Internet DRAFT - draft-khasnabish-sfc-dsi-apps
draft-khasnabish-sfc-dsi-apps
Network Working Group B. Khasnabish
Internet-Draft ZTE TX, Inc.
Intended status: Informational W. Meng
Expires: April 30, 2015 C. Wang
ZTE Corporation
October 27, 2014
Deep Stats Inspection (DSI) and its Applications to Dynamic Service
Function Chaining (D-SFC)
draft-khasnabish-sfc-dsi-apps-00.txt
Abstract
This draft focuses on using Deep Statistics Inspection (DSI) for
smart analytics in Service function chaining. DSI can be utilized
for service chaining in multi-tenant (Data centers) environment,
automated load balancing (ALB), and automated disaster recovery
(ADR).
Status of This Memo
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provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on April 30, 2015.
Copyright Notice
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2. Abbreviations . . . . . . . . . . . . . . . . . . . . . . . 4
1.3. Conventions and Definitions . . . . . . . . . . . . . . . . 5
2. Deep Stats Inspection (DSI) . . . . . . . . . . . . . . . . . . 6
3. Management and Orchestration . . . . . . . . . . . . . . . . . 7
4. API to Deep Stats Storage . . . . . . . . . . . . . . . . . . . 7
5. Deep Stats Lifecycle Management . . . . . . . . . . . . . . . . 8
6. Security Considerations . . . . . . . . . . . . . . . . . . . . 8
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . . 8
8. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . 8
9. References . . . . . . . . . . . . . . . . . . . . . . . . . . 8
9.1. Normative References . . . . . . . . . . . . . . . . . . . 8
9.2. Informative References . . . . . . . . . . . . . . . . . . 9
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1. Introduction
Statistics can help SFC to make a right decision on packet forwarding
behavior. For example, statistics about overload packets on one
Service Function Instance will help SFC to switch path.
DSI is a method of statistics, it can help the service function
forwarder (SFF) with additional smartness for analyzing packet-
stream's (or flow's) path/routing/processing history, forecasted
transit nodes, and destination. The SFF can be physical or virtual
or a combination of both in the chained path.
Network/Service Function Grouping (N/SFG): Random grouping of
network/service functions is commonly utilized for load distribution
and balancing.
Network/Service Function Chaining (N/SFC): Sequential grouping of
network/service functions is commonly utilized for service chaining
(ordered processing).
Benefits: The comprehensive deep stats knowledge help achieve load
balancing dynamically and efficiently. This ultimately will result
in improved (a) resources utilization, and (b) user experience and
satisfaction.
1.1. Scope
The scope of this document is inspection of deep stats and its
utilization in service function chaining (SFC).
Ongoing discussions on virtualization and service chaining in network
can be found in the following IETF and IRTF Websites: SFC
[http://datatracker.ietf.org/wg/sfc/], I2RS
[http://datatracker.ietf.org/wg/i2rs/], SPRING
[http://datatracker.ietf.org/wg/spring/],and SDN-RG
[http://irtf.org/sdnrg].
Traditional deep packet inspection (DPI) can help service chaining
and load balancing only to a certain extent because the actions are
taken only after inspecting the packet (header, trailer, payload,
etc.)
The proposed deep stats inspection (DSI) invokes the concept of both
historical and predicted (based on estimation, time series analysis,
etc.) information about the life-cycle of packets and flows in the
network.
The intelligence obtained from DSI and DPI can be utilized for cost-
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effective and efficient management of (a) the time packets/flows
spend in the network and (b) physical and virtualized network
resources.
Virtual resources management in the context of Cloud and Data Center
(DC) environment using unified API has been discussed in
[I-D.junsheng-opsawg-virtual-resource-management].
1.2. Abbreviations
o ADR: Automated Disaster Recovery
o ALB: Automated Load Balancing
o API: Application Programming Interface
o AR: Auto Regressive
o ARIMA: Auto Regressive Integrated Moving Average
o ARMA: Auto Regressive Moving Average
o DC: Data Center
o DLB: Dynamic Load Balancing
o DPI: Deep Packet Inspection
o DR: Disaster Recovery
o DSI: Deep Stats Inspection
o I2RS: Interface to Routing System
o LB: Load Balancing
o MA: Moving Average
o NC: Network Coding
o NE: Network Element
o PDP: Policy Decision Point
o PEP: Policy Enforcement Point
o SDN: Software-Defined Network/Networking
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o SE: Service Element
o SFC: Service Function Chaining
o VDC: Virtual DC
o VNE: Virtual NE
o VSE: Virtual SE
1.3. Conventions and Definitions
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in [RFC2119].
The following definitions and descriptions of terms are utilized
throughout this draft. When applicable, descriptions of some of the
terms are repeated here from other IETF/IRTF document for
convenience.
o APP --
o APP Interface --
o DPI --
o SFC --
o Multi-Tenant Service Chaining --
o I2RS --
o VNE --
o VSE --
o PDP --
o PEP --
o DSI --
o LB --
o DLB --
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o ALB --
o DR --
o ADR --
2. Deep Stats Inspection (DSI)
The major differences between DPI and DSI:
Traditional deep packet inspection (DPI) can help service chaining
and load balancing only to a certain extent because the actions are
taken only after inspecting the packet (header, trailer, payload,
etc.)
The proposed deep stats inspection (DSI) invokes the concept of both
historical and predicted (based on estimation, time series analysis,
etc.) information about the life-cycle of packets and flows in the
network.
