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RFC 3272

Overview and Principles of Internet Traffic Engineering

Pages: 71
Updated by:  5462
Part 2 of 3 – Pages 21 to 43
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3.0 Traffic Engineering Process Model(s)

This section describes a generic process model that captures the high level practical aspects of Internet traffic engineering in an operational context. The process model is described as a sequence of actions that a traffic engineer, or more generally a traffic engineering system, must perform to optimize the performance of an operational network (see also [RFC-2702, AWD2]). The process model described here represents the broad activities common to most traffic engineering methodologies although the details regarding how traffic engineering is executed may differ from network to network. This process model may be enacted explicitly or implicitly, by an automaton and/or by a human.
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   The traffic engineering process model is iterative [AWD2].  The four
   phases of the process model described below are repeated continually.

   The first phase of the TE process model is to define the relevant
   control policies that govern the operation of the network.  These
   policies may depend upon many factors including the prevailing
   business model, the network cost structure, the operating
   constraints, the utility model, and optimization criteria.

   The second phase of the process model is a feedback mechanism
   involving the acquisition of measurement data from the operational
   network.  If empirical data is not readily available from the
   network, then synthetic workloads may be used instead which reflect
   either the prevailing or the expected workload of the network.
   Synthetic workloads may be derived by estimation or extrapolation
   using prior empirical data.  Their derivation may also be obtained
   using mathematical models of traffic characteristics or other means.

   The third phase of the process model is to analyze the network state
   and to characterize traffic workload.  Performance analysis may be
   proactive and/or reactive.  Proactive performance analysis identifies
   potential problems that do not exist, but could manifest in the
   future.  Reactive performance analysis identifies existing problems,
   determines their cause through diagnosis, and evaluates alternative
   approaches to remedy the problem, if necessary.  A number of
   quantitative and qualitative techniques may be used in the analysis
   process, including modeling based analysis and simulation.  The
   analysis phase of the process model may involve investigating the
   concentration and distribution of traffic across the network or
   relevant subsets of the network, identifying the characteristics of
   the offered traffic workload, identifying existing or potential
   bottlenecks, and identifying network pathologies such as ineffective
   link placement, single points of failures, etc.  Network pathologies
   may result from many factors including inferior network architecture,
   inferior network design, and configuration problems.  A traffic
   matrix may be constructed as part of the analysis process.  Network
   analysis may also be descriptive or prescriptive.

   The fourth phase of the TE process model is the performance
   optimization of the network.  The performance optimization phase
   involves a decision process which selects and implements a set of
   actions from a set of alternatives.  Optimization actions may include
   the use of appropriate techniques to either control the offered
   traffic or to control the distribution of traffic across the network.
   Optimization actions may also involve adding additional links or
   increasing link capacity, deploying additional hardware such as
   routers and switches, systematically adjusting parameters associated
   with routing such as IGP metrics and BGP attributes, and adjusting
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   traffic management parameters.  Network performance optimization may
   also involve starting a network planning process to improve the
   network architecture, network design, network capacity, network
   technology, and the configuration of network elements to accommodate
   current and future growth.

3.1 Components of the Traffic Engineering Process Model

The key components of the traffic engineering process model include a measurement subsystem, a modeling and analysis subsystem, and an optimization subsystem. The following subsections examine these components as they apply to the traffic engineering process model.

3.2 Measurement

Measurement is crucial to the traffic engineering function. The operational state of a network can be conclusively determined only through measurement. Measurement is also critical to the optimization function because it provides feedback data which is used by traffic engineering control subsystems. This data is used to adaptively optimize network performance in response to events and stimuli originating within and outside the network. Measurement is also needed to determine the quality of network services and to evaluate the effectiveness of traffic engineering policies. Experience suggests that measurement is most effective when acquired and applied systematically. When developing a measurement system to support the traffic engineering function in IP networks, the following questions should be carefully considered: Why is measurement needed in this particular context? What parameters are to be measured? How should the measurement be accomplished? Where should the measurement be performed? When should the measurement be performed? How frequently should the monitored variables be measured? What level of measurement accuracy and reliability is desirable? What level of measurement accuracy and reliability is realistically attainable? To what extent can the measurement system permissibly interfere with the monitored network components and variables? What is the acceptable cost of measurement? The answers to these questions will determine the measurement tools and methodologies appropriate in any given traffic engineering context. It should also be noted that there is a distinction between measurement and evaluation. Measurement provides raw data concerning state parameters and variables of monitored network elements. Evaluation utilizes the raw data to make inferences regarding the monitored system.
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   Measurement in support of the TE function can occur at different
   levels of abstraction.  For example, measurement can be used to
   derive packet level characteristics, flow level characteristics, user
   or customer level characteristics, traffic aggregate characteristics,
   component level characteristics, and network wide characteristics.

