IPFIX R. Krishnan
Internet Draft D. Meyer
Intended status: Informational Brocade Communications
Expires: August 2013 Ning So
February 17, 2013 Tata Communications
Flow Aware Packet Sampling Techniques
draft-krishnan-ipfix-flow-aware-packet-sampling-01.txt
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Abstract
The demands on the networking infrastructure and thus the
switch/router bandwidths are growing exponentially; the drivers are
bandwidth hungry rich media applications, inter data center
communications etc. Using sampling techniques, for a given sampling
rate, the amount of samples that need to be processed is increasing
exponentially. This draft suggests flow aware sampling techniques for
handling various scenarios with minimal sampling overhead.
Table of Contents
1. Introduction...................................................2
1.1. Conventions used..........................................3
1.2. Acronyms..................................................3
1.3. Terminology...............................................3
2. Flow Aware Packet Sampling.....................................3
2.1. Large Flow Recognition....................................4
2.1.1. Flow Identification..................................4
2.1.2. Criteria for Identifying a Large Flow................5
2.1.3. Automatic Recognition................................5
2.1.4. Simulation...........................................7
3. Acknowledgements...............................................7
4. IANA Considerations............................................7
5. Security Considerations........................................7
6. Data Model Considerations......................................7
7. References.....................................................7
7.1. Normative References......................................7
7.2. Informative References....................................8
Authors' Addresses...............................................10
1. Introduction
Packet sampling techniques in switches and routers provide an
effective mechanism for approximate detection of various types of
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flows -- long-lived large flows and other flows (which include long-
lived small flows, short-lived small/large flows) with minimal packet
replication bandwidth overhead. A large percentage of the packet
samples comprise of long-lived large flows and a small percentage of
the packet samples comprise of other flows. The long-lived large
flows aka top-talkers consume a large percentage of the bandwidth and
small percentage of the flow space. The other flows, which are the
typical cause of security threats like Denial of Service (DOS)
attacks, Scanning attacks etc., consume a small percentage of the
bandwidth and a large percentage of the flow space. This draft
explores light-weight techniques for automatically detecting the top-
talkers in real-time with a high degree of accuracy and sampling only
the other flows -- this makes security threat detection more
effective with minimal sampling overhead.
1.1. Conventions used
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 RFC 2119 [RFC2119].
1.2. Acronyms
DOS: Denial of Service
GRE: Generic Routing Encapsulation
MPLS: Multi Protocol Label Switching
NVGRE: Network Virtualization using Generic Routing Encapsulation
TCAM: Ternary Content Addressable Memory
STT: Stateless Transport Tunneling
VXLAN: Virtual Extensible LAN
1.3. Terminology
Large flow(s): long-lived large flow(s)
Small flow(s): long-lived small flow(s) and short-lived small/large
flow(s)
2. Flow Aware Packet Sampling
The steps in flow aware packet sampling are described below
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1) Large Flow Recognition in switches and routers:
From a bandwidth and time duration perspective, in order to
identify large flows in switches and routers, we define an
observation interval and observe the bandwidth of the flow over
that interval. A flow that exceeds a certain minimum bandwidth
threshold over that observation interval would be considered a
large flow. For identifying large flows, use the techniques
described in Section 2.1. This helps in identifying the large
flows aka top-talkers in real-time with a high degree of accuracy
in switches and routers.
2) Large Flow Classification:
The identified large flows can be broadly classified into 2
categories as detailed below.
a. Well behaved (steady rate) large flows, e.g. video streams
b. Bursty (fluctuating rate) large flows e.g. Peer-to-Peer
traffic
The large flows can be sampled at a low rate for further analysis
or need not be sampled. If desired, the large flows could be
exported to a central entity, for e.g. sFlow Collector, for
further analysis.
3) Small Flow Processing:
The small flows (excluding the large flows) can be sampled at a
normal rate. The small flows can be examined for determining
security threats like DOS attacks, Scanning attacks etc. [LANCOPE]
Thus, we can see that, security threat detection is possible with
minimal sampling overhead.
