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Internet Research Task Force (IRTF)                         R. Krishnan
Internet Draft                                                     Dell
Category: Experimental                                      N. Figueira
                                                                Brocade
                                                     Dilip Krishnaswamy
                                                           IBM Research
                                                            D. R. Lopez
                                                         Telefonica I+D
                                                          Steven Wright
                                                                   AT&T
                                                           Tim Hinrichs
                                                                  Styra
                                                      Ruby Krishnaswamy
                                                                 Orange
                                                             Arun Yerra
                                                                   Dell

Expires: March 2016                                       March 2, 2016


   NFVIaaS Architectural Framework for Policy Based Resource Placement
                              and Scheduling

             draft-krishnan-nfvrg-policy-based-rm-nfviaas-06

Abstract

   One of the goals of Network Functions Virtualization (NFV) is to
   offer the NFV infrastructure as a service to other SP customers -
   this is called NFVIaaS. Virtual Network Function (VNF) deployment in
   this paradigm will drive support for unique placement policies,
   given VNF's stringent service level specifications (SLS) required by
   customer SPs. Additionally, NFV DCs often have capacity, energy and
   other constraints - thus, optimizing the overall resource usage
   based on policy is an important part of the overall solution. The
   purpose of this document is to depict an architectural framework for
   policy based resource placement and scheduling in the context of
   NFVIaaS.

Status of this Memo

   This Internet-Draft is submitted to IETF in full conformance with
   the provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF), its areas, and its working groups. Note that




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Internet-Draft NFVIaaS Policy Resource Placement & Scheduling March 2016

   other groups may also distribute working documents as Internet-
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   This Internet-Draft will expire in March 2016.

Copyright Notice

   Copyright (c) 2016 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
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   (http://trustee.ietf.org/license-info) in effect on the date of
   publication of this document. Please review these documents
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   respect to this document.

Conventions used in this document

Table of Contents

   1. Introduction...................................................3
   2. NFVIaaS Architectural Framework for Policy Based Resource
   Placement and Scheduling..........................................3
   3. System Analysis in a OpenStack Framework.......................5
      3.1. Compute Monitoring with OpenStack - Use Case and Example..5
      3.2. Joint Network and Compute Awareness with OpenStack - Use
      Case...........................................................9
   4. Related Work..................................................10
   5. Summary.......................................................10
   6. Future Work...................................................10
   7. IANA Considerations...........................................10
   8. Security Considerations.......................................11
   9. Contributors..................................................11
   10. Acknowledgements.............................................11
   11. References...................................................11



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      11.1. Normative References....................................11
      11.2. Informative References..................................11
   Authors' Addresses...............................................12

1. Introduction

   One of the goals of NFV [ETSI-NFV-WHITE] is to offer the NFV
   infrastructure as a service to other SP customers - this is called
   NFVIaaS [ETSI-NFV-USE-CASES]. In this context, it may be desirable
   for a Service Provider to run virtual network elements (e.g.,
   virtual routers, virtual firewalls, and etc. - these are called
   Virtual Network Functions - VNF) as virtual machine instances inside
   the infrastructure of another Service Provider. In this document, we
   call the former a customer SP and the latter an NFVIaaS SP.

   There are many reasons for a customer SP to require the services of
   an NFVIaaS SP, including: to meet performance requirements (e.g.,
   latency or throughput) in locations where the customer SP does not
   have physical data center presence, to allow for expanded customer
   reach, regulatory requirements, and etc.

   As VNFs are virtual machines, their deployment in such NFVIaaS SPs
   would share some of the same placement restrictions (i.e., placement
   policies) as those intended for Cloud Services. However, VNF
   deployment will drive support for unique placement policies, given
   VNF's stringent service level specifications (SLS) required/imposed
   by customer SPs. Additionally, NFV DCs or NFV PoPs [ETSI-NFV-TERM]
   often have capacity, energy and other constraints - thus, optimizing
   the overall resource usage based on policy is an important part of
   the overall solution.

   The purpose of this document is to depict an architectural framework
   for policy based resource placement and scheduling in the context of
   NFVIaaS.