3.3 Modeling, Analysis, and Simulation

Modeling and analysis are important aspects of Internet traffic engineering. Modeling involves constructing an abstract or physical representation which depicts relevant traffic characteristics and network attributes. A network model is an abstract representation of the network which captures relevant network features, attributes, and characteristics, such as link and nodal attributes and constraints. A network model may facilitate analysis and/or simulation which can be used to predict network performance under various conditions as well as to guide network expansion plans. In general, Internet traffic engineering models can be classified as either structural or behavioral. Structural models focus on the organization of the network and its components. Behavioral models focus on the dynamics of the network and the traffic workload. Modeling for Internet traffic engineering may also be formal or informal. Accurate behavioral models for traffic sources are particularly useful for analysis. Development of behavioral traffic source models that are consistent with empirical data obtained from operational networks is a major research topic in Internet traffic engineering. These source models should also be tractable and amenable to analysis. The topic of source models for IP traffic is a research topic and is therefore outside the scope of this document. Its importance, however, must be emphasized. Network simulation tools are extremely useful for traffic engineering. Because of the complexity of realistic quantitative analysis of network behavior, certain aspects of network performance studies can only be conducted effectively using simulation. A good network simulator can be used to mimic and visualize network characteristics under various conditions in a safe and non-disruptive manner. For example, a network simulator may be used to depict congested resources and hot spots, and to provide hints regarding possible solutions to network performance problems. A good simulator may also be used to validate the effectiveness of planned solutions to network issues without the need to tamper with the operational network, or to commence an expensive network upgrade which may not
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   achieve the desired objectives.  Furthermore, during the process of
   network planning, a network simulator may reveal pathologies such as
   single points of failure which may require additional redundancy, and
   potential bottlenecks and hot spots which may require additional

   Routing simulators are especially useful in large networks.  A
   routing simulator may identify planned links which may not actually
   be used to route traffic by the existing routing protocols.
   Simulators can also be used to conduct scenario based and
   perturbation based analysis, as well as sensitivity studies.
   Simulation results can be used to initiate appropriate actions in
   various ways.  For example, an important application of network
   simulation tools is to investigate and identify how best to make the
   network evolve and grow, in order to accommodate projected future

3.4 Optimization

Network performance optimization involves resolving network issues by transforming such issues into concepts that enable a solution, identification of a solution, and implementation of the solution. Network performance optimization can be corrective or perfective. In corrective optimization, the goal is to remedy a problem that has occurred or that is incipient. In perfective optimization, the goal is to improve network performance even when explicit problems do not exist and are not anticipated. Network performance optimization is a continual process, as noted previously. Performance optimization iterations may consist of real-time optimization sub-processes and non-real-time network planning sub-processes. The difference between real-time optimization and network planning is primarily in the relative time- scale in which they operate and in the granularity of actions. One of the objectives of a real-time optimization sub-process is to control the mapping and distribution of traffic over the existing network infrastructure to avoid and/or relieve congestion, to assure satisfactory service delivery, and to optimize resource utilization. Real-time optimization is needed because random incidents such as fiber cuts or shifts in traffic demand will occur irrespective of how well a network is designed. These incidents can cause congestion and other problems to manifest in an operational network. Real-time optimization must solve such problems in small to medium time-scales ranging from micro-seconds to minutes or hours. Examples of real- time optimization include queue management, IGP/BGP metric tuning, and using technologies such as MPLS explicit LSPs to change the paths of some traffic trunks [XIAO].
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   One of the functions of the network planning sub-process is to
   initiate actions to systematically evolve the architecture,
   technology, topology, and capacity of a network.  When a problem
   exists in the network, real-time optimization should provide an
   immediate remedy.  Because a prompt response is necessary, the real-
   time solution may not be the best possible solution.  Network
   planning may subsequently be needed to refine the solution and
   improve the situation.  Network planning is also required to expand
   the network to support traffic growth and changes in traffic
   distribution over time.  As previously noted, a change in the
   topology and/or capacity of the network may be the outcome of network

   Clearly, network planning and real-time performance optimization are
   mutually complementary activities.  A well-planned and designed
   network makes real-time optimization easier, while a systematic
   approach to real-time network performance optimization allows network
   planning to focus on long term issues rather than tactical
   considerations.  Systematic real-time network performance
   optimization also provides valuable inputs and insights toward
   network planning.

   Stability is an important consideration in real-time network
   performance optimization.  This aspect will be repeatedly addressed
   throughout this memo.

4.0 Historical Review and Recent Developments

This section briefly reviews different traffic engineering approaches proposed and implemented in telecommunications and computer networks. The discussion is not intended to be comprehensive. It is primarily intended to illuminate pre-existing perspectives and prior art concerning traffic engineering in the Internet and in legacy telecommunications networks.

4.1 Traffic Engineering in Classical Telephone Networks

This subsection presents a brief overview of traffic engineering in telephone networks which often relates to the way user traffic is steered from an originating node to the terminating node. This subsection presents a brief overview of this topic. A detailed description of the various routing strategies applied in telephone networks is included in the book by G. Ash [ASH2]. The early telephone network relied on static hierarchical routing, whereby routing patterns remained fixed independent of the state of the network or time of day. The hierarchy was intended to accommodate overflow traffic, improve network reliability via
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   alternate routes, and prevent call looping by employing strict
   hierarchical rules.  The network was typically over-provisioned since
   a given fixed route had to be dimensioned so that it could carry user
   traffic during a busy hour of any busy day.  Hierarchical routing in
   the telephony network was found to be too rigid upon the advent of
   digital switches and stored program control which were able to manage
   more complicated traffic engineering rules.

