Internet DRAFT - draft-krishnan-nfvrg-real-time-analytics-orch
draft-krishnan-nfvrg-real-time-analytics-orch
Internet Research Task Force (IRTF) R. Krishnan
Internet Draft Brocade
Category: Informational Dilip Krishnaswamy
IBM Research
D. R. Lopez
Telefonica I+D
Asif Qamar
Evolv
Steven Wright
AT&T
Norival Figueira
Brocade
Expires: April 2015 November 11, 2014
NFV Real-time Analytics and Orchestration: Use Cases and
Architectural Framework
draft-krishnan-nfvrg-real-time-analytics-orch-01
Abstract
One of the key goals of NFV is to optimize the infrastructure
resource usage while driving operational simplicity. Real-time
analytics providing insight into various components such as compute
(e.g. dynamic CPU utilization), storage (e.g. dynamic capacity
usage), network (e.g. dynamic bandwidth utilization), energy (e.g.
dynamic power consumption) is key to not only providing visibility
into the NFV infrastructure and thus driving operational simplicity
but also optimizing resource usage for the purposes of
orchestration. This draft focusses on use cases and architectural
framework for real-time analytics and orchestration including Big
Data predictive analytics for addressing the aforementioned
requirements.
Status of this Memo
This Internet-Draft is submitted to IETF in full conformance with
the provisions of BCP 78 and BCP 79.
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Internet-Draft NFV Real-time Analytics and Orchestration October 2013
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Table of Contents
1. Introduction...................................................3
2. Real-time Analytics and Orchestration Use Cases................4
2.1. Enhancements to Real-time Analytics Application...........5
2.1.1. Distributed Predictive Analytics.....................5
2.1.2. Detecting Noisy Neighbors............................5
2.1.3. Addressing security issues due to inconsistent
configuration...............................................6
3. Real-time Analytics and Orchestration Architectural Framework..6
4. Summary........................................................6
5. Future Work....................................................6
6. IANA Considerations............................................6
7. Security Considerations........................................6
8. Contributors...................................................6
9. Acknowledgements...............................................6
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10. References....................................................7
10.1. Normative References.....................................7
10.2. Informative References...................................7
Authors' Addresses................................................8
1. Introduction
Operator Network Function Virtualization Infrastructure Point-of-
Presence (NFVI-PoP) locations [ETSI-NFV-TERM] often have capacity,
energy and other constraints. Thus, optimizing overall resource
usage is an important requirement [ETSI-NFV-REQ]. The general case
must consider a distributed (elastic) VNF NFVI platform
implementation where VMs running for different VNFs (with different
characteristics) can co-exist in the same physical server. This case
must address the goal of optimizing overall resource usage through
mechanisms like bin-packing [BIN-PACK].
In this context, some of the important challenges faced are:
. Performance issues due to noisy neighbor effect, where a VM
running for a VNF can affect the VM(s) running for another VNF.
. Security issues, especially due to inconsistent configuration
in a dynamic environment where one VNF could affect others.
. Energy Efficiency given that servers have substantial idle
power usage.
. Resources used (Compute, bandwidth, storage) for the real-
time analytics in comparison to the VNF payload resource usage.
The purpose of this document is two-fold. First, it intends to
discuss various use cases to describe the above challenges. Second,
it will depict an architectural framework for real-time analytics
and orchestration, applicable to the above use cases in a multi-
vendor environment.
For the purposes of real-time analytics for orchestration, various
metrics need to be collected, stored and analyzed.
Metrics collection: Metric collection may occur at different periods
during the lifecycle of the VNF. Metric collection during an
onboarding process in a controlled load configuration may provide a
baseline for characterization of "normal" operational performance.
Such baseline characterizations may be useful for detection "out of
normal" performance at a later point in the VNF lifecycle.
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Metrics storage: It is recommended to store and analyze metrics
locally to minimize the costs of backhaul to remote locations.
Metrics analysis: The assumption here is that the metrics to be
collected for analysis would be VNF independent in the sense that
they would apply regardless of the type of VNF. Metrics that are
specific to particular types of VNF are more appropriate for service
specific diagnostics.
2. Real-time Analytics and Orchestration Use Cases
A real-time analytics application periodically collects metrics
(also called information in this document) from individual VMs,
VNFs, physical servers, network elements etc. regarding various sub-
systems such as compute (e.g. dynamic CPU utilization), storage
(e.g. dynamic capacity usage), network (e.g. dynamic bandwidth
utilization), energy (e.g. dynamic power consumption) through
polling. The real-time analytics application computes the average
utilization for VMs, VNFs, physical servers, networks etc. regarding
the various sub-systems such as compute (e.g. average CPU
utilization), storage (e.g. average capacity usage), network (e.g.
average bandwidth utilization), energy (e.g. average power
consumption).
Using the average utilization information, the real-time analytics
application provides real-time visibility into the operating point
of the VNF in the NFV Node thus driving operational efficiency.
The NFV orchestrator uses the average utilization information from
the real-time analytics application to determine the appropriate
time to scale up/down the running software instances. Typically the
thresholds for scale up/down are manually programmed into the system
- this may not be performance optimal since the workloads and
deployment scenarios can substantially vary.
In addition, predictive analytics based on machine learning
techniques [MACHINE-LEARNING-BOOK] can be used by the real-time
analytics application to automatically determine the appropriate
thresholds for scale up/down the running software instances for
differing workloads including events related to social behavior
(think of a YouTube video going viral) and deployment scenarios.
