Home > Articles > Cisco Network Technology > General Networking > Traffic Engineering with MPLS

Traffic Engineering with MPLS

  • Sample Chapter is provided courtesy of Cisco Press.
  • Date: Aug 16, 2002.

Chapter Description

This sample chapter offers a brief look at the DiffServ architecture and its interaction with IP and MPLS packets, the MQC, and DS-TE.


Quality of service (QoS) and MPLS are, at a political level, similar. They're both technologies that have been gaining popularity in recent years. They both seem to be technologies that you either love or hate—some people are huge QoS fans, and others can't stand it. The same is true of MPLS—some people like it, and others don't.

At a technical level, though, QoS and MPLS are very different.

QoS is an umbrella term that covers network performance characteristics. As discussed in Chapter 1, "Understanding Traffic Engineering with MPLS," QoS has two parts:

  • Finding a path through your network that can provide the service you offer

  • Enforcing that service

The acronym QoS in respect to IP first showed up in RFC 1006, "ISO Transport Service on Top of the TCP: Version 3," published in 1987. The term QoS has been around for even longer, because it is a general term used to describe performance characteristics in networks. In the IP and MPLS worlds, the term QoS is most often used to describe a set of techniques to manage packet loss, latency, and jitter. QoS has been rather appropriately described as "managed unfairness": If you have contention for system resources, who are you unfair to, and why?

Two QoS architectures are in use today:

  • Integrated Services (IntServ)

  • Differentiated Services (DiffServ)

For various reasons, IntServ never scaled to the level it needed to get to for Internet-size networks. IntServ is fine for small- to medium-sized networks, but its need to make end-to-end, host-to-host, per-application microflows across a network means it can't grow to the level that large service provider networks need.

DiffServ, on the other hand, has proven quite scalable. Its use of classification on the edge and per-hop queuing and discard behaviors in the core means that most of the work is done at the edge, and you don't need to keep any microflow state in the core.

This chapter assumes that you understand QoS on an IP network. It concentrates on the integration of MPLS into the IP QoS spectrum of services. This means that you should be comfortable with acronyms such as CAR, LLQ, MDRR, MQC, SLA, and WRED in order to get the most out of this chapter. This chapter briefly reviews both the DiffServ architecture and the Modular QoS CLI (MQC), but see Appendix B, "CCO and Other References," if you want to learn more about the portfolio of Cisco QoS tools.

QoS, as used in casual conversation and in the context of IP and MPLS networks, is a method of packet treatment: How do you decide which packets get what service?

MPLS, on the other hand, is a switching method used to get packets from one place to another by going through a series of hops. Which hops a packet goes through can be determined by your IGP routing or by MPLS TE.

So there you have it—MPLS is about getting packets from one hop to another, and QoS (as the term is commonly used) is what happens to packets at each hop. As you can imagine, between two complex devices such as QoS and MPLS, a lot can be done.

This chapter covers five topics:

  • The DiffServ architecture

  • DiffServ's interaction with IP Precedence and MPLS EXP bits

  • The treatment of EXP values in a label stack as packets are forwarded throughout a network

  • A quick review of the Modular QoS CLI (MQC), which is how most QoS features on most platforms are configured

  • Where DiffServ and MPLS TE intersect—the emerging DiffServ-Aware Traffic Engineering (DS-TE) devices and how they can be used to further optimize your network performance

DiffServ and MPLS TE

It is important to understand that the DiffServ architecture and the sections of this chapter that cover DiffServ and MPLS have nothing to do with MPLS TE. DiffServ is purely a method of treating packets differently at each hop. The DiffServ architecture doesn't care what control plane protocol a given label assignment comes from. Whether it's RSVP or LDP, or BGP, or something else entirely, the forwarding plane doesn't care. Why does this chapter exist then, if it's not about TE? Partly because MPLS TE and DiffServ treatment of MPLS packets go hand in hand in many network designs, and partly because of the existence of something called DS-TE, discussed later in this chapter.

The DiffServ Architecture

RFC 2475 defines an architecture for Differentiated Services—how to use DiffServ Code Point (DSCP) bits and various QoS mechanisms to provide different qualities of service in your network.

DiffServ has two major components:

  • Traffic conditioning—Includes things such as policing, coloring, and shaping. Is done only at the edge of the network.

  • Per-hop behaviors—Essentially consist of queuing, scheduling, and dropping mechanisms. As the name implies, they are done at every hop in the network.

Cisco IOS Software provides all sorts of different tools to apply these architecture pieces. You can configure most services in two ways—a host of older, disconnected, per-platform methods, and a newer, unified configuration set called the MQC. Only MQC is covered in this chapter. For information on the older configuration mechanisms, see Appendix B or the documentation on CCO. Not all platforms support MQC, so there might be times when you need to configure a service using a non-MQC configuration method; however, MQC is where all QoS configuration services are heading, so it's definitely worth understanding.