   Dynamic routing was introduced to alleviate the routing inflexibility
   in the static hierarchical routing so that the network would operate
   more efficiently.  This resulted in significant economic gains
   [HUSS87].  Dynamic routing typically reduces the overall loss
   probability by 10 to 20 percent (compared to static hierarchical
   routing).  Dynamic routing can also improve network resilience by
   recalculating routes on a per-call basis and periodically updating

   There are three main types of dynamic routing in the telephone
   network.  They are time-dependent routing, state-dependent routing
   (SDR), and event dependent routing (EDR).

   In time-dependent routing, regular variations in traffic loads (such
   as time of day or day of week) are exploited in pre-planned routing
   tables.  In state-dependent routing, routing tables are updated
   online according to the current state of the network (e.g., traffic
   demand, utilization, etc.).  In event dependent routing, routing
   changes are incepted by events (such as call setups encountering
   congested or blocked links) whereupon new paths are searched out
   using learning models.  EDR methods are real-time adaptive, but they
   do not require global state information as does SDR.  Examples of EDR
   schemes include the dynamic alternate routing (DAR) from BT, the
   state-and-time dependent routing (STR) from NTT, and the success-to-
   the-top (STT) routing from AT&T.

   Dynamic non-hierarchical routing (DNHR) is an example of dynamic
   routing that was introduced in the AT&T toll network in the 1980's to
   respond to time-dependent information such as regular load variations
   as a function of time.  Time-dependent information in terms of load
   may be divided into three time scales: hourly, weekly, and yearly.
   Correspondingly, three algorithms are defined to pre-plan the routing
   tables.  The network design algorithm operates over a year-long
   interval while the demand servicing algorithm operates on a weekly
   basis to fine tune link sizes and routing tables to correct forecast
   errors on the yearly basis.  At the smallest time scale, the routing
   algorithm is used to make limited adjustments based on daily traffic
   variations.  Network design and demand servicing are computed using
   offline calculations.  Typically, the calculations require extensive
   searches on possible routes.  On the other hand, routing may need
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   online calculations to handle crankback.  DNHR adopts a "two-link"
   approach whereby a path can consist of two links at most.  The
   routing algorithm presents an ordered list of route choices between
   an originating switch and a terminating switch.  If a call overflows,
   a via switch (a tandem exchange between the originating switch and
   the terminating switch) would send a crankback signal to the
   originating switch.  This switch would then select the next route,
   and so on, until there are no alternative routes available in which
   the call is blocked.

4.2 Evolution of Traffic Engineering in Packet Networks

This subsection reviews related prior work that was intended to improve the performance of data networks. Indeed, optimization of the performance of data networks started in the early days of the ARPANET. Other early commercial networks such as SNA also recognized the importance of performance optimization and service differentiation. In terms of traffic management, the Internet has been a best effort service environment until recently. In particular, very limited traffic management capabilities existed in IP networks to provide differentiated queue management and scheduling services to packets belonging to different classes. In terms of routing control, the Internet has employed distributed protocols for intra-domain routing. These protocols are highly scalable and resilient. However, they are based on simple algorithms for path selection which have very limited functionality to allow flexible control of the path selection process. In the following subsections, the evolution of practical traffic engineering mechanisms in IP networks and its predecessors are reviewed.

4.2.1 Adaptive Routing in the ARPANET

The early ARPANET recognized the importance of adaptive routing where routing decisions were based on the current state of the network [MCQ80]. Early minimum delay routing approaches forwarded each packet to its destination along a path for which the total estimated transit time was the smallest. Each node maintained a table of network delays, representing the estimated delay that a packet would experience along a given path toward its destination. The minimum delay table was periodically transmitted by a node to its neighbors. The shortest path, in terms of hop count, was also propagated to give the connectivity information.
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   One drawback to this approach is that dynamic link metrics tend to
   create "traffic magnets" causing congestion to be shifted from one
   location of a network to another location, resulting in oscillation
   and network instability.

4.2.2 Dynamic Routing in the Internet

The Internet evolved from the APARNET and adopted dynamic routing algorithms with distributed control to determine the paths that packets should take en-route to their destinations. The routing algorithms are adaptations of shortest path algorithms where costs are based on link metrics. The link metric can be based on static or dynamic quantities. The link metric based on static quantities may be assigned administratively according to local criteria. The link metric based on dynamic quantities may be a function of a network congestion measure such as delay or packet loss. It was apparent early that static link metric assignment was inadequate because it can easily lead to unfavorable scenarios in which some links become congested while others remain lightly loaded. One of the many reasons for the inadequacy of static link metrics is that link metric assignment was often done without considering the traffic matrix in the network. Also, the routing protocols did not take traffic attributes and capacity constraints into account when making routing decisions. This results in traffic concentration being localized in subsets of the network infrastructure and potentially causing congestion. Even if link metrics are assigned in accordance with the traffic matrix, unbalanced loads in the network can still occur due to a number factors including: - Resources may not be deployed in the most optimal locations from a routing perspective. - Forecasting errors in traffic volume and/or traffic distribution. - Dynamics in traffic matrix due to the temporal nature of traffic patterns, BGP policy change from peers, etc. The inadequacy of the legacy Internet interior gateway routing system is one of the factors motivating the interest in path oriented technology with explicit routing and constraint-based routing capability such as MPLS.
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4.2.3 ToS Routing

Type-of-Service (ToS) routing involves different routes going to the same destination with selection dependent upon the ToS field of an IP packet [RFC-2474]. The ToS classes may be classified as low delay and high throughput. Each link is associated with multiple link costs and each link cost is used to compute routes for a particular ToS. A separate shortest path tree is computed for each ToS. The shortest path algorithm must be run for each ToS resulting in very expensive computation. Classical ToS-based routing is now outdated as the IP header field has been replaced by a Diffserv field. Effective traffic engineering is difficult to perform in classical ToS-based routing because each class still relies exclusively on shortest path routing which results in localization of traffic concentration within the network.

