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Multilevel Simulation of Discrete Network Models

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Title: Multilevel Simulation of Discrete Network Models


1
Multilevel Simulation of Discrete Network Models
  • John A. Drew Hamilton, Jr., Ph.D.Lieutenant
    Colonel, United States ArmyDirector, Joint
    Forces Program Office

2
Research Objectives
  • Specify discrete event network simulation
    components at different levels of abstraction
    with transparent consistency.
  • Examine extracted run-time components of a
    simulation.
  • Alter simulation model components at various
    levels of abstraction.
  • Validate the developed network simulation results
    against those obtained by using classical
    simulation procedures.

3
Why Study Networks?
  • Computer networks provide the underlying support
    for the information explosion that is
    revolutionizing academia, industry and
    government.
  • This increase in capability brings a concomitant
    increase in complexity.
  • The current lack of easy to use , large scale
    network monitoring and modeling tools makes
    systematic study of networks difficult.

4
Research Prerequisites
  • The network components must be decomposable into
    reusable generic objects.
  • Varying levels of detail are possible.
  • Accurate traffic can be represented.

5
Abstraction in Simulation
  • Abstraction is the selective examination of
    certain aspects of a problem. The goal of
    abstraction is to isolate those aspects that are
    important for some purpose and suppress those
    aspects that are unimportant.Rumbaugh, et al,
    Object-Oriented Modeling and Design,
    Prentice-Hall.
  • Model abstraction is the identification of
    relationships between models described at
    different levels of detail and with deriving more
    abstract relationships from more detailed ones.
    Sevinc, Theories of Discrete Event Model
    Abstraction, Proceedings of the 1991 Winter
    Simulation Conference.

6
Relationships
Tradeoffs detail vs. abstractiondecomposition
vs. aggregation
Wall, Multilevel Abstraction of Discrete Models
in an Interactive Object-Oriented Simulation
Environment, Ph.D. Dissertation, Texas AM
University, 1993.
7
Network Complexity Factors
  • The number and variety of managed resources.
  • The distribution of devices and physical
    structure of the network, as well as subnetting.
  • The number and variety of communications services
    and distributed applications.
  • The degree to which services are integrated and
    the associated quality of service (QoS).
  • The number of organizational and administrative
    units.
  • Mission of the organization.
  • Hegering, Abeck Wies, A Corporation Operation
    Framework for Network Service Management, IEEE
    Communications, Jan. 1996.

8
Network Monitoring Tools
  • There are a variety of commercial network
    analysis tools on the market. Law and
    McComas,Simulation Software for Communications
    Networks The State of the Art, IEEE
    Communications, Vol. 32, No. 3, Mar. 1994, pp. 44
    - 50.
  • Many of these products are very expensive,
    putting them out of bounds for many researchers.
  • Fortunately, there are some free alternatives.
  • Network monitoring provides the measurements that
    ultimately validate the accuracy of a network
    simulation.
  • The monitoring process is neither easy nor
    inexpensive and is limited to the configuration
    already installed and operational.

9
Network Monitoring
  • An important step in simulating a complex system
    is to observe the system (if possible).
  • Systematic observation also yields data for
    validating the model.
  • Additionally, monitoring provides the means to
    em-ploy historical validation.
  • Sargent, Simulation model verification and
    validation, Proceedings of the 1991 Winter
    Simulation Conference, p. 40.

10
Network Monitoring
  • Drawbacks
  • This is not predictive, just lessons learned by
    the hard taskmaster of experience.
  • Experimenting on an operational network is
  • risky
  • costly
  • interfering
  • Meaningful measurement efforts must be made over
    time and will produce very large amounts of data
    to be interpreted.
  • Theoretical systems cannot be directly observed.

11
Network Simulators
  • NETSIM
  • M.I.Ts Network Simulator
  • MaRS
  • Maryland Routing Simulator (University of
    Maryland)
  • LANSF
  • Local Area Network Simulation Facility
    (University of Alberta)
  • SMURPH
  • System for Modeling Unslotted Real-Time Phenomena
    (University of Alberta)

12
Commercial Simulators
  • General-purpose simulation languages such as
    MODSIM, BONeS DESIGNER, GPSS/H, SIMSCRIPT II.5,
    SLAMSYSTEM, SES/workbench.
  • Communications-oriented simulators such as BONeS
    PlanNet, COMNET III, LNET II.5 and NETWORK II.5.
  • Communications-oriented simulation language such
    as OPNET Modeler.

13
Simulation Granularity
14
Simulation Granularity
  • The major component of resolution is the level of
    detail that the simulation can receive as input
    and return as output.
  • If the resolution of a network model is at the
    packet level, then there is no information
    provided at the bit level.
  • Bits submitted as input to the model cannot be
    processed unless the bits are collected and
    formed as packets prior to processing.
  • The resolution of the output will be at the
    packet level unless an external synthesizing
    function of some sort is used to manipulate the
    output.
  • The output will be produced based on the
    granularity of the simulation, but the output may
    be manipulated by an external function which may
    change the granularity.

15
Variable Resolution
  • Davis outlines methods of varying simulation
    resolution
  • Using high resolution to provide a picture when
    the lower-resolution depiction seems too
    abstract.
  • Invoking high resolution for special processes
    within the course of an otherwise low resolution
    simulation.
  • Using high resolution to establish bounds for
    parametric analysis using lower-resolution
    models. (e.g., the number of retries to deliver
    a packet.)
  • Using high resolution to calibrate
    lower-resolution recognizing that knowledge of
    the world comes at all levels of detail.
  • Using low resolution for decision support,
    including rapid analysis of alternative courses
    of action.
  • Davis, An Introduction to Variable-Resolution
    Modeling and Cross-Resolution Model Connection,
    RAND Report R-4252-DARPA, The RAND Corporation,
    Santa Monica, Calif., 1992.