Traffic conditioning generally involves classification, policing, and marking, and per-hop behaviors deal with queuing, scheduling, and dropping. Each of these topics are discussed briefly.


The first step in applying the DiffServ architecture is to have the capability to classify packets. Classification is the act of examining a packet to decide what sort of rules it should be run through, and subsequently what DSCP or EXP value should be set on the packet.

Classifying IP Packets

Classifying IP packets is straightforward. You can match on just about anything in the IP header. Specific match capabilities vary by platform, but generally, destination IP address, source IP address, and DSCP values can be matched against. The idea behind DSCP is discussed in the section "DiffServ and IP Packets."

Classifying MPLS Packets

The big thing to keep in mind when classifying MPLS packets is that you can't match on anything other than the outermost EXP value in the label stack. There's no way to look past the MPLS header at the underlying IP packet and do any matching on or modification of that packet. You can't match on the label value in the top of the stack, and you can't match on TTL (just as you can't match on IP TTL). Finally, you can't do any matching of EXP values on any label other than the topmost label on the stack.


Policing involves metering traffic against a specified service contract and dealing with in-rate and out-of-rate traffic differently. One of the fundamental pieces of the DiffServ architecture is that you don't allow more traffic on your network than you have designed for, to make sure that you don't overtax the queues you've provisioned. This is generally done with policing, although it can also be done with shaping.

Policing is done on the edge of the network. As such, the packets coming into the network are very often IP packets. However, under some scenarios it is possible to receive MPLS-labeled packets on the edge of the network. For example, the Carrier Supporting Carrier architecture (see Appendix B) means that a provider receives MPLS-labeled packets from a customer.


The marking configuration is usually very tightly tied to the policing configuration. You can mark traffic as in-rate and out-of-rate as a result of policing traffic.

You don't need to police in order to mark. For example, you can simply define a mapping between the IP packet's DSCP value and the MPLS EXP bits to be used when a label is imposed on these packets. Another possibility is to simply mark all traffic coming in on an interface, regardless of traffic rate. This is handy if you have some customers who are paying extra for better QoS and some who are not. For those who are not, simply set the EXP to 0 on all packets from that customer.

Being able to set the EXP on a packet, rather than having to set the IP Precedence, is one of the advantages of MPLS. This is discussed in more detail in the sections "Label Stack Treatment" and "Tunnel Modes."


Queuing is accomplished in different ways on different platforms. However, the good news is that you can treat MPLS EXP just like IP Precedence.

Multiple queuing techniques can be applied to MPLS, depending on your platform and code version:

  • First In First Out (FIFO)

  • Modified Deficit Round Robin (MDRR) (GSR platforms only)

  • Class-Based Weighted Fair Queuing (CBWFQ) (most non-GSR platforms)

  • Low-Latency Queuing (LLQ)

FIFO exists on every platform and every interface. It is the default on almost all of those interfaces.

MDRR, CBWFQ, and LLQ are configured using the MQC, just like most other QoS mechanisms on most platforms. Just match the desired MPLS EXP values in a class map and then configure a bandwidth or latency guarantee via the bandwidth or priority commands. The underlying scheduling algorithm (MDRR, CBWFQ/LLQ) brings the guarantee to life.

Queuing is one of two parts of what the DiffServ architecture calls per-hop behaviors (PHBs). A per-hop behavior is, surprisingly, a behavior that is implemented at each hop. PHBs have two fundamental pieces—queuing and dropping.


Dropping is the other half of DiffServ's PHB. Dropping is important not only to manage queue depth per traffic class, but also to signal transport-level backoff to TCP-based applications. TCP responds to occasional packet drops by slowing down the rate at which it sends. TCP responds better to occasional drops than to tail drop after a queue is completely filled up. See Appendix B for more information.

Weighted Random Early Detection (WRED) is the DiffServ drop mechanism implemented on most Cisco platforms. WRED works on MPLS EXP just like it does on IP Precedence. See the next section for WRED configuration details.

As you can see, implementing DiffServ behavior with MPLS packets is no more and no less than implementing the same behavior with IP.

2. A Quick MQC Review | Next Section

Cisco Press Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from Cisco Press and its family of brands. I can unsubscribe at any time.


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about Cisco Press products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information

To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.


Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites; develop new products and services; conduct educational research; and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.


If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@ciscopress.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information

Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.


This site is not directed to children under the age of 13.


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information

If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.


Users can always make an informed choice as to whether they should proceed with certain services offered by Cisco Press. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.ciscopress.com/u.aspx.

Sale of Personal Information

Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents

California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure

Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact

Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice

We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020