4.2.4 Equal Cost Multi-Path

Equal Cost Multi-Path (ECMP) is another technique that attempts to address the deficiency in the Shortest Path First (SPF) interior gateway routing systems [RFC-2328]. In the classical SPF algorithm, if two or more shortest paths exist to a given destination, the algorithm will choose one of them. The algorithm is modified slightly in ECMP so that if two or more equal cost shortest paths exist between two nodes, the traffic between the nodes is distributed among the multiple equal-cost paths. Traffic distribution across the equal-cost paths is usually performed in one of two ways: (1) packet-based in a round-robin fashion, or (2) flow-based using hashing on source and destination IP addresses and possibly other fields of the IP header. The first approach can easily cause out- of-order packets while the second approach is dependent upon the number and distribution of flows. Flow-based load sharing may be unpredictable in an enterprise network where the number of flows is relatively small and less heterogeneous (for example, hashing may not be uniform), but it is generally effective in core public networks where the number of flows is large and heterogeneous. In ECMP, link costs are static and bandwidth constraints are not considered, so ECMP attempts to distribute the traffic as equally as possible among the equal-cost paths independent of the congestion status of each path. As a result, given two equal-cost paths, it is possible that one of the paths will be more congested than the other. Another drawback of ECMP is that load sharing cannot be achieved on multiple paths which have non-identical costs.
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4.2.5 Nimrod

Nimrod is a routing system developed to provide heterogeneous service specific routing in the Internet, while taking multiple constraints into account [RFC-1992]. Essentially, Nimrod is a link state routing protocol which supports path oriented packet forwarding. It uses the concept of maps to represent network connectivity and services at multiple levels of abstraction. Mechanisms are provided to allow restriction of the distribution of routing information. Even though Nimrod did not enjoy deployment in the public Internet, a number of key concepts incorporated into the Nimrod architecture, such as explicit routing which allows selection of paths at originating nodes, are beginning to find applications in some recent constraint-based routing initiatives.

4.3 Overlay Model

In the overlay model, a virtual-circuit network, such as ATM, frame relay, or WDM, provides virtual-circuit connectivity between routers that are located at the edges of a virtual-circuit cloud. In this mode, two routers that are connected through a virtual circuit see a direct adjacency between themselves independent of the physical route taken by the virtual circuit through the ATM, frame relay, or WDM network. Thus, the overlay model essentially decouples the logical topology that routers see from the physical topology that the ATM, frame relay, or WDM network manages. The overlay model based on ATM or frame relay enables a network administrator or an automaton to employ traffic engineering concepts to perform path optimization by re-configuring or rearranging the virtual circuits so that a virtual circuit on a congested or sub-optimal physical link can be re-routed to a less congested or more optimal one. In the overlay model, traffic engineering is also employed to establish relationships between the traffic management parameters (e.g., PCR, SCR, and MBS for ATM) of the virtual-circuit technology and the actual traffic that traverses each circuit. These relationships can be established based upon known or projected traffic profiles, and some other factors. The overlay model using IP over ATM requires the management of two separate networks with different technologies (IP and ATM) resulting in increased operational complexity and cost. In the fully-meshed overlay model, each router would peer to every other router in the network, so that the total number of adjacencies is a quadratic function of the number of routers. Some of the issues with the overlay model are discussed in [AWD2].
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4.4 Constrained-Based Routing

Constraint-based routing refers to a class of routing systems that compute routes through a network subject to the satisfaction of a set of constraints and requirements. In the most general setting, constraint-based routing may also seek to optimize overall network performance while minimizing costs. The constraints and requirements may be imposed by the network itself or by administrative policies. Constraints may include bandwidth, hop count, delay, and policy instruments such as resource class attributes. Constraints may also include domain specific attributes of certain network technologies and contexts which impose restrictions on the solution space of the routing function. Path oriented technologies such as MPLS have made constraint-based routing feasible and attractive in public IP networks. The concept of constraint-based routing within the context of MPLS traffic engineering requirements in IP networks was first defined in [RFC-2702]. Unlike QoS routing (for example, see [RFC-2386] and [MA]) which generally addresses the issue of routing individual traffic flows to satisfy prescribed flow based QoS requirements subject to network resource availability, constraint-based routing is applicable to traffic aggregates as well as flows and may be subject to a wide variety of constraints which may include policy restrictions.

4.5 Overview of Other IETF Projects Related to Traffic Engineering

This subsection reviews a number of IETF activities pertinent to Internet traffic engineering. These activities are primarily intended to evolve the IP architecture to support new service definitions which allow preferential or differentiated treatment to be accorded to certain types of traffic.

