | UDP | | |
| | | | |
+-----------+--------------+---------------+--------------+
Figure 3: Existing Work
The management plane of the network element interacts with the Network Management System (NMS), and provides information such as performance data, network logging data, network warning and defects data, and network statistics and state data. Some legacy protocols are widely used for the management plane, such as SNMP and Syslog, but these protocols do not meet the requirements of the automatic network operation applications.
New management plane telemetry protocols should consider the following requirements:
NETCONF is one popular network management protocol, which is also recommended by IETF. Although it can be used for data collection, NETCONF is good at configurations. YANG Push extends NETCONF and enables subscriber applications to request a continuous, customized stream of updates from a YANG datastore. Providing such visibility into changes made upon YANG configuration and operational objects enables new capabilities based on the remote mirroring of configuration and operational state. Moreover, distributed data collection mechanism via UDP based publication channel provides enhanced efficiency for the NETCONF based telemetry.
gRPC Network Management Interface (gNMI) is a network management protocol based on the gRPC RPC (Remote Procedure Call) framework. With a single gRPC service definition, both configuration and telemetry can be covered. gRPC is an HTTP/2 based open source micro service communication framework. It provides a number of capabilities that makes it well-suited for network telemetry, including:
The control plane telemetry refers to the health condition monitoring of different network protocols, which covers Layer 2 to Layer 7. Keeping track of the running status of these protocols is beneficial for detecting, localizing, and even predicting various network issues, as well as network optimization, in real-time and in fine granularities.
One of the most challenging problems for the control plane telemetry is how to correlate the E2E Key Performance Indicators (KPI) to a specific layer's KPIs. For example, an IPTV user may describe his User Experience (UE) by the video fluency and definition. Then in case of an unusually poor UE KPI or a service disconnection, it is non-trivial work to delimit and localize the issue to the responsible protocol layer (e.g., the Transport Layer or the Network Layer), the responsible protocol (e.g., ISIS or BGP at the Network Layer), and finally the responsible device(s) with specific reasons.
Traditional OAM-based approaches for control plane KPI measurement include PING (L3), Tracert (L3), Y.1731 (L2) and so on. One common issue behind these methods is that they only measure the KPIs instead of reflecting the actual running status of these protocols, making them less effective or efficient for control plane troubleshooting and network optimization. An example of the control plane telemetry is the BGP monitoring protocol (BMP), it is currently used to monitoring the BGP routes and enables rich applications, such as BGP peer analysis, AS analysis, prefix analysis, security analysis, and so on. However, the monitoring of other layers, protocols and the cross-layer, cross-protocol KPI correlations are still in their infancies (e.g., the IGP monitoring is missing), which require substantial further research.
BGP Monitoring Protocol (BMP) is used to monitor BGP sessions and intended to provide a convenient interface for obtaining route views.
The BGP routing information is collected from the monitored device(s) to the BMP monitoring station by setting up the BMP TCP session. The BGP peers are monitored by the BMP Peer Up and Peer Down Notifications. The BGP routes (including Adjacency_RIB_In, Adjacency_RIB_out, and Local_Rib are encapsulated in the BMP Route Monitoring Message and the BMP Route Mirroring Message, in the form of both initial table dump and real-time route update. In addition, BGP statistics are reported through the BMP Stats Report Message, which could be either timer triggered or event driven. More BMP extensions can be explored to enrich the applications of BGP monitoring.
An effective data plane telemetry system relies on the data that the network device can expose. The data's quality, quantity, and timeliness must meet some stringent requirements. This raises some challenges to the network data plane devices where the first hand data originate.
The industry has agreed that the data plane programmability is essential to support network telemetry. Newer data plane chips are all equipped with advanced telemetry features and provide flexibility to support customized telemetry functions.
There can be multiple possible dimensions to classify the data plane telemetry techniques.
The Alternate Marking method is efficient to perform packet loss, delay, and jitter measurements both in an IP and Overlay Networks, as presented in IPFPM and [I-D.fioccola-ippm-multipoint-alt-mark].
This technique can be applied to point-to-point and multipoint-to-multipoint flows. Alternate Marking creates batches of packets by alternating the value of 1 bit (or a label) of the packet header. These batches of packets are unambiguously recognized over the network and the comparison of packet counters for each batch allows the packet loss calculation. The same idea can be applied to delay measurement by selecting ad hoc packets with a marking bit dedicated for delay measurements.
