IPFIX R. Krishnan Internet Draft D. Meyer Intended status: Informational Brocade Communications Expires: August 2013 Ning So February 24, 2013 Tata Communications Flow Aware Packet Sampling Techniques draft-krishnan-ipfix-flow-aware-packet-sampling-03.txt Status of this Memo This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79. This document may not be modified, and derivative works of it may not be created, except to publish it as an RFC and to translate it into languages other than English. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF), its areas, and its working groups. Note that other groups may also distribute working documents as Internet- Drafts. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. 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Krishnan Expires August 24, 2013 [Page 1] Internet-Draft Flow Aware Packet Sampling Techniques February 2013 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. Acronyms..................................................3 1.2. 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 2.1.3.1. Applicability of suggested technique............6 2.1.3.2. Enhancements to suggested technique.............7 2.1.3.3. Handling Inactive Large Flows...................7 2.1.4. Simulation...........................................7 3. Acknowledgements...............................................7 4. IANA Considerations............................................7 5. Security Considerations........................................7 6. Data Model Considerations......................................8 7. References.....................................................8 7.1. Normative References......................................8 7.2. Informative References....................................8 1. Introduction Packet sampling techniques in switches and routers provide an effective mechanism for approximate detection of various types of 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 Krishnan Expires August 24, 2013 [Page 2] Internet-Draft Flow Aware Packet Sampling Techniques February 2013 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. 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 1.2. 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 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: Krishnan Expires August 24, 2013 [Page 3] Internet-Draft Flow Aware Packet Sampling Techniques February 2013 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. Netflow 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 (for e.g. SYN floods), Scanning attacks etc. [FDDOS, PDSN, ALDS] Thus, we can see that, security threat detection is possible with minimal sampling overhead. For packet sampling, it is recommended to use PSAMP -- [RFC 5474], [RFC 5475], [RFC 5476], [RFC 5477]. 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: . 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 Krishnan Expires August 24, 2013 [Page 4] Internet-Draft Flow Aware Packet Sampling Techniques February 2013 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 10 Mbps for a time period of at least 30 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. 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 uses a counting Bloom filter using thresholding and periodic reset. This technique requires a few tables -- a flow table, and multiple hash tables. Krishnan Expires August 24, 2013 [Page 5] Internet-Draft Flow Aware Packet Sampling Techniques February 2013 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. 2.1.3.1. Applicability of suggested technique The suggested technique for automatic recognition works well for standard applications generating large flows, for e.g. video content like movies and catch-up episodes, backup transactions etc. with a detection time of approximately 30-60 seconds. These detection times ensure that short-lived large flows, for e.g. HD video clips, are not unnecessarily recognized. Krishnan Expires August 24, 2013 [Page 6] Internet-Draft Flow Aware Packet Sampling Techniques February 2013 2.1.3.2. Enhancements to suggested technique If faster flow recognition times are desired (much shorter than 30s), the suggested technique may pose the following problem that the effective filtered flow size is phase-dependent: that is, relatively smaller constant-rate flows, for e.g. HD video clips, beginning early within a counting Bloom filter reset interval would be unnecessarily detected with the same probability as relatively larger flows beginning toward the interval. [VRM] suggests techniques for addressing the above problem using rotating conservative counting Bloom filters with periodic decay. 2.1.3.3. Handling Inactive Large Flows Once a flow has been recognized as a large flow, it should continue to be recognized as a large flow as long as the traffic received during an observation interval exceeds some fraction of the bandwidth threshold, for example 80% of the bandwidth threshold. If the traffic received during an observation interval falls below a fraction of the bandwidth threshold, the large flow should be removed from the flow- table. 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, Jianrong Wong and Brian Trammell 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. Krishnan Expires August 24, 2013 [Page 7] Internet-Draft Flow Aware Packet Sampling Techniques February 2013 6. Data Model Considerations In Section 2, for exporting the identified large flows to an external entity, it is recommended to use IPFIX protocol [RFC 5101]. 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 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 5101] B. Claise, "Specification of the IP Flow Information Export (IPFIX) Protocol for the Exchange of IP Traffic Flow Information", January 2008 [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 [VRM] G. Bianchi et al., "Measurement Data Reduction through Variation Rate Metering", INFOCOM 2010 [PDSN] Ignasi Paredes-Oliva et al., "Portscan Detection with Sampled NetFlow", TMA 2009 [ALDS] Z. Morley Mao et al., "Analyzing Large DDoS Attacks Using Multiple Data Sources", SIGCOMM 2006 Krishnan Expires August 24, 2013 [Page 8] Internet-Draft Flow Aware Packet Sampling Techniques February 2013 [FDDOS] David Holmes, "The DDoS Threat Spectrum", F5 White paper 2012 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 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 (Video clips, DOS attacks (for e.g. SYN floods), Scanning attacks etc.) . 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 (Video clips, DOS attacks (for e.g. SYN floods), Scanning attacks etc.) . Aggregate traffic - 3.1Gbps Total traffic on 2 servers - 5.8Gbps Krishnan Expires August 24, 2013 [Page 9] Internet-Draft Flow Aware Packet Sampling Techniques February 2013 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 - 60 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 Total sampled traffic = 4.8Gbps/10000 + 1Gbps/1000 = 1.48Mbps Percentage improvement in sampling (most of the samples are only small flows) = (5.8 - 1.48)/5.8 ~= 78% The small flows can be examined in a central entity like Netflow Collector for determining security threats like DOS attacks, Scanning attacks etc. Thus, we can see that, security threat detection is possible with minimal sampling overhead. Authors' Addresses Ram Krishnan Brocade Communications San Jose, 95134, USA Phone: +001-408-406-7890 Email: ramk@brocade.com David Meyer Brocade Communications Krishnan Expires August 24, 2013 [Page 10] Internet-Draft Flow Aware Packet Sampling Techniques February 2013 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 Krishnan Expires August 24, 2013 [Page 11]