Network Working Group                                          V. Talwar
Internet-Draft                                                  J. Kolhe
Intended status: Informational                                 A. Shaikh
Expires: July 21, 2017                                            Google
                                                               J. George
                                                        January 17, 2017

                Use cases for gRPC in network management


   gRPC is an open, high-performance RPC framework designed for
   efficient low-latency cross-service communications.  This document
   describes use cases for gRPC in network management and other
   services, particularly streaming telemetry.

Status of This Memo

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   This Internet-Draft will expire on July 21, 2017.

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   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  gRPC use cases  . . . . . . . . . . . . . . . . . . . . . . .   3
     2.1.  Network management  . . . . . . . . . . . . . . . . . . .   3
       2.1.1.  Streaming telemetry motivation and overview . . . . .   3
       2.1.2.  Streaming telemetry with gRPC . . . . . . . . . . . .   5
       2.1.3.  Network configuration management  . . . . . . . . . .   5
     2.2.  Additional use cases  . . . . . . . . . . . . . . . . . .   6
       2.2.1.  Client Libraries for connecting polyglot systems  . .   6
       2.2.2.  MicroServices . . . . . . . . . . . . . . . . . . . .   6
       2.2.3.  Browser and mobile applications communicating to gRPC
               Services  . . . . . . . . . . . . . . . . . . . . . .   6
       2.2.4.  High performance access to Cloud Services . . . . . .   6
       2.2.5.  Secure and low overhead communications in embedded
               systems . . . . . . . . . . . . . . . . . . . . . . .   6
       2.2.6.  Unified inter-process and remote communication  . . .   7
   3.  Security Considerations . . . . . . . . . . . . . . . . . . .   7
   4.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   7
   5.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   7
     5.1.  Normative references  . . . . . . . . . . . . . . . . . .   7
     5.2.  Informative references  . . . . . . . . . . . . . . . . .   8
   Appendix A.  Change summary . . . . . . . . . . . . . . . . . . .   9
     A.1.  Changes between revisions -00 and -01 . . . . . . . . . .   9
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   9

1.  Introduction

   gRPC is a high performance universal RPC framework to connect
   distributed systems [GRPC-WWW].  gRPC emerged from an internal Google
   framework called Stubby which has been used to connect large numbers
   microservices running within and across data centers for over a
   decade.  Having a uniform, cross-platform RPC infrastructure allowed
   Google to deploy fleet-wide improvements in efficiency, security,
   reliability and behavioral analysis critical to supporting the
   incredible growth of these services.  gRPC is the next generation of
   Stubby, built in the open originally for services, as well as last
   mile computing use cases like mobile, browser, IOT [GRPC-DESIGN].  It
   is based on standards like HTTP/2 [RFC7540] and is extensible and
   pluggable by design.

   This document describes use cases for the gRPC protocol
   [I-D.kumar-rtgwg-grpc-protocol] in network management, including
   monitoring, configuration management and programmatic operations.  We

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   also summarize a number of additional use cases where gRPC is
   currently being applied.

2.  gRPC use cases

2.1.  Network management

   Below we discuss several gRPC applications related to network
   management with a focus on monitoring and telemetry.  gRPC is already
   implemented by several network device vendors as a primary transport
   for monitoring data based on the streaming telemetry paradigm.

2.1.1.  Streaming telemetry motivation and overview

   Network operations depend fundamentally on the availability of
   accurate, near real-time data to drive a variety of management
   systems, including traffic control systems, fault recovery systems,
   and demand and capacity forecasting systems.  This data consists of
   information about the control plane (e.g., protocol operations),
   management plane (e.g., system availability, statistics, and
   counters), and data plane (e.g., packet and flow statistics).

   In addition to the variety of data, the volume of monitoring and
   management data continues to increase significantly.  Modern, high-
   density platforms with thousands of interfaces and numerous hardware
   and software modules means potentially collecting millions of objects
   and running tens of thousands of CLI commands every few minutes in a
   large-scale network.  Network monitoring data is increasingly used to
   manage mission-critical systems such as real-time monitoring,
   centralized traffic engineering, server selection and load balancing.
   Hence it requires efficient, secure, and scalable mechanisms for data
   transport, encoding, and control.

   Most networks rely on traditional management protocols such as SNMP
   [RFC1157] [RFC3410] for collecting monitoring data about the control
   and management planes, and SFLOW [RFC3176] or IPFIX [RFC7011] for the
   data plane.  For control and management data in particular, SNMP is
   the primary tool, despite limitations which make it ill-suited for
   modern, large-scale networks, especially Web- and Internet-scale
   backbones, and large, high-capacity data center networks.