4.5.1 Integrated Services

The IETF Integrated Services working group developed the integrated services (Intserv) model. This model requires resources, such as bandwidth and buffers, to be reserved a priori for a given traffic flow to ensure that the quality of service requested by the traffic flow is satisfied. The integrated services model includes additional components beyond those used in the best-effort model such as packet classifiers, packet schedulers, and admission control. A packet classifier is used to identify flows that are to receive a certain level of service. A packet scheduler handles the scheduling of
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   service to different packet flows to ensure that QoS commitments are
   met.  Admission control is used to determine whether a router has the
   necessary resources to accept a new flow.

   Two services have been defined under the Integrated Services model:
   guaranteed service [RFC-2212] and controlled-load service [RFC-2211].

   The guaranteed service can be used for applications requiring bounded
   packet delivery time.  For this type of application, data that is
   delivered to the application after a pre-defined amount of time has
   elapsed is usually considered worthless.  Therefore, guaranteed
   service was intended to provide a firm quantitative bound on the
   end-to-end packet delay for a flow.  This is accomplished by
   controlling the queuing delay on network elements along the data flow
   path.  The guaranteed service model does not, however, provide
   bounds on jitter (inter-arrival times between consecutive packets).

   The controlled-load service can be used for adaptive applications
   that can tolerate some delay but are sensitive to traffic overload
   conditions.  This type of application typically functions
   satisfactorily when the network is lightly loaded but its performance
   degrades significantly when the network is heavily loaded.
   Controlled-load service, therefore, has been designed to provide
   approximately the same service as best-effort service in a lightly
   loaded network regardless of actual network conditions.  Controlled-
   load service is described qualitatively in that no target values of
   delay or loss are specified.

   The main issue with the Integrated Services model has been
   scalability [RFC-2998], especially in large public IP networks which
   may potentially have millions of active micro-flows in transit

   A notable feature of the Integrated Services model is that it
   requires explicit signaling of QoS requirements from end systems to
   routers [RFC-2753].  The Resource Reservation Protocol (RSVP)
   performs this signaling function and is a critical component of the
   Integrated Services model.  The RSVP protocol is described next.

4.5.2 RSVP

RSVP is a soft state signaling protocol [RFC-2205]. It supports receiver initiated establishment of resource reservations for both multicast and unicast flows. RSVP was originally developed as a signaling protocol within the integrated services framework for applications to communicate QoS requirements to the network and for the network to reserve relevant resources to satisfy the QoS requirements [RFC-2205].
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   Under RSVP, the sender or source node sends a PATH message to the
   receiver with the same source and destination addresses as the
   traffic which the sender will generate.  The PATH message contains:
   (1) a sender Tspec specifying the characteristics of the traffic, (2)
   a sender Template specifying the format of the traffic, and (3) an
   optional Adspec which is used to support the concept of one pass with
   advertising" (OPWA) [RFC-2205].  Every intermediate router along the
   path forwards the PATH Message to the next hop determined by the
   routing protocol.  Upon receiving a PATH Message, the receiver
   responds with a RESV message which includes a flow descriptor used to
   request resource reservations.  The RESV message travels to the
   sender or source node in the opposite direction along the path that
   the PATH message traversed.  Every intermediate router along the path
   can reject or accept the reservation request of the RESV message.  If
   the request is rejected, the rejecting router will send an error
   message to the receiver and the signaling process will terminate.  If
   the request is accepted, link bandwidth and buffer space are
   allocated for the flow and the related flow state information is
   installed in the router.

   One of the issues with the original RSVP specification was
   Scalability.  This is because reservations were required for micro-
   flows, so that the amount of state maintained by network elements
   tends to increase linearly with the number of micro-flows.  These
   issues are described in [RFC-2961].

   Recently, RSVP has been modified and extended in several ways to
   mitigate the scaling problems.  As a result, it is becoming a
   versatile signaling protocol for the Internet.  For example, RSVP has
   been extended to reserve resources for aggregation of flows, to set
   up MPLS explicit label switched paths, and to perform other signaling
   functions within the Internet.  There are also a number of proposals
   to reduce the amount of refresh messages required to maintain
   established RSVP sessions [RFC-2961].

   A number of IETF working groups have been engaged in activities
   related to the RSVP protocol.  These include the original RSVP
   working group, the MPLS working group, the Resource Allocation
   Protocol working group, and the Policy Framework working group.

4.5.3 Differentiated Services

The goal of the Differentiated Services (Diffserv) effort within the IETF is to devise scalable mechanisms for categorization of traffic into behavior aggregates, which ultimately allows each behavior aggregate to be treated differently, especially when there is a shortage of resources such as link bandwidth and buffer space [RFC- 2475]. One of the primary motivations for the Diffserv effort was to
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   devise alternative mechanisms for service differentiation in the
   Internet that mitigate the scalability issues encountered with the
   Intserv model.

   The IETF Diffserv working group has defined a Differentiated Services
   field in the IP header (DS field).  The DS field consists of six bits
   of the part of the IP header formerly known as TOS octet.  The DS
   field is used to indicate the forwarding treatment that a packet
   should receive at a node [RFC-2474].  The Diffserv working group has
   also standardized a number of Per-Hop Behavior (PHB) groups.  Using
   the PHBs, several classes of services can be defined using different
   classification, policing, shaping, and scheduling rules.

