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NFVRG                                                        C. Meirosu
Internet Draft                                                 Ericsson
Intended status:  Informational                            A. Manzalini
Expires: April 2016                                      Telecom Italia
                                                            R. Steinert
                                                                   SICS
                                                           G. Marchetto
                                                  Politecnico di Torino
                                                            I. Papafili
                                Hellenic Telecommunications Organization
                                                         K. Pentikousis
                                                                   EICT
                                                              S. Wright
                                                                   AT&T

                                                      October 14, 2015




            DevOps for Software-Defined Telecom Infrastructures
                      draft-unify-nfvrg-devops-03.txt


Status of this Memo

   This Internet-Draft is submitted in full conformance with the
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   This Internet-Draft will expire on April 14, 2016.





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Copyright Notice

   Copyright (c) 2015 IETF Trust and the persons identified as the
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   This document is subject to BCP 78 and the IETF Trust's Legal
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   described in the Simplified BSD License.

Abstract

   Carrier-grade network management was optimized for environments built
   with monolithic physical nodes and involves significant deployment,
   integration and maintenance efforts from network service providers.
   The introduction of virtualization technologies, from the physical
   layer all the way up to the application layer, however, invalidates
   several well-established assumptions in this domain. This draft opens
   the discussion in NFVRG about challenges related to transforming the
   telecom network infrastructure into an agile, model-driven production
   environment for communication services. We take inspiration from data
   center DevOps regarding how to simplify and automate management
   processes for a telecom service provider software-defined
   infrastructure (SDI). Among the identified challenges, we consider
   scalability of observability processes and automated inference of
   monitoring requirements from logical forwarding graphs, as well as
   initial placement (and re-placement) of monitoring functionality
   following changes in flow paths enforced by the controllers. In
   another category of challenges, verifying correctness of behavior for
   network functions where flow rules are no longer necessary and
   sufficient for determining the forwarding state (for example,
   stateful firewalls or load balancers) is very difficult with current
   technology. Finally, we introduce challenges associated with
   operationalizing DevOps principles at scale in software-defined
   telecom networks in three areas related to key monitoring,
   verification and troubleshooting processes.

Table of Contents


   1. Introduction ................................................ 3



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   2. Software-Defined Telecom Infrastructure: Roles and DevOps
   principles ..................................................... 5
      2.1. Service Developer Role ................................. 5
      2.2. VNF Developer role ..................................... 6
      2.3. Operator role .......................................... 6
      2.4. DevOps Principles ...................................... 6
   3. Continuous Integration .......................................7
   4. Continuous Delivery ......................................... 8
   5. Stability Challenges......................................... 8
   6. Consistency, Availability and Partitioning Challenges ....... 10
   7. Observability Challenges .................................... 11
   8. Verification Challenges ..................................... 13
   9. Troubleshooting Challenges .................................. 14
   10. Programmable network management ............................ 16
   11. DevOps Performance Metrics ................................. 17
   12. Security Considerations .................................... 18
   13. IANA Considerations ........................................ 18
   14. References ................................................. 18
      14.1. Informative References ................................ 18
   15. Contributors .............................................. 20
   16. Acknowledgments ........................................... 21
   17. Authors' Addresses ........................................ 22

1. Introduction

   Carrier-grade network management was developed as an incremental
   solution once a particular network technology matured and came to be
   deployed in parallel with legacy technologies. This approach requires
   significant integration efforts when new network services are
   launched. Both centralized and distributed algorithms have been
   developed in order to solve very specific problems related to
   configuration, performance and fault management. However, such
   algorithms consider a network that is by and large functionally
   static. Thus, management processes related to introducing new or
   maintaining functionality are complex and costly due to significant
   efforts required for verification and integration.

   Network virtualization, by means of Software-Defined Networking (SDN)
   and Network Function Virtualization (NFV), creates an environment
   where network functions are no longer static or strictly embedded in
   physical boxes deployed at fixed points. The virtualized network is
   dynamic and open to fast-paced innovation enabling efficient network
   management and reduction of operating cost for network operators. A
   significant part of network capabilities are expected to become
   available through interfaces that resemble the APIs widespread within
   datacenters instead of the traditional telecom means of management
   such as the Simple Network Management Protocol, Command Line


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   Interfaces or CORBA. Such an API-based approach, combined with the
   programmability offered by SDN interfaces [RFC7426], open
   opportunities for handling infrastructure, resources, and Virtual
   Network Functions (VNFs) as code, employing techniques from software
   engineering.

   The efficiency and integration of existing management techniques in
   virtualized and dynamic network environments are limited, however.
   Monitoring tools, e.g. based on simple counters, physical network
   taps and active probing, do not scale well and provide only a small
   part of the observability features required in such a dynamic
   environment. Although huge amounts of monitoring data can be
   collected from the nodes, the typical granularity is rather coarse.
   Debugging and troubleshooting techniques developed for software-
   defined environments are a research topic that has gathered interest
   in the research community in the last years. Still, it is yet to be
   explored how to integrate them into an operational network management
   system. Moreover, research tools developed in academia (such as
   NetSight [H2014], OFRewind [W2011], FlowChecker [S2010], etc.) were
   limited to solving very particular, well-defined problems, and
   oftentimes are not built for automation and integration into carrier-
   grade network operations workflows.