2. NFVIaaS Architectural Framework for Policy Based Resource Placement
   and Scheduling

   The policy engine performs policy-based resource placement and
   scheduling of Virtual Machines (VMs) in support for NFVIaaS. It
   determines optimized placement and scheduling choices based on the
   constraints specified in the policy. The NFVIaaS Architectural
   Framework for Policy Based resource placement and scheduling is
   based on the NFV policy architectural framework [IRTF-NFV-POLICY-
   ARCH].This is depicted in Figure 1.





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   In one instantiation of this architecture, the policy engine would
   interface with the Measurement Collector to periodically retrieve
   instantaneous per-server CPU utilization, which it would then use to
   compute a table of per-server average CPU utilization. In an
   alternative instantiation of this architecture, the measurement
   collector could itself compute per-server average CPU utilization.
   The latter approach reduces overhead, since it avoids too frequent
   pulling of stats from Ceilometer. The policy engine evaluates such
   policies based on an event trigger or a based on a programmable
   timer.

   Other average utilization parameters such as VM CPU utilization, VM
   Memory utilization, VM disk read IOPS, Network utilization/latency
   etc. could be used by the policy engine to enforce other types of
   placement policies.

                  |---------------------------------------|

                  |           Policy Engine               |

                  |Performs resource placement            |

                  |and scheduling function (proactive and |

                  |dynamic policy enforcement)            |

                  |--------------------|------------------|

                                       |

                                       |

                           |-----------|---------------|

                           |  Measurement   Collector  |

                           |  VM DB - CPU              |

                           |  Utilization, Network     |

                           |  Utilization/Latency etc. |

                           |---------------------------|

   Figure 1: NFVIaaS Architecture for Policy Based Resource Placement
   and Scheduling




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   In an ETSI NFV Architectural Framework [ETSI-NFV-ARCH][NFV-MANO-
   SPEC], Policy Engine is part of the Orchestrator and the Measurement
   Collector is part of the Virtual Infrastructure Manager (VIM).

3. System Analysis in a OpenStack Framework

3.1. Compute Monitoring with OpenStack - Use Case and Example

   Consider an NFVIaaS SP that owns a multitude of mini NFV data
   centers managed by OpenStack [OPENSTACK] where,

   -  The Policy Engine function is performed by OpenStack Congress
     [OPENSTACK-CONGRESS-POLICY-ENGINE]

   -  The Measurement Collector function is performed by OpenStack
     Celiometer [OPENSTACK-CELIOMETER-MEASUREMENT]

   -  The Policy Engine has access to the OpenStack Nova database that
     stores records of mapping of virtual machines to physical servers.

   Exemplary Mini NFV DC configuration:

   An exemplary mini NFV DC configuration is depicted below.

   -  There are 210 physical servers in 2U rack server configuration
   spread over 10 racks.

   -  There are 4 types of physical servers each with a different
   system configuration and from a particular manufacturer. It is
   possible that the servers are from the same or different
   manufacturer. For the purpose of this example, server type 1 is
   described further. Server type 1 has 32 virtual CPUs and 128GB DRAM
   from manufacturer x. Assume 55 physical servers of type 1 per mini
   NFV DC.

   -  There are 2 types of instances large.2 and large.3, which are
   described in the table below. Each parameter has a minimum guarantee
   and a maximum usage limit.

   |--------|----------------- |-------------------|---------------|

   |Instance|Virtual CPU Units |Memory (GB)        |Minimum/Maximum|

   |Type    |Minimum Guarantee |Minimum Guarantee  |Physical Server|

   |        |/Maximum Usage    |/Maximum Usage     |Utilization (%)|




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   |--------|------------------|-------------------|---------------|

   |large.2 |     0/4          |    0/16           |  0/12.5       |

   |large.3 |     0/8          |    0/32           |  0/25         |

   |--------|------------------|-------------------|---------------|

                     Table 1: NFVIaaS Instance Types

   For the purpose of this example, the Mini NFV DC topology is
   considered static -- the above topology, including the network
   interconnection, is available through a simple file-based interface.

   Policy 1 (an exemplary NFV policy):

   Policy 1 is an exemplary NFV policy. In a descriptive language,
   Policy 1 is as follows - "For physical servers of type 1, there can
   be at most only one active physical server with average overall
   utilization less than 50%."