   For an end-user of network services to receive Differentiated
   Services from its Internet Service Provider (ISP), it may be
   necessary for the user to have a Service Level Agreement (SLA) with
   the ISP.  An SLA may explicitly or implicitly specify a Traffic
   Conditioning Agreement (TCA) which defines classifier rules as well
   as metering, marking, discarding, and shaping rules.

   Packets are classified, and possibly policed and shaped at the
   ingress to a Diffserv network.  When a packet traverses the boundary
   between different Diffserv domains, the DS field of the packet may be
   re-marked according to existing agreements between the domains.

   Differentiated Services allows only a finite number of service
   classes to be indicated by the DS field.  The main advantage of the
   Diffserv approach relative to the Intserv model is scalability.
   Resources are allocated on a per-class basis and the amount of state
   information is proportional to the number of classes rather than to
   the number of application flows.

   It should be obvious from the previous discussion that the Diffserv
   model essentially deals with traffic management issues on a per hop
   basis.  The Diffserv control model consists of a collection of
   micro-TE control mechanisms.  Other traffic engineering capabilities,
   such as capacity management (including routing control), are also
   required in order to deliver acceptable service quality in Diffserv
   networks.  The concept of Per Domain Behaviors has been introduced to
   better capture the notion of differentiated services across a
   complete domain [RFC-3086].

4.5.4 MPLS

MPLS is an advanced forwarding scheme which also includes extensions to conventional IP control plane protocols. MPLS extends the Internet routing model and enhances packet forwarding and path control [RFC-3031].
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   At the ingress to an MPLS domain, label switching routers (LSRs)
   classify IP packets into forwarding equivalence classes (FECs) based
   on a variety of factors, including, e.g., a combination of the
   information carried in the IP header of the packets and the local
   routing information maintained by the LSRs.  An MPLS label is then
   prepended to each packet according to their forwarding equivalence
   classes.  In a non-ATM/FR environment, the label is 32 bits long and
   contains a 20-bit label field, a 3-bit experimental field (formerly
   known as Class-of-Service or CoS field), a 1-bit label stack
   indicator and an 8-bit TTL field.  In an ATM (FR) environment, the
   label consists of information encoded in the VCI/VPI (DLCI) field.
   An MPLS capable router (an LSR) examines the label and possibly the
   experimental field and uses this information to make packet
   forwarding decisions.

   An LSR makes forwarding decisions by using the label prepended to
   packets as the index into a local next hop label forwarding entry
   (NHLFE).  The packet is then processed as specified in the NHLFE.
   The incoming label may be replaced by an outgoing label, and the
   packet may be switched to the next LSR.  This label-switching process
   is very similar to the label (VCI/VPI) swapping process in ATM
   networks.  Before a packet leaves an MPLS domain, its MPLS label may
   be removed.  A Label Switched Path (LSP) is the path between an
   ingress LSRs and an egress LSRs through which a labeled packet
   traverses.  The path of an explicit LSP is defined at the originating
   (ingress) node of the LSP.  MPLS can use a signaling protocol such as
   RSVP or LDP to set up LSPs.

   MPLS is a very powerful technology for Internet traffic engineering
   because it supports explicit LSPs which allow constraint-based
   routing to be implemented efficiently in IP networks [AWD2].  The
   requirements for traffic engineering over MPLS are described in
   [RFC-2702].  Extensions to RSVP to support instantiation of explicit
   LSP are discussed in [RFC-3209].  Extensions to LDP, known as CR-LDP,
   to support explicit LSPs are presented in [JAM].

4.5.5 IP Performance Metrics

The IETF IP Performance Metrics (IPPM) working group has been developing a set of standard metrics that can be used to monitor the quality, performance, and reliability of Internet services. These metrics can be applied by network operators, end-users, and independent testing groups to provide users and service providers with a common understanding of the performance and reliability of the Internet component 'clouds' they use/provide [RFC-2330]. The criteria for performance metrics developed by the IPPM WG are described in [RFC-2330]. Examples of performance metrics include one-way packet
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   loss [RFC-2680], one-way delay [RFC-2679], and connectivity measures
   between two nodes [RFC-2678].  Other metrics include second-order
   measures of packet loss and delay.

   Some of the performance metrics specified by the IPPM WG are useful
   for specifying Service Level Agreements (SLAs).  SLAs are sets of
   service level objectives negotiated between users and service
   providers, wherein each objective is a combination of one or more
   performance metrics, possibly subject to certain constraints.

4.5.6 Flow Measurement

The IETF Real Time Flow Measurement (RTFM) working group has produced an architecture document defining a method to specify traffic flows as well as a number of components for flow measurement (meters, meter readers, manager) [RFC-2722]. A flow measurement system enables network traffic flows to be measured and analyzed at the flow level for a variety of purposes. As noted in RFC 2722, a flow measurement system can be very useful in the following contexts: (1) understanding the behavior of existing networks, (2) planning for network development and expansion, (3) quantification of network performance, (4) verifying the quality of network service, and (5) attribution of network usage to users. A flow measurement system consists of meters, meter readers, and managers. A meter observes packets passing through a measurement point, classifies them into certain groups, accumulates certain usage data (such as the number of packets and bytes for each group), and stores the usage data in a flow table. A group may represent a user application, a host, a network, a group of networks, etc. A meter reader gathers usage data from various meters so it can be made available for analysis. A manager is responsible for configuring and controlling meters and meter readers. The instructions received by a meter from a manager include flow specification, meter control parameters, and sampling techniques. The instructions received by a meter reader from a manager include the address of the meter whose date is to be collected, the frequency of data collection, and the types of flows to be collected.