This information can be used by the orchestrator for optimizing
overall performance and maximizing energy efficiency. Maximizing
energy efficiency comes from the fact that by determining the
appropriate thresholds for scale up/down the workloads can be
consolidated into a minimum set of physical resources so the rest of
the unused physical resources can be completely powered off to avoid
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any idle power consumption. [SPEC-BENCHMARK] analyzes the power
profile of physical servers from various vendors; the active idle
power consumption of physical servers could be as much as 30%.
2.1. Enhancements to Real-time Analytics Application
2.1.1. Distributed Predictive Analytics
A real-time analytics application could be notified of significant
events by individual running software instances of VMs, VNFs etc. or
by infrastructure elements such as physical servers, hypervisors
etc. This helps reduce the rate of polling by the real-time
analytics application and also helps in reacting to significant
events such as overload much faster. The challenge in this case is
to determine the appropriate thresholds (e.g. average power
consumption has been higher than x Watts for t seconds) for event
notification.
Predictive analytics engines which use machine learning techniques
[MACHINE-LEARNING-BOOK] can be used to determine the appropriate
thresholds per running software instance and infrastructure element
for different workloads and deployment scenarios. These predictive
analytics engines can run in various nodes in the infrastructure in
a distributed predictive analytics architectural framework.
2.1.2. Detecting Noisy Neighbors
In the context of multiple VNFs, "Noisy Neighbor Effect" could be
defined as follows: the VM running for one VNF can affect the
performance of a VM running for another VNF in the case where they
are using the same physical resources (physical servers, physical
network elements). A real-time analytics application could help in
detecting and mitigating the noisy neighbor effect. A good example
is the case where the VMs running for two VNFs share the same
physical server, are memory access intensive (load balancers,
firewalls etc.) and have correlated memory access patterns for the
given workload and deployment scenario.
Real-time big data analytics techniques [RT-ANALYTICS-BOOK] can be
used by the analytics application to determine such correlation
patterns which can affect performance in real-time. Additionally,
predictive analytics based on machine learning techniques [MACHINE-
LEARNING-BOOK] can be used to predict the frequency and duration of
such correlation patterns. This information can be used to create
dynamic anti-affinity rules for VM placement and migration including
redundancy considerations - e.g. VMs of VNF "A" cannot co-exist with
VMs of VNF "B".
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2.1.3. Addressing security issues due to inconsistent configuration
NFV configuration is expected to be dynamic, especially in the edge
NFV PoPs where capacity is limited; a very good example is handling
a viral event such as mobile gaming application. While autonomic
networking techniques could be used to automate the configuration
process including modular updates, it is important to take into
account that incomplete and/or inconsistent configuration may lead
to security issues. Distributed VNF implementations (e.g. VMs of
single VNF which span different physical servers) typically use an
eventually consistent configuration model [CAP-THEOREM] for
scalability reasons -- this poses additional security challenges.
Real-time analytics techniques [RT-ANALYTICS-BOOK] can be used by
the analytics application to determine communication pattern
anomalies due to incomplete and/or inconsistent configuration in
real-time by analyzing event logs. Additionally, predictive
analytics based on machine learning techniques [MACHINE-LEARNING-
BOOK] can be used to predict the frequency and duration of such
communication pattern anomalies. A simple example is a flow-specific
firewall rule which never got installed due to reasons such as
control plane messaging issues, data plane table full condition etc.
3. Real-time Analytics and Orchestration Architectural Framework
TBD
4. Summary
TBD
5. Future Work
TBD
6. IANA Considerations
This draft does not have any IANA considerations.
7. Security Considerations
8. Contributors
9. Acknowledgements
None.
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10. References
10.1. Normative References
10.2. 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] "Terminology for Main Concepts in NFV,"
http://www.etsi.org/deliver/etsi_gs/NFV/001_099/003/01.01.01_60/gs_n
fv003v010101p.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
[OPENSTACK-CELIOMETER-MEASUREMENT] "OpenStack Celiometer,"
http://docs.openstack.org/developer/ceilometer/measurements.html
[OPENSTACK-NOVA-COMPUTE] "OpenStack Nova,"
https://wiki.openstack.org/wiki/Nova
[NFV-MANO-SPEC] "NFV Management and Orchestration Framework
Specification,"
http://docbox.etsi.org/ISG/NFV/Open/Latest_Drafts/NFV-MAN001v061-
%20management%20and%20orchestration.pdf
[BIN-PACK] Coffman, Jr., E., M. Garey, and D. Johnson. Approximation
Algorithms for Bin-Packing -- An Updated Survey. In Algorithm Design
for Computer System Design, ed. by Ausiello, Lucertini, and
Serafini. Springer-Verlag, 1984.
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[SPEC-BENCHMARK] "SPEC Benchmark Results: HP Proliant DL380p Rack
Server," http://i.dell.com/sites/doccontent/shared-content/data-
sheets/en/Documents/Comparing-Dell-R720-and-HP-Proliant-DL380p-Gen8-
Servers.pdf
[CAP-THEOREM] Eric Brewer, "CAP twelve years later: How the "rules"
have changed", IEEE Explore, Volume 45, Issue 2 (2012), pg. 23-29.
[MACHINE-LEARNING-BOOK] Ian H. Witten et al., "Practical Machine
Learning Tools and Techniques, Third Edition," Morgan Kaufmann, 2011
[RT-ANALYTICS-BOOK] Byron Ellis, "Real-Time Analytics: Techniques to
Analyze and Visualize Streaming Data," Wiley, 2014
Authors' Addresses
Ram (Ramki) Krishnan
Brocade Communications
ramk@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
Asif Qamar
Evolv
asif@asifqamar.com
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