   The topics at hand have already attracted several standardization
   organizations to look into the issues arising in this new
   environment. For example, IETF working groups have activities in the
   area of OAM and Verification for Service Function Chaining
   [I-D.aldrin-sfc-oam-framework] [I-D.lee-sfc-verification] for Service
   Function Chaining. At IRTF, [RFC7149] asks a set of relevant
   questions regarding operations of SDNs. The ETSI NFV ISG defines the
   MANO interfaces [NFVMANO], and TMForum investigates gaps between
   these interfaces and existing specifications in [TR228]. The need for
   programmatic APIs in the orchestration of compute, network and
   storage resources is discussed in [I-D.unify-nfvrg-challenges].

   From a research perspective, problems related to operations of
   software-defined networks are in part outlined in [SDNsurvey] and
   research referring to both cloud and software-defined networks are
   discussed in [D4.1].

   The purpose of this first version of this document is to act as a
   discussion opener in NFVRG by describing a set of principles that are
   relevant for applying DevOps ideas to managing software-defined
   telecom network infrastructures. We identify a set of challenges
   related to developing tools, interfaces and protocols that would
   support these principles and how can we leverage standard APIs for
   simplifying management tasks.


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2. Software-Defined Telecom Infrastructure: Roles and DevOps principles

   Agile methods used in many software focused companies are focused on
   releasing small interactions of code tom implement VNFs with high
   velocity and high quality into a production environment. Similarly
   Service providers are interested to release incremental improvements
   in the network services that they create from virtualized network
   functions. The cycle time for devops as applied in many open source
   projects is on the order of one quarter year or 13 weeks.

   The code needs to undergo a significant amount of automated testing
   and verification with pre-defined templates in a realistic setting.
   From the point of view of infrastructure management, the verification
   of the network configuration as result of network policy
   decomposition and refinement, as well as the configuration of virtual
   functions, is one of the most sensitive operations. When
   troubleshooting the cause of unexpected behavior, fine-grained
   visibility onto all resources supporting the virtual functions
   (either compute, or network-related) is paramount to facilitating
   fast resolution times. While compute resources are typically very
   well covered by debugging and profiling toolsets based on many years
   of advances in software engineering, programmable network resources
   are a still a novelty and tools exploiting their potential are
   scarce.

2.1. Service Developer Role

   We identify two dimensions of the "developer" role in software-
   defined infrastructure (SDI). One dimension relates to determining
   which high-level functions should be part of a particular service,
   deciding what logical interconnections are needed between these
   blocks and defining a set of high-level constraints or goals related
   to parameters that define, for instance, a Service Function Chain.
   This could be determined by the product owner for a particular family
   of services offered by a telecom provider. Or, it might be a key
   account representative that adapts an existing service template to
   the requirements of a particular customer by adding or removing a
   small number of functional entities. We refer to this person as the
   Service Developer and for simplicity (access control, training on
   technical background, etc.) we consider the role to be internal to
   the telecom provider.






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2.2. VNF Developer role

   The other dimension of the "developer" role is a person that writes
   the software code for a new virtual network function (VNF). Depending
   on the actual VNF being developed, this person might be internal or
   external to the telecom provider. We refer to them as VNF Developers.

2.3. Operator role

   The role of an Operator in SDI is to ensure that the deployment
   processes were successful and a set of performance indicators
   associated to a service are met while the service is supported on
   virtual infrastructure within the domain of a telecom provider.

   System integration roles are important and we intend to approach them
   in a future reversion of this draft.

2.4. DevOps Principles

   In line with the generic DevOps concept outlined in [DevOpsP], we
   consider that these four principles as important for adapting DevOps
   ideas to SDI:

   * Deploy with repeatable, reliable processes: Service and VNF
   Developers should be supported by automated build, orchestrate and
   deploy processes that are identical in the development, test and
   production environments. Such processes need to be made reliable and
   trusted in the sense that they should reduce the chance of human
   error and provide visibility at each stage of the process, as well as
   have the possibility to enable manual interactions in certain key
   stages.

   * Develop and test against production-like systems: both Service
   Developers and VNF Developers need to have the opportunity to verify
   and debug their respective SDI code in systems that have
   characteristics which are very close to the production environment
   where the code is expected to be ultimately deployed. Customizations
   of Service Function Chains or VNFs could thus be released frequently
   to a production environment in compliance with policies set by the
   Operators. Adequate isolation and protection of the services active
   in the infrastructure from services being tested or debugged should
   be provided by the production environment.

   * Monitor and validate operational quality: Service Developers, VNF
   Developers and Operators must be equipped with tools, automated as
   much as possible, that enable to continuously monitor the operational
   quality of the services deployed on SDI. Monitoring tools should be


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   complemented by tools that allow verifying and validating the
   operational quality of the service in line with established
   procedures which might be standardized (for example, Y.1564 Ethernet
   Activation [Y1564]) or defined through best practices specific to a
   particular telecom operator.