Alternate Marking method needs two counters each marking period for each flow under monitor. For instance, by considering n measurement points and m monitored flows, the order of magnitude of the packet counters for each time interval is n*m*2 (1 per color).
Since networks offer rich sets of network performance measurement data (e.g packet counters), traditional approaches run into limitations. One reason is the fact that the bottleneck is the generation and export of the data and the amount of data that can be reasonably collected from the network. In addition, management tasks related to determining and configuring which data to generate lead to significant deployment challenges.
Multipoint Alternate Marking approach, described in [I-D.fioccola-ippm-multipoint-alt-mark], aims to resolve this issue and makes the performance monitoring more flexible in case a detailed analysis is not needed.
An application orchestrates network performance measurements tasks across the network to allow an optimized monitoring and it can calibrate how deep can be obtained monitoring data from the network by configuring measurement points roughly or meticulously.
Using Alternate Marking, it is possible to monitor a Multipoint Network without examining in depth by using the Network Clustering (subnetworks that are portions of the entire network that preserve the same property of the entire network, called clusters). So in case there is packet loss or the delay is too high the filtering criteria could be specified more in order to perform a detailed analysis by using a different combination of clusters up to a per-flow measurement as described in IPFPM.
In summary, an application can configure initially an end to end monitoring between ingress points and egress points of the network. If the network does not experiment issues, this approximate monitoring is good enough and is very cheap in terms of network resources. But, in case of problems, the application becomes aware of the issues from this approximate monitoring and, in order to localize the portion of the network that has issues, configures the measurement points more exhaustively. So a new detailed monitoring is performed. After the detection and resolution of the problem the initial approximate monitoring can be used again.
Hardware based Dynamic Network Probe (DNP) provides a programmable means to customize the data that an application collects from the data plane. A direct benefit of DNP is the reduction of the exported data. A full DNP solution covers several components including data source, data subscription, and data generation. The data subscription needs to define the custom data which can be composed and derived from the raw data sources. The data generation takes advantage of the moderate in-network computing to produce the desired data.
While DNP can introduce unforeseeable flexibility to the data plane telemetry, it also faces some challenges. It requires a flexible data plane that can be dynamically reprogrammed at run-time. The programming API is yet to be defined.
Traffic on a network can be seen as a set of flows passing through network elements. IP Flow Information Export (IPFIX) provides a means of transmitting traffic flow information for administrative or other purposes. A typical IPFIX enabled system includes a pool of Metering Processes collects data packets at one or more Observation Points, optionally filters them and aggregates information about these packets. An Exporter then gathers each of the Observation Points together into an Observation Domain and sends this information via the IPFIX protocol to a Collector.
Traditional passive and active monitoring and measurement techniques are either inaccurate or resource-consuming. It is preferable to directly acquire data associated with a flow's packets when the packets pass through a network. In-situ OAM (iOAM), a data generation technique, embeds a new instruction header to user packets and the instruction directs the network nodes to add the requested data to the packets. Thus, at the path end the packet's experience on the entire forwarding path can be collected. Such firsthand data is invaluable to many network OAM applications.
However, iOAM also faces some challenges. The issues on performance impact, security, scalability and overhead limits, encapsulation difficulties in some protocols, and cross-domain deployment need to be addressed.
Events that occur outside the boundaries of the network system are another important source of telemetry information. Correlating both internal telemetry data and external events with the requirements of network systems, as presented in Exploiting External Event Detectors to Anticipate Resource Requirements for the Elastic Adaptation of SDN/NFV Systems, provides a strategic and functional advantage to management operations.
As with other sources of telemetry information, the data and events must meet strict requirements, especially in terms of timeliness, which is essential to properly incorporate external event information to management cycles. Thus, the specific challenges are described as follows:
Organizing together both internal and external telemetry information will be key for the general exploitation of the management possibilities of current and future network systems, as reflected in the incorporation of cognitive capabilities to new hardware and software (virtual) elements.
TBD
This document includes no request to IANA.
The other main contributors of this document are listed as follows.
We would like to thank Victor Liu and others who have provided helpful comments and suggestions to improve this document.
| [RFC2119] | Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/RFC2119, March 1997. |