   The goal of this policy is to address the energy efficiency
   requirements described in the ETSI NFV Virtualization Requirements
   [ETSI-NFV-REQ].

   Policy 1 is an example of reactive enforcement.

   Policy 2 (another exemplary NFV policy):

   Policy 2 is necessary to protect NFV servers from failures. In this
   example we consider failures of physical servers. Policy 2 is as
   follows - "Not more than one VM of the same HA group must be
   deployed on the same physical server".

   Note: There may be conditions (according to current Mini DC usage
   and policies of type 2 that are currently active) when there may not
   be any placement solution respecting both policies. It may be better
   to reformulate Policy 1? For example, "Minimize the number of
   physical servers with average overall utilization less than 50%"

   Policy 2 is an example of proactive and reactive enforcement.
   Various Module Interactions for Policy 1:

   The various module interactions with respect the architectural
   framework in Figure 1 for Policy 1 is described below.





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   The policy calls for the identification of servers by type.
   OpenStack Congress would need to support server type, average CPU
   utilization, and be able to support additional performance
   parameters (in the future) to support additional types of placement
   policies. OpenStack Congress would run the policy periodically or
   based on events such as deleting/adding VMs etc. Initially, we could
   use a periodic timer based approach. In case OpenStack Congress
   detects a violation, it determines optimized placement and
   scheduling choices so that the policy is not violated.

   OpenStack Congress could interface with OpenStack Celiometer to
   periodically retrieve instantaneous per-server CPU utilization,
   which it would then use to compute a table of per-server average CPU
   utilization. Alternatively, OpenStack Celiometer could itself
   compute per-server average CPU utilization which could be used by
   OpenStack Congress.

   The proposed module interactions in this NFVIaaS placement policy
   example are as depicted in the architectural framework in Figure 1.

   A key goal of Policy 1 above is to ensure that servers are not kept
   under low utilization, since servers have a non-linear power profile
   and exhibit relatively higher power wastage at lower utilization.
   For example, in the active idle state as much as 30% of peak power
   is consumed. At the physical server level, instantaneous energy
   consumption can be accurately measured through IPMI standard. At a
   customer instance level, instantaneous energy consumption can be
   approximately measured using an overall utilization metric, which is
   a combination of CPU utilization, memory usage, I/O usage, and
   network usage. Hence, the policy is written in terms of overall
   utilization and not power usage.

   The following example combines Policy 1 and Policy 2.

   For an exemplary maximum usage scenario, 53 physical servers could
   be under peak utilization (100%), 1 server (server-a) could be under
   partial utilization (62.5%) with 2 instances of type large.3 and 1
   instance of type large.2 (this instance is referred as large.2.X1),
   and 1 server (server-b) could be under partial utilization (37.5%)
   with 3 instances of type large.2. Call these three instances
   large.2.X2, large.2.Y and large.2.Z

   One HA-group has been configured and two large.2 instances belong to
   this HA-group. To enforce Policy 2 large.2.X1 and large2.X2 that
   belong to the HA-group have been deployed in different physical
   servers, one in server-a and a second in server-b.




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   When one of the large.3 instances mapped to server-a is deleted from
   physical server type 1, Policy 1 will be violated, since the overall
   utilization of server-a falls to 37,5%, since two servers are
   underutilized (below 50%)

   OpenStack Congress, on detecting the policy violation, uses various
   constraint based placement techniques to find the new placement(s)
   for physical server type 1 to address Policy 1 violation without
   breaking Policy 2. Constrained based placement will be explored in a
   convex optimization framework [CONVEX-OPT]; some of the algorithms
   which would be considered are linear programming [LINEAR-PROGRAM],
   branch and bound [BRANCH-AND-BOUND], interior point methods,
   equality constrained minimization, non-linear optimization etc.

   Various new placement(s) are described below.

   1) New placement 1: Move 2 of three instances of large.2 running on
   server-b to server-a. Overall utilization of server-a - 62,5%.
   Overall utilization of server-b - 25%. large.2.X2 must not be one of
   the migrated instances.

   2) New placement 2: Move 1 instance of large.3 to server-b. Overall
   utilization of server-a - 12,5%. Overall utilization of server-b -
   62.5%.