   * Amplify development cycle feedback loops: An integral part of the
   DevOps ethos is building a cross-cultural environment that bridges
   the cultural gap between the desire for continuous change by the
   Developers and the demand by the Operators for stability and
   reliability of the infrastructure. Feedback from customers is
   collected and transmitted throughout the organization. From a
   technical perspective, such cultural aspects could be addressed
   through common sets of tools and APIs that are aimed at providing a
   shared vocabulary for both Developers and Operators, as well as
   simplifying the reproduction of problematic situations in the
   development, test and operations environments.

   Network operators that would like to move to agile methods to deploy
   and manage their networks and services face a different environment
   compared to typical software companies where simplified trust
   relationships between personnel are the norm. In such companies, it
   is not uncommon that the same person may be rotating between
   different roles. In contrast, in a telecom service provider, there
   are strong organizational boundaries between suppliers (whether in
   Developer roles for network functions, or in Operator roles for
   outsourced services) and the carrier's own personnel that might also
   take both Developer and Operator roles. How DevOps principles reflect
   on these trust relationships and to what extent initiatives such as
   co-creation could transform the environment to facilitate closer Dev
   and Ops integration across business boundaries is an interesting area
   for business studies, but we could not for now identify a specific
   technological challenge.



3. Continuous Integration

   Software integration is the process of bringing together the software
   component subsystems into one software system, and ensuring that the
   subsystems function together as a system. Software integration can
   apply regardless of the size of the software components. The
   objective of Continuous Integration is to prevent integration
   problems close to the expected release of a software development
   project into a production (operations) environment. Continuous
   Integration is therefore closely coupled with the notion of DevOps as
   a mechanism to ease the transition from development to operations.


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   Continuous integration may result in multiple builds per day. It is
   also typically used in conjunction with test driven development
   approaches that integrate unit testing into the build process. The
   unit testing is typically automated through build servers. Such
   servers may implement a variety of additional static and dynamic
   tests as well as other quality control and documentation extraction
   functions. The reduced cycle times of continuous enable improved
   software quality by applying small efforts frequently.

   Continuous Integration applies to developers of VNF as they integrate
   the components that they need to deliver their VNF. The VNFs may
   contain components developed by different teams within the VNF
   Provider, or may integrate code developed externally - e.g. in
   commercial code libraries or in open source communities.

   Service providers also apply continuous integration in the
   development of network services. Network services are comprised of
   various aspects including VNFs and connectivity within and between
   them as well as with various associated resource authorizations. The
   components of the networks service are all dynamic, and largely
   represented by software that must be integrated regularly to maintain
   consistency.   Some of the software components that Service Providers
   may be sourced from VNF Providers or from open source communities.
   Service Providers are increasingly motivated to engage with open
   Source communities [OSandS]. Open source interfaces supported by open
   source communities may be more useful than traditional paper
   interface specifications.  Even where Service Providers are deeply
   engaged in the open source community (e.g. OPNFV) many service
   providers may prefer to obtain the code through some software
   provider as a business practice. Such software providers have the
   same interests in software integration as other VNF providers.



4. Continuous Delivery

   The practice of Continuous Delivery extends Continuous Integration by
   ensuring that the software checked in on the mainline is always in a
   user deployable state and enables rapid deployment by those users.



5. Stability Challenges

   The dimensions, dynamicity and heterogeneity of networks are growing
   continuously. Monitoring and managing the network behavior in order
   to meet technical and business objectives is becoming increasingly


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   complicated and challenging, especially when considering the need of
   predicting and taming potential instabilities.

   In general, instability in networks may have primary effects both
   jeopardizing the performance and compromising an optimized use of
   resources, even across multiple layers: in fact, instability of end-
   to-end communication paths may depend both on the underlying
   transport network, as well as the higher level components specific to
   flow control and dynamic routing. For example, arguments for
   introducing advanced flow admission control are essentially derived
   from the observation that the network otherwise behaves in an
   inefficient and potentially unstable manner. Even with resources over
   provisioning, a network without an efficient flow admission control
   has instability regions that can even lead to congestion collapse in
   certain configurations. Another example is the instability which is
   characteristic of any dynamically adaptive routing system. Routing
   instability, which can be (informally) defined as the quick change of
   network reachability and topology information, has a number of
   possible origins, including problems with connections, router
   failures, high levels of congestion, software configuration errors,
   transient physical and data link problems, and software bugs.

   As a matter of fact, the states monitored and used to implement the
   different control and management functions in network nodes are
   governed by several low-level configuration commands (today still
   done mostly manually). Further, there are several dependencies among
   these states and the logic updating the states (most of which are not
   kept aligned automatically). Normally, high-level network goals (such
   as the connectivity matrix, load-balancing, traffic engineering
   goals, survivability requirements, etc) are translated into low-level
   configuration commands (mostly manually) individually executed on the
   network elements (e.g., forwarding table, packet filters, link-
   scheduling weights, and queue-management parameters, as well as
   tunnels and NAT mappings). Network instabilities due to configuration
   errors can spread from node to node and propagate throughout the
   network.