Figure 1 describes a smart (with coded intelligence) flow (or packet
stream) classifier. The intelligence about health, security,
loading, etc. conditions of SFFs, SFs are gathered and stored in a
database and then coded in manner that can be easily utilized by the
flow classifier during inspection of the tags/stats of the incoming
flows and can be adjusted accordingly without impacting user/service
experience. This database provides inputs directly to the flow
director/classifier for dynamically adjusting the tags/stats of the
incoming flows.
coded +-----------+ +-------------------------------------+
intelligence-->| Database | | ,---. ,---. ,---. |
+-----^-----+ | / \ / \ / \ |
| | ( SF1 | ( SF2 ) ( SF3 ) |
---v--- | \ / \ / \ / |
/ | \ | `---' `---' `---' |
packet -->|Classifier +---->| |
\ | / | ,---. ,---. ,---. |
------- | / \ / \ / \ |
| ( SFF ) ( SFF ) ( SFF ) |
| \ / \ / \ / |
| `---' `---' `---' |
+-------------------------------------+
Figure 1: Smart Classifier based on DSI
The classifier categorizes the incoming flows based on the tags
associated with the flows. It may be also be possible to utilize
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some minor statistics that may be available in the flow!_s header.
The categorized flows are sent through a series of service functions
(SFs) or through the network functions (NFs) via the SFF/NF. Note
that both SF/SFF and NF can be physical or virtual or a combination
of both in the path of the service chain. The classifier usually
does not have any knowledge of the loading or other conditions of any
of the forwarders (SFF or NFF) which may cause serious performance
and service bottlenecks or impairments in user experience.
if with a smart (with coded intelligence) flow (or packet stream)
classifier, as mentioned before, the coded intelligence is derived
from monitoring (health, loading, security, etc. conditions) the SFFs
and NFFs.
The following is one possibility for defining the granularity of
monitoring of the conditions.
SFF (or NFF) Health condition = {frail, modest, steady}
SFF (or NFF) Loading condition = {low, medium, high}
SFF (or NFF) Security condition = {at-risk, vulnerable, safe}
The monitoring frequency can be preconfigured to a default value or
dynamically adjusted based on ay set of criteria.
A flow can be routed to an SF through an NF in addition to being
routed directly from an SFF. The SFF can process the incoming flows
in a round-robin fashion or on a first-come-first-serve basis or
using any other intelligent incoming flow processing mechanism. The
statistics tag of the flows can be utilized for intelligent servicing
of the flows in the service function (SFs) which can be physical or
virtual or a combination of both.
3. Management and Orchestration
TBD
4. API to Deep Stats Storage
The added flexibility (due to using an open API) will allow dynamic
navigation of sessions/flows through a variety of network operations
systems and physical/virtual infrastructure network/service elements.
This will help achieve unified and seamless user experience
irrespective of what the underlying network infrastructure is.
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5. Deep Stats Lifecycle Management
TBD
6. Security Considerations
TBD
7. IANA Considerations
This document introduces no additional considerations for IANA.
8. Acknowledgments
The author(s) would like to thank many colleagues for their
discussions and support.
9. References
9.1. Normative References
[I-D.junsheng-opsawg-virtual-resource-management] Chu, J.,
Khasnabish, B.,
Qing, Y., and Y.
Meng, "Virtual
Resource
Management in
Cloud", draft-
junsheng-opsawg-
virtual-resource-
management-00
(work in
progress),
July 2011.
[I-D.karavettil-vdcs-security-framework] Karavettil, S.,
Khasnabish, B.,
Ning, S., and W.
Dong, "Security
Framework for
Virtualized Data
Center Services",
draft-karavettil-
vdcs-security-
framework-05 (work
in progress),
December 2012.
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[I-D.khasnabish-cloud-reference-framework] Khasnabish, B.,
Chu, J., Ma, S.,
So, N., Unbehagen,
P., Morrow, M.,
Hasan, M.,
Demchenko, Y., and
M. Yu, "Cloud
Reference
Framework", draft-
khasnabish-cloud-
reference-
framework-07 (work
in progress),
October 2014.
[RFC2119] Bradner, S., "Key
words for use in
RFCs to Indicate
Requirement
Levels", BCP 14,
RFC 2119,
March 1997.
9.2. Informative References
[RFC3654] Khosravi, H. and
T. Anderson,
"Requirements for
Separation of IP
Control and
Forwarding",
RFC 3654,
November 2003.
[RFC3746] Yang, L., Dantu,
R., Anderson, T.,
and R. Gopal,
"Forwarding and
Control Element
Separation
(ForCES)
Framework",
RFC 3746,
April 2004.
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Authors' Addresses
Bhumip Khasnabish
ZTE TX, Inc.
55 Madison Avenue, Suite 160
Morristown, New Jersey 07960
USA
Phone: +001-781-752-8003
EMail: vumip1@gmail.com, bhumip.khasnabish@ztetx.com
URI: http://tinyurl.com/bhumip/
Wei Meng
ZTE Corporation
No.50 Software Avenue, Yuhuatai District
Nanjing
China
EMail: meng.wei2@zte.com.cn,vally.meng@gmail.com
Cui Wang
ZTE Corporation
No.50 Software Avenue, Yuhuatai District
Nanjing
China
EMail: wang.cui1@zte.com.cn
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