Network Working Group Bharat M Gaonkar Internet Draft Sudhin Jacob Intended status: Experimental Juniper Networks Expires: July 2017 Giuseppe Fioccola Telecom Italia Qin Wu Huawei Technologies Praveen Ananthasankaran Nokia January 27, 2017 Packet Loss measurement Model draft-bhaprasud-ippm-pm-01.txt Abstract This document defines the loss measurement matrix models for service level packets on the network which can be implemented in different kind of network scenarios. Status of This Memo This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet- Drafts is at http://datatracker.ietf.org/drafts/current/. 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. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress." This Internet-Draft will expire on July 4, 2017. 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 Provisions Relating to IETF Documents (http://trustee.ietf.org/license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Simplified BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Simplified BSD License. Expires July 4, 2017 [Page 1] Table of Contents 1. Introduction ..................................................3 2. Terminologies..................................................4 3. Loss Measurement Models........................................5 3.1. Complete data measurement....................................5 3.2. Color based data measurement.................................6 3.3. COS based Data measurement...................................6 3.4. COS and color based Data measurement.........................6 4. Active and Passive performance measurements.....................6 5. Use Case .......................................................7 Appendix A Appendix ...............................................9 Authors' Addresses ................................................9 Expires July 4, 2017 [Page 2] 1. Introduction Today, Performance monitoring is a key technology to strengthen service offers based on enhanced QoE and SLAs. The draft aims to define performance monitoring loss measurement matrix models for service level packets on the network. The network would be provisioned with multiple services having different SLAs based on the customers' requirement.This models aims at computing Loss measurement for these services independently for each defined SLA matrixes. The class-of-service and packet color classification defined in the network drives the SLA factors and the implementation to achieve these SLAs.This draft uses the class-of-service model and color based model for any given network to define the packet loss measurement for the different SLAs. The proposed matrix models is suitable mainly for passive performance measurements but can be considered for active and hybrid performance measurements as well. This solution models loss measurement in different kinds of network scenarios. The different models explained here will help to analyse packet loss pattern, analyze the network congestion in a better way and model the network in a better way. Loss measurement is carried out between 2 end points.The underlying technology could be an active loss measurement or a Passive loss measurement. Any loss measurement will require 2 counters o Number of packets transmitted from one end point. o Number of packets received at the other end point. This draft explains the different ways to model the above data and get meaningful result for the loss measurement compulation. The underlying technology could be an MPLS Loss measurement, or based loss measurement or an IP based loss measurement. Expires July 4, 2017 [Page 3] 2. Terminologies Color Identifier: It is used to identify the color that applies to the data packet. COS Identifier: It is used to identify the COS that applies to the data packet. Complete data measurement: Complete data measurement is a data measurement method which monitors every packet and condense a large amount of information about packet arrivals into a small number of statistics. The aim of "monitoring every packet" is to ensure that the information reported is not dependent on the application. Color based data measurement: Color based data measurement is a data measurement method which monitors the data packet with the same color identifier. COS based data measurement: Color based data measurement is a data measurement method which monitors the data packet with the same COS identifier. COS identifier could be C-Tag Priority Code Point(PCP) or DSCP. COS and color based Data measurement: COS and color based Data measurement is a data measurement method which monitors the data packet with the same defined SLA matrix.The SLA matrix is an array of Color identifier attribute and COS identifier attribute. Expires July 4, 2017 [Page 4] 3. Loss Measurement Models 3.1. Complete data measurement This model uses the complete data traffic between the 2 end-points to compute loss measurement. This will result in computation of loss measurement for the entire traffic in the network in one direction. This is primarily used in cases of backbone traffic where traffic from different services are aggregated and send into the core network.This will count all the packet, this gives the overall loss measurment between one endpoint to other. 