   DevOps in the data center is a source of inspiration regarding how to
   simplify and automate management processes for software-defined
   infrastructure. Although the low-level configuration could be
   automated by DevOps tools such as CFEngine [C2015], Puppet [P2015]
   and Ansible [A2015], the high-level goal translation towards tool-
   specific syntax is still a manual process. In addition, while
   carrier-grade configuration tools using the NETCONF protocol support
   complex atomic transaction management (which reduces the potential
   for instability), Ansible requires third-party components to support
   rollbacks and the Puppet transactions are not atomic.


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   As a specific example, automated configuration functions are expected
   to take the form of a "control loop" that monitors (i.e., measures)
   current states of the network, performs a computation, and then
   reconfigures the network. These types of functions must work
   correctly even in the presence of failures, variable delays in
   communicating with a distributed set of devices, and frequent changes
   in network conditions. Nevertheless cascading and nesting of
   automated configuration processes can lead to the emergence of non-
   linear network behaviors, and as such sudden instabilities (i.e.
   identical local dynamic can give rise to widely different global
   dynamics).



6. Consistency, Availability and Partitioning Challenges

   The CAP theorem [CAP] states that any networked shared-data system
   can have at most two of following three properties: 1) Consistency
   (C) equivalent to having a single up-to-date copy of the data; 2)
   high Availability (A) of that data (for updates); and 3) tolerance to
   network Partitions (P).

   Looking at a telecom SDI as a distributed computational system
   (routing/forwarding packets can be seen as a computational problem),
   just two of the three CAP properties will be possible at the same
   time. The general idea is that 2 of the 3 have to be chosen. CP favor
   consistency, AP favor availability, CA there are no partition. This
   has profound implications for technologies that need to be developed
   in line with the "deploy with repeatable, reliable processes"
   principle for configuring SDI states. Latency or delay and
   partitioning properties are closely related, and such relation
   becomes more important in the case of telecom service providers where
   Devs and Ops interact with widely distributed infrastructure.
   Limitations of interactions between centralized management and
   distributed control need to be carefully examined in such
   environments. Traditionally connectivity was the main concern: C and
   A was about delivering packets to destination. The features and
   capabilities of SDN and NFV are changing the concerns: for example in
   SDN, control plane Partitions no longer imply data plane Partitions,
   so A does not imply C. In practice, CAP reflects the need for a
   balance between local/distributed operations and remote/centralized
   operations.

   Furthermore to CAP aspects related to individual protocols,
   interdependencies between CAP choices for both resources and VNFs
   that are interconnected in a forwarding graph need to be considered.
   This is particularly relevant for the "Monitor and Validate


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   Operational Quality" principle, as apart from transport protocols,
   most OAM functionality is generally configured in processes that are
   separated from the configuration of the monitored entities. Also,
   partitioning in a monitoring plane implemented through VNFs executed
   on compute resources does not necessarily mean that the dataplane of
   the monitored VNF was partitioned as well.



7. Observability Challenges

   Monitoring algorithms need to operate in a scalable manner while
   providing the specified level of observability in the network, either
   for operation purposes (Ops part) or for debugging in a development
   phase (Dev part). We consider the following challenges:

   * Scalability - relates to the granularity of network observability,
   computational efficiency, communication overhead, and strategic
   placement of monitoring functions.

   * Distributed operation and information exchange between monitoring
   functions - monitoring functions supported by the nodes may perform
   specific operations (such as aggregation or filtering) locally on the
   collected data or within a defined data neighborhood and forward only
   the result to a management system. Such operation may require
   modifications of existing standards and development of protocols for
   efficient information exchange and messaging between monitoring
   functions. Different levels of granularity may need to be offered for
   the data exchanged through the interfaces, depending on the Dev or
   Ops role. Modern messaging systems, such as Apache Kafka [AK2015],
   widely employed in datacenter environments, were optimized for
   messages that are considerably larger than reading a single counter
   value (typical SNMP GET call usage) - note the throughput vs record
   size from [K2014]. It is also debatable to what extent properties
   such as message persistence within the bus are needed in a carrier
   environment, where MIBs practically offer already a certain level of
   persistence of management data at the node level. Also, they require
   the use of IP addressing which might not be needed when the monitored
   data is consumed by a function within the same node.