4.5.7 Endpoint Congestion Management

[RFC-3124] is intended to provide a set of congestion control mechanisms that transport protocols can use. It is also intended to develop mechanisms for unifying congestion control across a subset of an endpoint's active unicast connections (called a congestion group). A congestion manager continuously monitors the state of the path for
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   each congestion group under its control.  The manager uses that
   information to instruct a scheduler on how to partition bandwidth
   among the connections of that congestion group.

4.6 Overview of ITU Activities Related to Traffic Engineering

This section provides an overview of prior work within the ITU-T pertaining to traffic engineering in traditional telecommunications networks. ITU-T Recommendations E.600 [ITU-E600], E.701 [ITU-E701], and E.801 [ITU-E801] address traffic engineering issues in traditional telecommunications networks. Recommendation E.600 provides a vocabulary for describing traffic engineering concepts, while E.701 defines reference connections, Grade of Service (GOS), and traffic parameters for ISDN. Recommendation E.701 uses the concept of a reference connection to identify representative cases of different types of connections without describing the specifics of their actual realizations by different physical means. As defined in Recommendation E.600, "a connection is an association of resources providing means for communication between two or more devices in, or attached to, a telecommunication network." Also, E.600 defines "a resource as any set of physically or conceptually identifiable entities within a telecommunication network, the use of which can be unambiguously determined" [ITU-E600]. There can be different types of connections as the number and types of resources in a connection may vary. Typically, different network segments are involved in the path of a connection. For example, a connection may be local, national, or international. The purposes of reference connections are to clarify and specify traffic performance issues at various interfaces between different network domains. Each domain may consist of one or more service provider networks. Reference connections provide a basis to define grade of service (GoS) parameters related to traffic engineering within the ITU-T framework. As defined in E.600, "GoS refers to a number of traffic engineering variables which are used to provide a measure of the adequacy of a group of resources under specified conditions." These GoS variables may be probability of loss, dial tone, delay, etc. They are essential for network internal design and operation as well as for component performance specification. GoS is different from quality of service (QoS) in the ITU framework. QoS is the performance perceivable by a telecommunication service user and expresses the user's degree of satisfaction of the service. QoS parameters focus on performance aspects observable at the service
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   access points and network interfaces, rather than their causes within
   the network.  GoS, on the other hand, is a set of network oriented
   measures which characterize the adequacy of a group of resources
   under specified conditions.  For a network to be effective in serving
   its users, the values of both GoS and QoS parameters must be related,
   with GoS parameters typically making a major contribution to the QoS.

   Recommendation E.600 stipulates that a set of GoS parameters must be
   selected and defined on an end-to-end basis for each major service
   category provided by a network to assist the network provider with
   improving efficiency and effectiveness of the network.  Based on a
   selected set of reference connections, suitable target values are
   assigned to the selected GoS parameters under normal and high load
   conditions.  These end-to-end GoS target values are then apportioned
   to individual resource components of the reference connections for
   dimensioning purposes.

4.7 Content Distribution

The Internet is dominated by client-server interactions, especially Web traffic (in the future, more sophisticated media servers may become dominant). The location and performance of major information servers has a significant impact on the traffic patterns within the Internet as well as on the perception of service quality by end users. A number of dynamic load balancing techniques have been devised to improve the performance of replicated information servers. These techniques can cause spatial traffic characteristics to become more dynamic in the Internet because information servers can be dynamically picked based upon the location of the clients, the location of the servers, the relative utilization of the servers, the relative performance of different networks, and the relative performance of different parts of a network. This process of assignment of distributed servers to clients is called Traffic Directing. It functions at the application layer. Traffic Directing schemes that allocate servers in multiple geographically dispersed locations to clients may require empirical network performance statistics to make more effective decisions. In the future, network measurement systems may need to provide this type of information. The exact parameters needed are not yet defined. When congestion exists in the network, Traffic Directing and Traffic Engineering systems should act in a coordinated manner. This topic is for further study.
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   The issues related to location and replication of information
   servers, particularly web servers, are important for Internet traffic
   engineering because these servers contribute a substantial proportion
   of Internet traffic.

5.0 Taxonomy of Traffic Engineering Systems

This section presents a short taxonomy of traffic engineering systems. A taxonomy of traffic engineering systems can be constructed based on traffic engineering styles and views as listed below: - Time-dependent vs State-dependent vs Event-dependent - Offline vs Online - Centralized vs Distributed - Local vs Global Information - Prescriptive vs Descriptive - Open Loop vs Closed Loop - Tactical vs Strategic These classification systems are described in greater detail in the following subsections of this document.