   While SNMP is widely deployed and implemented in a variety of network
   environment, it suffers from a number of drawbacks:

   o  legacy implementations -- designed for devices with limited memory
      and little processing power; e.g., SNMPv2 supports multiple data
      items in a message, but is not optimized for high-volume data

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   o  lack of discoverability -- discovering new elements requires
      walking the SNMP MIB periodically; on high-density platforms this
      is extremely computationally expensive

   o  lack of capability advertisements -- each object ID must be
      checked to know whether it is supported by the target platform

   o  rigid data structures -- whether using standard or vendor
      proprietary MIBs, the structure and format of the data cannot be
      easily extended or augmented

   To address these drawbacks, a number of network operators proposed a
   new approach for network monitoring based on streaming telemetry (see
   an early proposal in [I-D.swhyte-i2rs-data-collection-system]).
   Streaming telemetry is based on a pub/sub push model in which target
   devices send data of interest over a streaming channel to a data
   collection system.

   Some notable features of a streaming telemetry system include:

   o  targets stream data continuously based on a specified period (or
      as frequently as the target supports), or on a state change

   o  data is sent as soon as it is available, reducing the need to
      buffer, or to handle a single large for all data at once

   o  data may be sent incrementally, e.g., only for those data items
      that have changed

   o  ability to distribute the telemetry sources (e.g., directly to
      linecards) to avoid burdening the management CPU

   o  users issue subscription requests via RPC to the target to request
      only the data of interest

   o  data is exported in a well-structured, common format, e.g., based
      on YANG models of operational state data

   o  the target and collector communicate over a secure, authenticated,
      reliable channel that is long-lived and efficient

   Streaming telemetry allows the network behavior to be observed
   through a time-series data stream.  This is in contrast to the
   polling mechanism used in SNMP in which a monitoring client must
   periodically request the set of desired data, and walk the MIB to
   discover changes.  The polling frequency is limited since the device

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   must be able to handle large requests for all interface or QoS
   counters, for example.

   Open source implementations of streaming telemetry are currently
   being developed by several network vendors, including adapters to
   deliver data into time-series databases, messaging systems, and data
   visualization systems [ST-CISCO] [ST-JUNIPER] [ST-ARISTA].

2.1.2.  Streaming telemetry with gRPC

   gRPC provides a number of capabilities that makes it well-suited for
   network telemetry.  Since its underlying transport is based on
   HTTP/2, it can exploit several key features:

   o  binary framing and header compression -- highly efficient encoding
      on the wire to enable bulk data transfer

   o  bidirectional streaming RPCs -- the target and collector can
      stream their data independently, and leverage application-level
      flow control

   o  flexible data encoding -- gRPC is payload agnostic, and can be
      used to transfer data encoded as XML, JSON, protocol buffer, or
      Thrift; as new data formats and encodings emerge for network data,
      the RPC layer can be easily adapted

   o  multi-language support -- open source gRPC IDLs are available for
      10 programming languages, and service endpoints can be created on
      a number of operating systems, giving device vendors flexibility
      in implementation

   gRPC-based telemetry stacks are now being implemented with some
   available as open source [ST-ARISTA].  A protocol specification for
   streaming telemetry based on gRPC is also available [GNMI-SPEC].

2.1.3.  Network configuration management

   gRPC offers a non-proprietary, modern alternative to vendor-specific
   configuration protocols or standards such as NETCONF [RFC6241] or TL1
   [TL1].  Some of the benefits of using gRPC for configuration
   management include more flexible data encodings (e.g., no requirement
   to use XML), easier integration based on the large number of language
   implementations available, and more options for securing connections.

   Several platforms now support gRPC configuration protocols using data
   based on YANG models [GRPC-CISCO] [GRPC-JUNIPER].

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2.2.  Additional use cases

2.2.1.  Client Libraries for connecting polyglot systems

   gRPC generates client libraries in 10 languages and thus allows
   developers to operate in their language of choice and system to
   communicate with any other system.  These libraries offer idiomatic-
   to-language API surface such that every developer feels they are in
   their language native environment.

2.2.2.  MicroServices

   Designed as a general, high performance protocol to interconnect
   polyglot systems, gRPC is ideal for microservices communication,
   independent of where the services are deployed.  A protocol that
   offers flow control, bidirectional streaming and a very compact
   serialization mechanism is ideally suited for connecting
   microservices at scale.  It is already being adopted by large
   organizations like Square and Netflix for their microservices

2.2.3.  Browser and mobile applications communicating to gRPC Services

   Mobile and Browser applications are becoming feature rich and more
   demanding by the day.  User expectation is that apps are performant
   in various network conditions and drain minimal battery and computing
   power of device. gRPC provides native iOS and Android Java libraries
   for more efficient communication for applications with backend
   services such that battery, data are efficiently used and developers
   have more control of communication with servers using gRPC APIs.