   * Common communication channel between monitoring functions and
   higher layer entities (orchestration, control or management systems)
   - a single communication channel for configuration and measurement
   data of diverse monitoring functions running on heterogeneous hard-
   and software environments. In telecommunication environments,
   infrastructure assets span not only large geographical areas, but
   also a wide range of technology domains, ranging from CPEs, access-,


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   aggregation-, and transport networks, to datacenters. This
   heterogeneity of hard- and software platforms requires higher layer
   entities to utilize various parallel communication channels for
   either configuration or data retrieval of monitoring functions within
   these technology domains. To address automation and advances in
   monitoring programmability, software defined telecommunication
   infrastructures would benefit from a single flexible communication
   channel, thereby supporting the dynamicity of virtualized
   environments. Such a channel should ideally support propagation of
   configuration, signalling, and results from monitoring functions;
   carrier-grade operations in terms of availability and multi-tenant
   features; support highly distributed and hierarchical architectures,
   keeping messages as local as possible; be lightweight, topology
   independent, network address agnostic; support flexibility in terms
   of transport mechanisms and programming language support.
   Existing popular state-of-the-art message queuing systems such as
   RabbitMQ [R2015] fulfill many of these requirements. However, they
   utilize centralized brokers, posing a single point-of-failure and
   scalability concerns within vastly distributed NFV environment.
   Furthermore, transport support is limited to TCP/IP. ZeroMQ [Z2015]
   on the other hard lacks any advanced features for carrier-grade
   operations, including high-availability, authentication, and tenant
   isolation.

   * Configurability and conditional observability - monitoring
   functions that go beyond measuring simple metrics (such as delay, or
   packet loss) require expressive monitoring annotation languages for
   describing the functionality such that it can be programmed by a
   controller. Monitoring algorithms implementing self-adaptive
   monitoring behavior relative to local network situations may employ
   such annotation languages to receive high-level objectives (KPIs
   controlling tradeoffs between accuracy and measurement frequency, for
   example) and conditions for varying the measurement intensity. Steps
   in this direction were taken by the DevOps tools such as Splunk
   [S2015], whose collecting agent has the ability to load particular
   apps that in turn access specific counters or log files. However,
   such apps are tool specific and may also require deploying additional
   agents that are specific to the application, library or
   infrastructure node being monitored. Choosing which objects to
   monitor in such environment means deploying a tool-specific script
   that configures the monitoring app.

   * Automation - includes mapping of monitoring functionality from a
   logical forwarding graph to virtual or physical instances executing
   in the infrastructure, as well as placement and re-placement of
   monitoring functionality for required observability coverage and
   configuration consistency upon updates in a dynamic network


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   environment. Puppet [P2015] manifests or Ansible [A2015] playbooks
   could be used for automating the deployment of monitoring agents, for
   example those used by Splunk [S2015]. However, both manifests and
   playbooks were designed to represent the desired system configuration
   snapshot at a particular moment in time - they would now need to be
   generated automatically by the orchestration tools instead of a
   DevOps person.

   * Actionable data

   Data produced by observability tools could be utilized in a wide
   category of processes, ranging from billing and dimensioning to real-
   time troubleshooting and optimization. In order to allow for data-
   driven automated decisions and actuations based on these decisions,
   the data needs to be actionable. We define actionable data as being
   representative for a particular context or situation and an adequate
   input towards a decision. Ensuring actionable data is challenging in
   a number of ways, including: defining adaptive correlation and
   sampling windows, filtering and aggregation methods that are adapted
   or coordinated with the actual consumer of the data, and developing
   analytical and predictive methods that account for the uncertainty or
   incompleteness of the data.



8. Verification Challenges

   Enabling ongoing verification of code is an important goal of
   continuous integration as part of the data center DevOps concept. In
   a telecom SDI, service definitions, decompositions and configurations
   need to be expressed in machine-readable encodings. For example,
   configuration parameters could be expressed in terms of YANG data
   models. However, the infrastructure management layers (such as
   Software-Defined Network Controllers and Orchestration functions)
   might not always export such machine-readable descriptions of the
   runtime configuration state. In this case, the management layer
   itself could be expected to include a verification process that has
   the same challenges as the stand-alone verification processes we
   outline later in this section. In that sense, verification can be
   considered as a set of features providing gatekeeper functions to
   verify both the abstract service models and the proposed resource
   configuration before or right after the actual instantiation on the
   infrastructure layer takes place.

   A verification process can involve different layers of the network
   and service architecture. Starting from a high-level verification of
   the customer input (for example, a Service Graph as defined in


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   [I-D.unify-nfvrg-challenges]), the verification process could go more
   in depth to reflect on the Service Function Chain configuration. At
   the lowest layer, the verification would handle the actual set of
   forwarding rules and other configuration parameters associated to a
   Service Function Chain instance. This enables the verification of
   more quantitative properties (e.g. compliance with resource
   availability), as well as a more detailed and precise verification of
   the abovementioned topological ones. Existing SDN verification tools
   could be deployed in this context, but the majority of them only
   operate on flow space rules commonly expressed using OpenFlow syntax.

   Moreover, such verification tools were designed for networks where
   the flow rules are necessary and sufficient to determine the
   forwarding state. This assumption is valid in networks composed only
   by network functions that forward traffic by analyzing only the
   packet headers (e.g. simple routers, stateless firewalls, etc.).
   Unfortunately, most of the real networks contain active network
   functions, represented by middle-boxes that dynamically change the
   forwarding path of a flow according to function-local algorithms and
   an internal state (that is based on the received packets), e.g. load
   balancers, packet marking modules and intrusion detection systems.
   The existing verification tools do not consider active network
   functions because they do not account for the dynamic transformation
   of an internal state into the verification process.