   A third solution consisting of moving 3 large.2 instances to server-
   a cannot be adopted since this breaks Policy 2. Another policy
   minimizing the number of migrations could allow choosing between
   solution (1) and (2).

   New placements 2 and 3 could be considered optimal, since they
   achieve maximal bin packing and open up the door for turning off
   server-a or server-b and maximizing energy efficiency.

   To detect violations of Policy 1, an example of a classification
   rule is expressed below in Datalog, the policy language used by
   OpenStack Congress.

   Database table exported by the Resource Placement and Scheduler for
   Policy 1 and Policy 2:

   The database table exported by the Resource Placement and Scheduler
   for Policy 1 is below.

   server_utilization (physical_server, overall_util)





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   -  Each database entry has the physical server and the calculated
   average overall utilization.

   vm_host_mapping(vm, server)

   -  Each database entry gives the physical server on which VM is
     deployed.

   anti-affinity_group(vm, group)

   -  Each entry gives the anti-affinity group to which a VM belongs.

   Policy 1 (in Datalog [DATALOG] policy language):

   Policy 1 in a Datalog policy language is as follows.

   error (physical_server) :-

   nova [OPENSTACK-NOVA-COMPUTE]: node (physical_server, "type1"),

   resource placement and scheduler: server_utilization
   (physical_server, overall_util < 50)

   Policy 2 (in Datalog policy language):

   error(vm) :-

   anti-affinity_group(vm1, grp1),

   anti-affinity_group(vm2, grp2),

   grp1 != grp2,

   nova: vm host mapping(vm1, server-1),

   nova:  vm host mapping(vm2,server-2),

   server-1 == server-2

3.2. Joint Network and Compute Awareness with OpenStack - Use Case

   There are several NFV DCs such as mobile base stations, small
   central offices, small branch office locations etc. Having an
   OpenStack Controller in each of these locations increases the
   management complexity and the DC capacity needs. The idea is to have
   the OpenStack compute and network nodes in these DC locations and
   manage them centrally through an OpenStack Controller node through a



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   larger central office location in the same metro area. The key
   considerations here are that the OpenStack management network
   extends over the metropolitan area network (MAN) and is an in-band
   network where the data and management traffic use the same physical
   pipe; typical MAN distances are within 100 miles but could have a
   geographic variation.

   Across MAN, some of the key management network considerations are
   latency and bandwidth impacting various operations such as
   configuration, VM image transfer, monitoring data collection,
   backing up of logs etc. OpenStack Neutron can provide the hooks for
   reporting MAN bandwidth and latency which will be abstracted by the
   enhanced OpenStack Nova scheduler. The network connection between
   NFV DCs can be modelled as Neutron end-points. During the validation
   phase, the enhanced OpenStack Nova scheduler (part of the OpenStack
   Controller node) in the central DC can determine if the
   compute/network nodes in the small DC are manageable remotely based
   on the service SLA requirements specified by the Orchestrator; each
   of the small DCs could have different bandwidth/latency depending on
   the network topology. During runtime, the enhanced OpenStack Nova
   scheduler periodically monitors the MAN bandwidth and latency
   variations and reports any exceptions. The enhanced scheduler can
   measure the overall bandwidth usage across MAN so that it can
   determine the optimized time window(s) during the day for backing up
   logs, detailed monitoring data etc. The enhanced scheduler can also
   measure overall latency usage across MAN so that it can place the
   high availability instances across DCs meeting the service SLA
   requirements. Dynamic measurement of MAN bandwidth and latency can
   be implemented using a vendor specific OpenStack Neutron plugin
   which typically interfaces with an SDN Controller [LAYERED-SDN].
   [Y.1731-monitoring] is typically used by switches for performance
   monitoring across physical/logical Ethernet network end points.

4. Related Work

   A related proof of concept in ETSI NFV on placement and scheduling
   is [ETSI-NFV-POC-PLACEMENT].

5. Summary

6. Future Work

   TBD

7. IANA Considerations

   This draft does not have any IANA considerations.



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8. Security Considerations

9. Contributors

   Hesham ElBakoury

   Huawei

   Hesham.ElBakoury@huawei.com

10. Acknowledgements

   The authors would like thank Prabhakar Kudva and Gokul Kandiraju for
   the proof of concept effort.