16
Network Simulation
  • The complexity of network simulation requires
    multiple forms of abstraction control.
  • Three methods of interest are
  • Multilevel Simulation
  • Hierarchical Abstraction
  • Aggregation

17
Multilevel Simulation
18
Hierarchical Abstraction
19
Aggregation
  • Representational aggregation
  • Absolute consistency

20
Resolution Summary
  • Model resolution is a critical component in
    determining the utility of a simulation. Model
    fidelity is a closely related concept but one
    does not imply the other.
  • Simulation models are under development which
    allow the analyst to dynamically alter the
    resolution of the various components.
  • There is a fundamental need for variable
    resolution models in which there is true
    consistency across resolution levels and for
    concepts and methods making it easier to do
    cross-resolution work, including models not
    originally designed to be compatible. Davis
    Blumenthal, The Base of Sand Problem, A White
    Paper on the State of Military Combat Modeling,
    RAND Report N-3148-OSD/DARPA, 1991.
  • An understanding of abstraction, resolution
    multimodeling provides the basis for an open
    simulation architecture OSA. Hamilton Pooch,
    An Open Simulation Architecture for Force XXI,
    Proceedings of the 1995 Winter Simulation
    Conference, Washington, D.C., Dec. 3 - 6, 1995,
    pp. 1296 - 1303.

21
OPNET
  • Communications-oriented scripting language.
  • Provides access to source code.
  • Uses a multilevel modeling structure.


State Transition Diagram
22
Network Model (Upper Level)
23
Network Model (Lower Level)
24
Node Model
25
Process Model
26
Validation Structure
Does the model implementation correctly reflect
the network?
Do the predicted approximate the actual results?
A modification to Knepell and Arangnos
validation framework
Does the software perform correctly?
27
Conceptual Model Validity
  • Three subnets 133, 134, 135 were monitored and
    250,000 packets were collected from each subnet.
  • Data used as simulator input and three
    performance measures computed
  • packet throughput in packets per second
  • mean packet length
  • utilization in terms of throughput mean packet
    length divided by bandwidth

28
Observed versus Expected
Expected
Observed
29
Conceptual Validation Results
  • Used the Smith-Satterthwaite procedure
  • for comparing means with unequal variances.
  • computer test statistic for a t test.
  • Packet lengths had identical means and variances,
    no further analysis needed.
  • Observed throughput and utilization data had high
    variances, expected data had low variances.
  • Throughput and utilization data for all subnets
    passed t-tests using the Smith-Satterthwaite
    (i.e. paired) procedure.

30
Steady State Computation
  • Three stations with varying loads were selected.
  • The packet throughput for each station was
    collected during three sixty second runs each
    using a different random number seed.
  • After discarding the first ten seconds of
    transient observations, the means of the
    remaining observations were computed.
  • From Udo W. Pooch. Ph.D., if the number of
    observations in which the output is greater than
    the average is about the same as the number in
    which it was less, then steady state conditions
    are likely to exist.

31
Steady State on Guru
32
Experimentation Plan
  • High resolution script or table driven
  • Low resolution distribution driven
  • Exponentially distributed synthetic workloads
    were generated for high resolution nodes.
  • Nodes set to either low or high resolution.

33
Operational Validity
  • Objective Approach
  • Utilization data statistically tested and found
    to be valid.
  • Subjective Approach
  • Throughput data found to have similar means but
    significantly different variances.

34
Summary
  • Multilevel, mixed-resolution simulation can
    effectively expand the problem domains studied
    through simulation in the following ways
  • improved cost efficiency
  • ability to model notional network components
  • ability to improve simulation run times by
    eliminating unnecessary detail
  • Representational aggregation of nodes into
    subnets and subnets into collections of subnets
    provides abstraction mechanisms that allow the
    analyst to focus on areas of specific interest.
  • Lack of data may dictate simulating entities at
    low resolution.

35
Original Recommendations
  • Operational use of the MMRNS requires an
    automated topology builder.
  • The OPNET environment provides a powerful GUI,
    but individually modeling hundreds of individual
    nodes is tedious.
  • Gateways between the MMRNS and other open
    architecture tools should be built.
  • Mixed-resolution simulation should be used to
    model communications networks still under
    development.

36
Results over Time
  • Army DISC4 adopted OPNET as a standard due to its
    open architecture tactical version under
    development.
  • Joint Staff initiates NETWARs program.
  • Army SIGCEN has implemented a network modeling
    and simulation block of instruction in their FA
    24 course.
  • Army ISEC has been using mixed resolution
    simulation to support base ops network design in
    their Technology Integration Center.
  • ISEC in partnership with University of Arizona
    form Arizona Center for Integrative Modeling and
    Simulation.

37
Conclusion
  • The Defense Department has demonstrated multiple
    interests in the extension of this research.
  • This research demonstrates the efficacy of mixed
    resolution simulation. The flexibility provided
    by mixed resolution simulation may be safely
    utilized by following the methodology described
    herein.
  • It was specifically demonstrated that utilization
    could be accurately simulated with as many as 75
    of the stations operating in low resolution mode.
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