   Defining a set of verification tools that can account for active
   network functions is a significant challenge. In order to perform
   verification based on formal properties of the system, the internal
   states of an active (virtual or not) network function would need to
   be represented. Although these states would increase the verification
   process complexity (e.g., using simple model checking would not be
   feasible due to state explosion), they help to better represent the
   forwarding behavior in real networks. A way to address this challenge
   is by attempting to summarize the internal state of an active network
   function in a way that allows for the verification process to finish
   within a reasonable time interval.



9. Troubleshooting Challenges

   One of the problems brought up by the complexity introduced by NFV
   and SDN is pinpointing the cause of a failure in an infrastructure
   that is under continuous change. Developing an agile and low-
   maintenance debugging mechanism for an architecture that is comprised
   of multiple layers and discrete components is a particularly
   challenging task to carry out. Verification, observability, and


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   probe-based tools are key to troubleshooting processes, regardless
   whether they are followed by Dev or Ops personnel.

   * Automated troubleshooting workflows

   Failure is a frequently occurring event in network operation.
   Therefore, it is crucial to monitor components of the system
   periodically. Moreover, the troubleshooting system should search for
   the cause automatically in the case of failure. If the system follows
   a multi-layered architecture, monitoring and debugging actions should
   be performed on components from the topmost layer to the bottom layer
   in a chain. Likewise, the result of operations should be notified in
   reverse order. In this regard, one should be able to define
   monitoring and debugging actions through a common interface that
   employs layer hopping logic. Besides, this interface should allow
   fine-grained and automatic on-demand control for the integration of
   other monitoring and verification mechanisms and tools.

   * Troubleshooting with active measurement methods

   Besides detecting network changes based on passively collected
   information, active probes to quantify delay, network utilization and
   loss rate are important to debug errors and to evaluate the
   performance of network elements. While tools that are effective in
   determining such conditions for particular technologies were
   specified by IETF and other standardization organization, their use
   requires a significant amount of manual labor in terms of both
   configuration and interpretation of the results.

   In contrast, methods that test and debug networks systematically
   based on models generated from the router configuration, router
   interface tables or forwarding tables, would significantly simplify
   management. They could be made usable by Dev personnel that have
   little expertise on diagnosing network defects. Such tools naturally
   lend themselves to integration into complex troubleshooting workflows
   that could be generated automatically based on the description of a
   particular service chain. However, there are scalability challenges
   associated with deploying such tools in a network. Some tools may
   poll each networking device for the forwarding table information to
   calculate the minimum number of test packets to be transmitted in the
   network. Therefore, as the network size and the forwarding table size
   increase, forwarding table updates for the tools may put a non-
   negligible load in the network.






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10. Programmable network management

   The ability to automate a set of actions to be performed on the
   infrastructure, be it virtual or physical, is key to productivity
   increases following the application of DevOps principles. Previous
   sections in this document touched on different dimensions of
   programmability:

   - Section 6 approached programmability in the context of developing
      new capabilities for monitoring and for dynamically setting
      configuration parameters of deployed monitoring functions

   - Section 7 reflected on the need to determine the correctness of
      actions that are to be inflicted on the infrastructure as result
      of executing a set of high-level instructions

   - Section 8 considered programmability in the perspective of an
      interface to facilitate dynamic orchestration of troubleshooting
      steps towards building workflows and for reducing the manual steps
      required in troubleshooting processes

   We expect that programmable network management - along the lines of
   [RFC7426] - will draw more interest as we move forward. For example,
   in [I-D.unify-nfvrg-challenges], the authors identify the need for
   presenting programmable interfaces that accept instructions in a
   standards-supported manner for the Two-way Active Measurement
   Protocol (TWAMP)TWAMP protocol. More specifically, an excellent
   example in this case is traffic measurements, which are extensively
   used today to determine SLA adherence as well as debug and
   troubleshoot pain points in service delivery. TWAMP is both widely
   implemented by all established vendors and deployed by most global
   operators. However, TWAMP management and control today relies solely
   on diverse and proprietary tools provided by the respective vendors
   of the equipment. For large, virtualized, and dynamically
   instantiated infrastructures where network functions are placed
   according to orchestration algorithms proprietary mechanisms for
   managing TWAMP measurements have severe limitations. For example,
   today's TWAMP implementations are managed by vendor-specific,
   typically command-line interfaces (CLI), which can be scripted on a
   platform-by-platform basis. As a result, although the control and
   test measurement protocols are standardized, their respective
   management is not. This hinders dramatically the possibility to
   integrate such deployed functionality in the SP-DevOps concept. In
   this particular case, recent efforts in the IPPM WG
   [I-D.cmzrjp-ippm-twamp-yang] aim to define a standard TWAMP data
   model and effectively increase the programmability of TWAMP
   deployments in the future.