For packet sampling, it is recommended to use PSAMP [RFCs 5474-5477]
or sFlow [sFlow-v5].
2.1. Large Flow Recognition
2.1.1. Flow Identification
A flow (large flow or small flow) can be defined as a sequence of
packets for which ordered delivery should be maintained. Flows are
typically identified using one or more fields from the packet header
from the following list:
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. Layer 2: source MAC address, destination MAC address, VLAN ID.
. IP header: IP Protocol, IP source address, IP destination
address, flow label (IPv6 only), TCP/UDP source port, TCP/UDP
destination port.
. MPLS Labels.
For tunneling protocols like GRE, VXLAN, NVGRE, STT, etc., flow
identification is possible based on inner and/or outer headers. The
above list is not exhaustive. The mechanisms described in this
document are agnostic to the fields that are used for flow
identification.
2.1.2. Criteria for Identifying a Large Flow
From a bandwidth and time duration perspective, in order to identify
large flows we define an observation interval and observe the
bandwidth of the flow over that interval. A flow that exceeds a
certain minimum bandwidth threshold over that observation interval
would be considered a large flow.
The two parameters -- the observation interval, and the minimum
bandwidth threshold over that observation interval -- should be
programmable in a switch or a router to facilitate handling of
different use cases and traffic characteristics. For example, a flow
which is at or above 100 Mbps for a time period of at least 5 minutes
could be declared a large flow.
An optional parameter is a policy specification (for e.g. identify
flows only from a given IP source and/or destination address)
2.1.3. Automatic Recognition
Implementations can perform automatic recognition of large flows in a
switch or a router -- it is an inline solution and would be expected
to operate at line rate.
The advantages and disadvantages of automatic recognition are:
Advantages:
. Accurate and performed in real-time.
Disadvantages:
. Not supported in many switches and routers.
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As mentioned earlier, the observation interval for determining a
large flow and the bandwidth threshold for classifying a flow as a
large flow should be programmable parameters in a switch or a router.
The implementation of automatic recognition of large flows is vendor
dependent. Below is a suggested technique.
This technique requires a few tables -- a flow table, and multiple
hash tables.
The flow table comprises entries which are programmed with packet
fields for flows that are already known to be large flows and each
entry has a corresponding byte counter. It is initialized as an
empty table (i.e. none of the incoming packets would match a flow
table entry).
The hash tables each have a different hash function and comprise
entries which are byte counters. The counters are initialized to
zero and would be modified as described by the algorithm below.
Step 1) If the large flow exists in the flow table (for e.g. TCAM),
increment the counter associated with the flow by the packet size.
Else, proceed to Step 2.
Step 2) The hash function for each table is applied to the fields of
the packet header and the result is looked up in parallel in
corresponding hash table and the associated counter corresponding to
the entry that is hit in that table is incremented by the packet
size. If the counter exceeds a programmed byte threshold in the
observation interval (this counter threshold would be set to match
the bandwidth threshold) in the entries that were hit in all of the
hash tables, a candidate large flow is learnt and programmed in the
flow table and the counters are reset.
Additionally, the counters in all of the hash tables must be reset
every observation interval.
There may be some false positives due to multiple small flows
masquerading as a large flow. The number of such false positives is
reduced by increasing the number of parallel hash tables using
different hash functions. There will be a design tradeoff between
size of the hash tables, the number of hash tables, and the
probability of a false positive.
This technique for automatic recognition is also suggested in [draft-
krishnan-opsawg-large-flow-load-balancing] -- please refer to the
draft for more details on the algorithm.
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2.1.4. Simulation
Simulation results for flow aware packet sampling are presented in
Appendix A. The goal of the simulation is to demonstrate the
effectiveness of flow aware packet sampling in a multi-tenant video
streaming data center.
3. Acknowledgements
The authors would like to thank Juergen Quittek, Brian Carpenter,
Michael Fargano, Michael Bugenhagen and Jianrong Wong for all the
support and valuable input.
4. IANA Considerations
This memo includes no request to IANA.
5. Security Considerations
This document does not directly impact the security of the Internet
infrastructure or its applications. In fact, it proposes techniques
which could help in identifying a DOS attack pattern.