3.2. Color based data measurement This is same as the above section of "complete data measurement" with a minor difference, only monitoring the data packet with specific color identifier. In this model the packets are counted in the following Way: Count specific data traffic with different color identifier between 2 end points for loss measurement.One example of Color based data measurement is to count two type of color based traffic: o Count all committed traffic between the 2 end-point for loss measurement. o Count all Excess traffic which is beyond the committed traffic for the specific network. When both of these are combined then it becomes the model for complete traffic as mentioned in the above section. In practice the Color of traffic can be using any mechanism based on the network encapsulation.As long as the packets could be treated differently based on the underlying encapsulation this mechanism could be used. This is used in core networks where the aggregated traffic has differential priority and loss measurement can be computed on the committed traffic which is guaranteed in the network when compared with excess traffic which could be dropped based on network load and provisioning. Expires July 4, 2017 [Page 5] 3.3. COS based Data measurement This model uses the data traffic in the network which is flowing in a specific COS to measure the loss in the network.Based on the class of traffic in the network the transmitted and received packets are counted to calculate the loss measurement. Cos is differentiated from Color as COS treats the network streams with different COS identifier as different classes of traffic whereas color differentiates a set of packets with different color. Primary use of this kind of loss measurement is to measure loss measurement for a specific service which has strict SLAs. The service could be a point-to-point layer2 service, an MPLS based service. 3.4. COS and color based Data measurement This model uses a combination of both Color based data measurement and Cos based data measurement. Packets are counter for a specific COS with a specific color.This can count both in profile packet which are green and yellow which are out profile packets. This will not count the red packet which violates the SLA.This will count the packet for each SLA and color separately. 4. Active and Passive performance measurements This model reinforces the use of well known methodologies for passive performance measurements.A very simple, flexible and straightforward mechanism is presented in [I-D.ietf-ippm-alt-mark].The basic idea is to virtually split traffic flows into consecutive batches of packets:each block represents a measurable entity unambiguously recognizable thanks to the alternate marking. This approach, called Alternate Marking method, is efficient both for passive performance monitoring and for active performance monitoring. Expires July 4, 2017 [Page 6] 5. Use Case +-------+ +-------+ | | | | P2P service +---------------+ | | | | | +-------+ +-------+ Router A Router B Figure 1 Consider a provider running point to point service between router A and B for his customer "X".Customer "X" has voice traffic which requires special treatment,then he requires attention for database traffic. The customer "X" has SLA with the provider.Now the challenge faced by the provider is how to measure the traffic of customer "X" for each calss and calculate the bandwidth, moreover the provider has to see whether the "X" is sending traffic which is exceeding the level so that he can make tariff accordingly.This problem is solved by the above models which can measures the packet for each class of traffic and tabulates the data.Later point of time this data can be pulled for evaluation. Expires July 4, 2017 [Page 7] 6. Acknowledgements We would like to thank Brian Trammell for giving us the opportunity to present our draft.We would like to thank Greg Mirsky for the comments. 7. Security Considerations NA 8.IANA Considerations NA 9. References 9.1 Normative References [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/RFC2119, March 1997, . 9.2 Informative References [I-D.ietf-ippm-alt-mark] Capello, A., Cociglio, M., Fioccola, G., Castaldelli, L., and A. Bonda, "Alternate Marking method for passive performance monitoring", draft-ietf-ippm-alt-mark-00 (work in progress), July 2016. Expires July 4, 2017 [Page 8] Appendix A. Appendix Authors' Addresses Bharat M Gaonkar Juniper Networks 1133 Innovation Way Sunnyvale, California 94089 USA Email: gbharat@juniper.net Sudhin Jacob Juniper Networks 1133 Innovation Way Sunnyvale, California 94089 USA Email: sjacob@juniper.net Giuseppe Fioccola Telecom Italia Via Reiss Romoli, 274 Torino 10148 Italy Email: giuseppe.fioccola@telecomitalia.it Qin Wu Huawei Technologies Co., Ltd. 101 Software Avenue, Yuhua District Nanjing, Jiangsu 210012 China Phone: +86-25-56629042 EMail: sunseawq@huawei.com Praveen Ananthasankaran Nokia Manyata Embassy Tech Park, Silver Oak (Wing A), Outer Ring Road, Nagawara, Bangalore-560045 Email: praveen.ananthasankaran@nokia.com Expires July 4, 2017 [Page 9]