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   Data center DevOps tools, such as those surveyed in [D4.1], developed
   proprietary methods for describing and interacting through interfaces
   with the managed infrastructure. Within certain communities, they
   became de-facto standards in the same way particular CLIs became de-
   facto standards for Internet professionals. Although open-source
   components and a strong community involvement exists, the diversity
   of the new languages and interfaces creates a burden for both vendors
   in terms of choosing which ones to prioritize for support, and then
   developing the functionality and operators that determine what fits
   best for the requirements of their systems.



11. DevOps Performance Metrics

   Defining a set of metrics that are used as performance indicators is
   important for service providers to ensure the successful deployment
   and operation of a service in the software-defined telecom
   infrastructure.

   We identify three types of considerations that are particularly
   relevant for these metrics: 1) technical considerations directly
   related to the service provided, 2) process-related considerations
   regarding the deployment, maintenance and troubleshooting of the
   service, i.e. concerning the operation of VNFs, and 3) cost-related
   considerations associated to the benefits from using a Software-
   Defined Telecom Infrastructure.

   First, technical performance metrics shall be service-dependent/-
   oriented and may address inter-alia service performance in terms of
   delay, throughput, congestion, energy consumption, availability, etc.
   Acceptable performance levels should be mapped to SLAs and the
   requirements of the service users. Metrics in this category were
   defined in IETF working groups and other standardization
   organizations with responsibility over particular service or
   infrastructure descriptions.

   Second, process-related metrics shall serve a wider perspective in
   the sense that they shall be applicable for multiple types of
   services. For instance, process-related metrics may include: number
   of probes for end-to-end QoS monitoring, number of on-site
   interventions, number of unused alarms, number of configuration
   mistakes, incident/trouble delay resolution, delay between service
   order and deliver, or number of self-care operations.

   Third, cost-related metrics shall be used to monitor and assess the
   benefit of employing SDI compared to the usage of legacy hardware


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   infrastructure with respect to operational costs, e.g. possible man-
   hours reductions, elimination of deployment and configuration
   mistakes, etc.

   Finally, identifying a number of highly relevant metrics for DevOps
   and especially monitoring and measuring them is highly challenging
   because of the amount and availability of data sources that could be
   aggregated within one such metric, e.g. calculation of human
   intervention, or secret aspects of costs.





12. Security Considerations

   TBD



13. IANA Considerations

   This memo includes no request to IANA.



14. References

14.1. Informative References

   [NFVMANO] ETSI, "Network Function Virtualization (NFV) Management
             and Orchestration V0.6.1 (draft)", Jul. 2014

   [I-D.aldrin-sfc-oam-framework]  S. Aldrin, R. Pignataro, N. Akiya.
             "Service Function Chaining Operations, Administration and
             Maintenance Framework", draft-aldrin-sfc-oam-framework-02,
             (work in progress), July 2015.

   [I-D.lee-sfc-verification] S. Lee and M. Shin. "Service Function
             Chaining Verification", draft-lee-sfc-verification-00,
             (work in progress), February 2014.

   [RFC7426] E. Haleplidis (Ed.), K. Pentikousis (Ed.), S. Denazis, J.
             Hadi Salim, D. Meyer, and O. Koufopavlou, "Software Defined
             Networking (SDN):  Layers and Architecture Terminology",
             RFC 7426, January 2015



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   [RFC7149] M. Boucadair and C Jaquenet. "Software-Defined Networking:
             A Perspective from within a Service Provider Environment",
             RFC 7149, March 2014.

   [TR228]  TMForum Gap Analysis Related to MANO Work. TR228, May 2014

   [I-D.unify-nfvrg-challenges] R. Szabo et al. "Unifying Carrier and
             Cloud Networks: Problem Statement and Challenges", draft-
             unify-nfvrg-challenges-02 (work in progress), July 2015

   [I-D.cmzrjp-ippm-twamp-yang] Civil, R., Morton, A., Zheng, L.,
             Rahman, R., Jethanandani, M., and K. Pentikousis, "Two-Way
             Active Measurement Protocol (TWAMP) Data Model", draft-
             cmzrjp-ippm-twamp-yang-01 (work in progress), March 2015.

   [D4.1]   W. John et al. D4.1 Initial requirements for the SP-DevOps
             concept, universal node capabilities and proposed tools,
             August 2014.

   [SDNsurvey] D. Kreutz, F. M. V. Ramos, P. Verissimo, C. Esteve
             Rothenberg, S. Azodolmolky, S. Uhlig. "Software-Defined
             Networking: A Comprehensive Survey." To appear in
             proceedings of the IEEE, 2015.

   [DevOpsP] "DevOps, the IBM Approach" 2013. [Online].

   [Y1564]  ITU-R Recommendation Y.1564: Ethernet service activation
             test methodology, March 2011

   [CAP]    E. Brewer, "CAP twelve years later: How the "rules" have
             changed", IEEE Computer, vol.45, no.2, pp.23,29, Feb. 2012.