5.1 Time-Dependent Versus State-Dependent Versus Event Dependent

Traffic engineering methodologies can be classified as time- dependent, or state-dependent, or event-dependent. All TE schemes are considered to be dynamic in this document. Static TE implies that no traffic engineering methodology or algorithm is being applied. In the time-dependent TE, historical information based on periodic variations in traffic, (such as time of day), is used to pre-program routing plans and other TE control mechanisms. Additionally, customer subscription or traffic projection may be used. Pre- programmed routing plans typically change on a relatively long time scale (e.g., diurnal). Time-dependent algorithms do not attempt to adapt to random variations in traffic or changing network conditions. An example of a time-dependent algorithm is a global centralized optimizer where the input to the system is a traffic matrix and multi-class QoS requirements as described [MR99]. State-dependent TE adapts the routing plans for packets based on the current state of the network. The current state of the network provides additional information on variations in actual traffic (i.e., perturbations from regular variations) that could not be predicted using historical information. Constraint-based routing is
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   an example of state-dependent TE operating in a relatively long time
   scale.  An example operating in a relatively short time scale is a
   load-balancing algorithm described in [MATE].

   The state of the network can be based on parameters such as
   utilization, packet delay, packet loss, etc.  These parameters can be
   obtained in several ways.  For example, each router may flood these
   parameters periodically or by means of some kind of trigger to other
   routers.  Another approach is for a particular router performing
   adaptive TE to send probe packets along a path to gather the state of
   that path.  Still another approach is for a management system to
   gather relevant information from network elements.

   Expeditious and accurate gathering and distribution of state
   information is critical for adaptive TE due to the dynamic nature of
   network conditions.  State-dependent algorithms may be applied to
   increase network efficiency and resilience.  Time-dependent
   algorithms are more suitable for predictable traffic variations.  On
   the other hand, state-dependent algorithms are more suitable for
   adapting to the prevailing network state.

   Event-dependent TE methods can also be used for TE path selection.
   Event-dependent TE methods are distinct from time-dependent and
   state-dependent TE methods in the manner in which paths are selected.
   These algorithms are adaptive and distributed in nature and typically
   use learning models to find good paths for TE in a network.  While
   state-dependent TE models typically use available-link-bandwidth
   (ALB) flooding for TE path selection, event-dependent TE methods do
   not require ALB flooding.  Rather, event-dependent TE methods
   typically search out capacity by learning models, as in the success-
   to-the-top (STT) method.  ALB flooding can be resource intensive,
   since it requires link bandwidth to carry LSAs, processor capacity to
   process LSAs, and the overhead can limit area/autonomous system (AS)
   size.  Modeling results suggest that event-dependent TE methods could
   lead to a reduction in ALB flooding overhead without loss of network
   throughput performance [ASH3].

5.2 Offline Versus Online

Traffic engineering requires the computation of routing plans. The computation may be performed offline or online. The computation can be done offline for scenarios where routing plans need not be executed in real-time. For example, routing plans computed from forecast information may be computed offline. Typically, offline computation is also used to perform extensive searches on multi- dimensional solution spaces.
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   Online computation is required when the routing plans must adapt to
   changing network conditions as in state-dependent algorithms.  Unlike
   offline computation (which can be computationally demanding), online
   computation is geared toward relative simple and fast calculations to
   select routes, fine-tune the allocations of resources, and perform
   load balancing.

5.3 Centralized Versus Distributed

Centralized control has a central authority which determines routing plans and perhaps other TE control parameters on behalf of each router. The central authority collects the network-state information from all routers periodically and returns the routing information to the routers. The routing update cycle is a critical parameter directly impacting the performance of the network being controlled. Centralized control may need high processing power and high bandwidth control channels. Distributed control determines route selection by each router autonomously based on the routers view of the state of the network. The network state information may be obtained by the router using a probing method or distributed by other routers on a periodic basis using link state advertisements. Network state information may also be disseminated under exceptional conditions.

5.4 Local Versus Global

Traffic engineering algorithms may require local or global network- state information. Local information pertains to the state of a portion of the domain. Examples include the bandwidth and packet loss rate of a particular path. Local state information may be sufficient for certain instances of distributed-controlled TEs. Global information pertains to the state of the entire domain undergoing traffic engineering. Examples include a global traffic matrix and loading information on each link throughout the domain of interest. Global state information is typically required with centralized control. Distributed TE systems may also need global information in some cases.

5.5 Prescriptive Versus Descriptive

TE systems may also be classified as prescriptive or descriptive.
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   Prescriptive traffic engineering evaluates alternatives and
   recommends a course of action.  Prescriptive traffic engineering can
   be further categorized as either corrective or perfective.
   Corrective TE prescribes a course of action to address an existing or
   predicted anomaly.  Perfective TE prescribes a course of action to
   evolve and improve network performance even when no anomalies are

   Descriptive traffic engineering, on the other hand, characterizes the
   state of the network and assesses the impact of various policies
   without recommending any particular course of action.

5.6 Open-Loop Versus Closed-Loop

Open-loop traffic engineering control is where control action does not use feedback information from the current network state. The control action may use its own local information for accounting purposes, however. Closed-loop traffic engineering control is where control action utilizes feedback information from the network state. The feedback information may be in the form of historical information or current measurement.

5.7 Tactical vs Strategic

Tactical traffic engineering aims to address specific performance problems (such as hot-spots) that occur in the network from a tactical perspective, without consideration of overall strategic imperatives. Without proper planning and insights, tactical TE tends to be ad hoc in nature. Strategic traffic engineering approaches the TE problem from a more organized and systematic perspective, taking into consideration the immediate and longer term consequences of specific policies and actions.

(page 43 continued on part 3)

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