6. Data Model Considerations
In Section 2, for exporting the identified large flows to an external
entity, it is recommended to use one of the protocols recommended in
evaluation of candidate protocols for IPFIX [RFC 3955]. For any
packet formats (for e.g. VXLAN, NVGRE) which are not covered by the
above RFCs, a flow export data model needs to be defined - IETF could
potentially consider a standards-based activity around this.
Section 2.1.2 defines programmable parameters in switches and routers
for automatic identification. IETF could potentially consider a
standards-based activity around defining a data model for moving this
information from a central management entity to the switch/router.
7. References
7.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
requirement Levels", BCP 14, March 1997.
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7.2. Informative References
[RFC 5474] N. Duffield et al., "A Framework for Packet Selection and
Reporting", March 2009.
[RFC 5475] T. Zseby et al., "Sampling and Filtering Techniques for IP
Packet Selection", March 2009.
[RFC 5476] B. Claise, Ed. et al., "Packet Sampling (PSAMP) Protocol
Specifications", March 2009.
[RFC 5477] T. Dietz et al., "Information Model for Packet Sampling
Exports", March 2009.
[RFC 3955] S. Leinen "Evaluation of Candidate Protocols for IP Flow
Information Export (IPFIX)", October 2004
[sFlow-v5] Phaal, P. and M. Lavine, "sFlow version 5", July 2004.
[LANCOPE] Benefits of Flow Analysis Using sFlow: Network Visibility,
Security and Integrity
http://www.lancope.com/files/Lancope_Generic_sFlow_WP.pdf
[draft-krishnan-opsawg-large-flow-load-balancing] R. Krishnan et
al., "Best Practices for Optimal LAG/ECMP Component Link Utilization
in Provider Backbone Networks", February 2013
Appendix A: Simulation of Flow aware packet sampling
Goal:
Demonstrate the effectiveness of flow aware packet sampling in a
practical use case, for e.g. multi-tenant video streaming in a data
center.
Test Topology:
Multiple virtual servers (server hosted on a virtual machine)
connected to a virtual switch (vSwitch) which in turn connects to the
data center network using a 10Gbps ethernet interface.
2 virtual servers are active.
First virtual server
. Traffic types
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o HD MPEG-4 video streams (bit rate 10Mbps) - 100 - 1Gbps
o SD MPEG-2 video streams (bit rate 4Mbps) - 300 - 1.2Gbps
o Other traffic - 500Mbps
. Aggregate traffic - 2.7Gbps
Second virtual server
. Traffic types
o HD MPEG-4 video streams (bit rate 10Mbps) - 50 - .5Gbps
o SD MPEG-2 video streams (bit rate 4Mbps) - 500 - 2.0Gbps
o Backup transaction - 100Mbps
o Other traffic - 500Mbps
. Aggregate traffic - 3.1Gbps
Total traffic on 2 servers - 5.8Gbps
Existing techniques:
Normal sampling rate - 1:1000
Total sampled traffic = 5.8Gbps/1000 = 5.8Mbps
Flow aware sampling technique:
Large flow recognition parameters
. Observation interval for large flow - 30 seconds
. Minimum bandwidth threshold over the observation interval -
2Mbps
Aggregate bit rate of large flows = 4.8Gbps
Aggregate bit rate of small flows = 1Gbps
Low sampling rate of large flows - 1:10000
Normal sampling rate of small flows - 1:1000
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Total sampled traffic = 4.8Gbps/10000 + 1Gbps/1000 = 1.48Mbps
Percentage improvement in sampling = (5.8 - 1.48)/5.8 ~= 78%
AUTHORS' ADDRESSES
Ram Krishnan
Brocade Communications
San Jose, 95134, USA
Phone: +001-408-406-7890
Email: ramk@brocade.com
David Meyer
Brocade Communications
San Jose, 95134, USA
Phone: +001-408-333-4193
Email: dmm@1-4-5.net
Ning So
Tata Communications
Plano, TX 75082, USA
Phone: +001-972-955-0914
Email: ning.so@tatacommunications.com
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