   [H2014] N. Handigol, B. Heller, V. Jeyakumar, D. Mazieres, N.
             McKeown; "I Know What Your Packet Did Last Hop: Using
             Packet Histories to Troubleshoot Networks", In Proceedings
             of the 11th USENIX Symposium on Networked Systems Design
             and Implementation (NSDI 14), pp.71-95

   [W2011] A. Wundsam, D. Levin, S. Seetharaman, A. Feldmann;
             "OFRewind: Enabling Record and Replay Troubleshooting for
             Networks". In Proceedings of the Usenix Anual Technical
             Conference (Usenix ATC '11), pp 327-340







Meirosu, et al.        Expires April 14, 2016                [Page 19]


Internet-Draft            DevOps Challenges               October 2015


   [S2010] E. Al-Shaer and S. Al-Haj. "FlowChecker: configuration
             analysis and verification of federated Openflow
             infrastructures" In Proceedings of the 3rd ACM workshop on
             Assurable and usable security configuration (SafeConfig
             '10). Pp. 37-44

   [OSandS]  S. Wright, D. Druta, "Open Source and Standards: The Role
             of Open Source in the Dialogue between Research and
             Standardization" Globecom Workshops (GC Wkshps), 2014 ,
             pp.650,655, 8-12 Dec. 2014

   [C2015] CFEngine. Online: http://cfengine.com/product/what-is-
             cfengine/, retrieved Sep 23, 2015.

   [P2015] Puppet. Online: http://puppetlabs.com/puppet/what-is-puppet,
             retrieved Sep 23, 2015.

   [A2015] Ansible. Online: http://docs.ansible.com/ , retrieved Sep
             23, 2015.

   [AK2015] Apache Kafka. Online:
             http://kafka.apache.org/documentation.html, retrieved Sep
             23, 2015.

   [S2015] Splunk. Online: http://www.splunk.com/en_us/products/splunk-
             light.html , retrieved Sep 23, 2015.

   [K2014] J. Kreps. Benchmarking Apache Kafka: 2 Million Writes Per
             Second (On Three Cheap Machines). Online:
             https://engineering.linkedin.com/kafka/benchmarking-apache-
             kafka-2-million-writes-second-three-cheap-machines,
             retrieved Sep 23, 2015.

   [R2015] RabbitMQ. Online: https://www.rabbitmq.com/ , retrieved Oct
             13, 2015

   [Z2015] ZeroMQ. Online: http://zeromq.org/ , retrieved Oct 13, 2015



15. Contributors

   W. John (Ericsson), J. Kim (Deutsche Telekom), S. Sharma (iMinds)






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16. Acknowledgments

   The research leading to these results has received funding from the
   European Union Seventh Framework Programme FP7/2007-2013 under grant
   agreement no. 619609 - the UNIFY project. The views expressed here
   are those of the authors only. The European Commission is not liable
   for any use that may be made of the information in this document.

   We would like to thank in particular the UNIFY WP4 contributors, the
   internal reviewers of the UNIFY WP4 deliverables and Russ White and
   Ramki Krishnan for their suggestions.



   This document was prepared using 2-Word-v2.0.template.dot.

































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17. Authors' Addresses

  Catalin Meirosu
  Ericsson Research
  S-16480 Stockholm, Sweden
  Email: catalin.meirosu@ericsson.com

  Antonio Manzalini
  Telecom Italia
  Via Reiss Romoli, 274
  10148 - Torino, Italy
  Email: antonio.manzalini@telecomitalia.it

  Juhoon Kim
  Deutsche Telekom AG
  Winterfeldtstr. 21
  10781 Berlin, Germany
  Email: J.Kim@telekom.de

  Rebecca Steinert
  SICS Swedish ICT AB
  Box 1263, SE-16429 Kista, Sweden
  Email: rebste@sics.se

  Sachin Sharma
  Ghent University-iMinds
  Research group IBCN - Department of Information Technology
  Zuiderpoort Office Park, Blok C0
  Gaston Crommenlaan 8 bus 201
  B-9050 Gent, Belgium
  Email: sachin.sharma@intec.ugent.be

  Guido Marchetto
  Politecnico di Torino
  Corso Duca degli Abruzzi 24
  10129 - Torino, Italy
  Email: guido.marchetto@polito.it

  Ioanna Papafili
  Hellenic Telecommunications Organization
  Measurements and Wireless Technologies Section
  Laboratories and New Technologies Division
  2, Spartis & Pelika str., Maroussi,
  GR-15122, Attica, Greece
  Buidling E, Office 102


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  Email: iopapafi@oteresearch.gr

  Kostas Pentikousis
  EICT GmbH
  Torgauer Strasse 12-15
  Berlin  10829
  Germany
  Email: k.pentikousis@eict.de

  Steven Wright
  AT&T Services Inc.
  1057 Lenox Park Blvd NE, STE 4D28
  Atlanta, GA 30319
  USA
  Email: sw3588@att.com

  Wolfgang John
  Ericsson Research
  S-16480 Stockholm, Sweden
  Email: wolfgang.john@ericsson.com




























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