11. References

11.1. Normative References

11.2. Informative References

   [ETSI-NFV-WHITE]  "ETSI NFV White Paper,"
   http://portal.etsi.org/NFV/NFV_White_Paper.pdf

   [ETSI-NFV-USE-CASES] "ETSI NFV Use Cases,"
   http://www.etsi.org/deliver/etsi_gs/NFV/001_099/001/01.01.01_60/gs_N
   FV001v010101p.pdf

   [ETSI-NFV-REQ]   "ETSI NFV Virtualization Requirements,"
   http://www.etsi.org/deliver/etsi_gs/NFV/001_099/004/01.01.01_60/gs_N
   FV004v010101p.pdf

   [ETSI-NFV-ARCH]   "ETSI NFV Architectural Framework,"
   http://www.etsi.org/deliver/etsi_gs/NFV/001_099/002/01.01.01_60/gs_N
   FV002v010101p.pdf

   [ETSI-NFV-TERM] "ETSI NFV: Terminology for main concepts in NFV,"
   http://www.etsi.org/deliver/etsi_gs/NFV/001_099/003/01.02.01_60/gs_N
   FV003v010201p.pdf

   [OPENSTACK]  "OpenStack Open Source Software,"
   https://www.openstack.org/

   [OPENSTACK-CONGRESS-POLICY-ENGINE] "A policy as a service open
   source project in OpenStack,"
   https://wiki.openstack.org/wiki/Congress




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   [OPENSTACK-CELIOMETER-MEASUREMENT] "OpenStack Celiometer,"
   http://docs.openstack.org/developer/ceilometer/measurements.html

   [OPENSTACK-NOVA-COMPUTE] "OpenStack Nova,"
   https://wiki.openstack.org/wiki/Nova

   [LINEAR-PROGRAM] Dmitris Alevras and Manfred W. Padberg, "Linear
   Optimization and Extensions: Problems and Solutions," Universitext,
   Springer-Verlag, 2001.

   [BRANCH-AND-BOUND] "Fundamentals of Algorithmics," G. Brassard and
   P. Bratley.

   [NFV-MANO-SPEC] "NFV Management and Orchestration Framework
   Specification,"
   http://docbox.etsi.org/ISG/NFV/Open/Latest_Drafts/NFV-MAN001v061-
   %20management%20and%20orchestration.pdf

   [CONVEX-OPT] "Convex Optimization,"
   https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf

   [ETSI-NFV-POC-PLACEMENT] "ETSI NFV Proof of Concept on Placement and
   Scheduling,"
   http://nfvwiki.etsi.org/index.php?title=Constraint_based_Placement_a
   nd_Scheduling_for_NFV/Cloud_Systems

   [DATALOG] Ceri, S. et al., "What you always wanted to know about
   Datalog (and never dared to ask)," Knowledge and Data Engineering,
   IEEE Transactions on  (Volume:1 ,  Issue: 1 )

   [IRTF-NFV-POLICY-ARCH] Figueira, N. et al., "Policy Architecture and
   Framework for NFV and Cloud Services,"

   [Y.1731-monitoring] "OAM functions and mechanisms for Ethernet-based
   networks," https://www.itu.int/rec/T-REC-Y.1731/en

   [LAYERED-SDN] "Cooperating Layered Architecture for SDN,"
   https://datatracker.ietf.org/doc/draft-contreras-sdnrg-layered-sdn/

Authors' Addresses

   Ram (Ramki) Krishnan
   Dell Inc.
   ramkri123@gmail.com

   Norival Figueira
   Brocade Communications



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   nfigueir@Brocade.com

   Dilip Krishnaswamy
   IBM Research
   dilikris@in.ibm.com

   Diego Lopez
   Telefonica I+D
   Don Ramon de la Cruz, 82
   Madrid, 28006, Spain
   +34 913 129 041
   diego.r.lopez@telefonica.com

   Steven Wright
   AT&T
   sw3588@att.com

   Tim Hinrichs
   Styra
   tim@styra.com

   Ruby Krishnaswamy
   Orange
   ruby.krishnaswamy@orange.com

   Arun Yerra
   Dell Inc.
   arun.yerra@dell.com





















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