2.2.4.  High performance access to Cloud Services

   The expectations from high request-rate cloud services like storage
   and pub/sub messaging systems are to be very efficient and low cost
   from a compute and networking point of view.  Hence, gRPC based APIs
   are being used for services like Google Cloud BigTable and Google
   Cloud PubSub.  External products like etcd (underlying storage system
   for kubernetes) also relies on gRPC.

2.2.5.  Secure and low overhead communications in embedded systems

   With its integrated authentication model and a IDL like nano-
   protobuf, gRPC could be ideal for secure device-to-device and device-
   to-cloud communication as well.  This use case is still under

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2.2.6.  Unified inter-process and remote communication

   gRPC can provide a unified programming model for both inter-process
   communication and remote service communication.  This use case is
   still under development.

3.  Security Considerations

   As applied to network configuration and monitoring, any transport
   protocol and RPC framework must have support for secure,
   authenticated communication.  gRPC supports a number of security
   mechanisms that are suitable for use in network management, including
   TLS-based transport, and client and server authentication.  These
   will be detailed further in subsequent drafts.

4.  IANA Considerations

   None at this time.  In the future, there may be proposals to
   designate specific application ports for gRPC-based telemetry and
   configuration traffic.

5.  References

5.1.  Normative references

   [RFC1157]  Case, J., Fedor, M., Schoffstall, M., and J. Davin,
              "Simple Network Management Protocol (SNMP)", RFC 1157, DOI
              10.17487/RFC1157, May 1990,

   [RFC3410]  Case, J., Mundy, R., Partain, D., and B. Stewart,
              "Introduction and Applicability Statements for Internet-
              Standard Management Framework", RFC 3410, DOI 10.17487/
              RFC3410, December 2002,

   [RFC3176]  Phaal, P., Panchen, S., and N. McKee, "InMon Corporation's
              sFlow: A Method for Monitoring Traffic in Switched and
              Routed Networks", RFC 3176, DOI 10.17487/RFC3176,
              September 2001, <>.

   [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,

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   [RFC7540]  Belshe, M., Peon, R., and M. Thomson, Ed., "Hypertext
              Transfer Protocol Version 2 (HTTP/2)", RFC 7540, DOI
              10.17487/RFC7540, May 2015,

   [RFC6241]  Enns, R., Ed., Bjorklund, M., Ed., Schoenwaelder, J., Ed.,
              and A. Bierman, Ed., "Network Configuration Protocol
              (NETCONF)", RFC 6241, DOI 10.17487/RFC6241, June 2011,

5.2.  Informative references

              Ryan, L., "gRPC Motivation and Design Principles",
              September 2015, <>.

              "gRPC Web site (", September 2015,

              "Arista Networks goarista GitHub repository", July 2016,

              "Cisco Systems bigmuddy GitHub repository", April 2016,

              "Cisco Systems grpc GitHub repository", February 2016,

              "Juniper Networks open-nti GitHub repository", July 2016,

              Juniper Networks, "Next-Generation Network Configuration
              and Management", May 2016,

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              Borman, P., Hines, M., Lebsack, C., Morrow, C., Shaikh,
              A., and R. Shakir, "gRPC Network Management Interface",
              November 2016,

   [TL1]      Telcordia, "GR-831-CORE - Operations Application Messages
              - Language for Operations Application Messages", November
              1996, <

              Kumar, A., Kolhe, J., Ghemawat, S., and L. Ryan, "gRPC
              Protocol", kumar-rtgwg-grpc-protocol-00 (work in
              progress), July 2016.

              Whyte, S., Hines, M., and W. Kumari, "Bulk Network Data
              Collection System", draft-swhyte-i2rs-data-collection-
              system-00 (work in progress), October 2013.

              Shakir, R., Shaikh, A., and M. Hines, "Consistent Modeling
              of Operational State Data in YANG", draft-openconfig-
              netmod-opstate-01 (work in progress), July 2015.

Appendix A.  Change summary

A.1.  Changes between revisions -00 and -01

   o  Added reference to gRPC Network Management Interface

   o  Updated author contact information.

Authors' Addresses

   Varun Talwar
   1600 Amphitheatre Pkwy
   Mountain View, CA  94043


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   Jayant Kolhe
   1600 Amphitheatre Pkwy
   Mountain View, CA  94043


   Anees Shaikh
   1600 Amphitheatre Pkwy
   Mountain View, CA  94043


   Joshua George
   170 W Tasman Dr
   San Jose, CA  95134


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