Session 1813 Traffic Behavior in addition to Queuing in a QoS Environment
McCarthy, Todd, Chief Film Critic has reference to this Academic Journal, PHwiki organized this Journal Networking Tutorials Session 1813 Traffic Behavior in addition to Queuing in a QoS Environment Prof. Dimitri P. Bertsekas Department of Electrical Engineering M.I.T. Objectives Provide some basic underst in addition to ing of queuing phenomena Explain the available solution approaches in addition to associated trade-offs Give guidelines on how to match applications in addition to solutions Outline Basic concepts Source models Service models (demo) Single-queue systems Priority/shared service systems Networks of queues Hybrid simulation (demo)
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Outline Basic concepts Per as long as mance measures Solution methodologies Queuing system concepts Stability in addition to steady-state Causes of delay in addition to bottlenecks Source models Service models (demo) Single-queue systems Priority/shared service systems Networks of queues Hybrid simulation (demo) Per as long as mance Measures Delay Delay variation (jitter) Packet loss Efficient sharing of b in addition to width Relative importance depends on traffic type (audio/video, file transfer, interactive) Challenge: Provide adequate per as long as mance as long as (possibly) heterogeneous traffic Solution Methodologies Analytical results ( as long as mulas) Pros: Quick answers, insight Cons: Often inaccurate or inapplicable Explicit simulation Pros: Accurate in addition to realistic models, broad applicability Cons: Can be slow Hybrid simulation Intermediate solution approach Combines advantages in addition to disadvantages of analysis in addition to simulation
Examples of Applications Queuing System Concepts: Arrival Rate, Occupancy, Time in the System Queuing system Data network where packets arrive, wait in various queues, receive service at various points, in addition to exit after some time Arrival rate Long-term number of arrivals per unit time Occupancy Number of packets in the system (averaged over a long time) Time in the system (delay) Time from packet entry to exit (averaged over many packets) Stability in addition to Steady-State A single queue system is stable if packet arrival rate < system transmission capacity For a single queue, the ratio packet arrival rate / system transmission capacity is called the utilization factor Describes the loading of a queue In an unstable system packets accumulate in various queues in addition to /or get dropped For unstable systems with large buffers some packet delays become very large Flow/admission control may be used to limit the packet arrival rate Prioritization of flows keeps delays bounded as long as the important traffic Stable systems with time-stationary arrival traffic approach a steady-state Littles Law For a given arrival rate, the time in the system is proportional to packet occupancy N = T where N: average of packets in the system : packet arrival rate (packets per unit time) T: average delay (time in the system) per packet Examples: On rainy days, streets in addition to highways are more crowded Fast food restaurants need a smaller dining room than regular restaurants with the same customer arrival rate Large buffering together with large arrival rate cause large delays Explanation of Littles Law Amusement park analogy: people arrive, spend time at various sites, in addition to leave They pay $1 per unit time in the park The rate at which the park earns is $N per unit time (N: average of people in the park) The rate at which people pay is $ T per unit time (: traffic arrival rate, T: time per person) Over a long horizon: Rate of park earnings = Rate of peoples payment or N = T Delay is Caused by Packet Interference If arrivals are regular or sufficiently spaced apart, no queuing delay occurs Regular Traffic Irregular but Spaced Apart Traffic Burstiness Causes Interference Note that the departures are less bursty Burstiness Example Different Burstiness Levels at Same Packet Rate Source: Fei Xue in addition to S. J. Ben Yoo, UCDavis, On the Generation in addition to Shaping Self-similar Traffic in Optical Packet-switched Networks, OPNETWORK 2002 Packet Length Variation Causes Interference Regular arrivals, irregular packet lengths High Utilization Exacerbates Interference As the work arrival rate: (packet arrival rate packet length) increases, the opportunity as long as interference increases Bottlenecks Types of bottlenecks At access points (flow control, prioritization, QoS en as long as cement needed) At points within the network core Isolated (can be analyzed in isolation) Interrelated (network or chain analysis needed) Bottlenecks result from overloads caused by: High load sessions, or Convergence of sufficient number of moderate load sessions at the same queue Bottlenecks Cause Shaping The departure traffic from a bottleneck is more regular than the arrival traffic The inter-departure time between two packets is at least as large as the transmission time of the 2nd packet Bottlenecks Cause Shaping Bottleneck 90% utilization Outgoing traffic Incoming traffic Exponential inter-arrivals gap Bottleneck 90% utilization Outgoing traffic Incoming traffic Large Medium Small Packet Trains Inter-departure times as long as small packets Variable packet sizes Peaks smeared Histogram of inter-departure times as long as small packets sec of packets Variable packet sizes Constant packet sizes Outline Basic concepts Source models Poisson traffic Batch arrivals Example applications voice, video, file transfer Service models (demo) Single-queue systems Priority/shared service systems Networks of queues Hybrid simulation (demo) Poisson Process with Rate l Interarrival times are independent in addition to exponentially distributed Models well the accumulated traffic of many independent sources The average interarrival time is 1/ l (secs/packet), so l is the arrival rate (packets/sec)
Batch Arrivals Some sources transmit in packet bursts May be better modeled by a batch arrival process (e.g., bursts of packets arriving according to a Poisson process) The case as long as a batch model is weaker at queues after the first, because of shaping Markov Modulated Rate Process (MMRP) Extension: Models with more than two states Stay in each state an exponentially distributed time, Transmit according to different model (e.g., Poisson, deterministic, etc) at each state Source Types Voice sources Video sources File transfers Web traffic Interactive traffic Different application types have different QoS requirements, e.g., delay, jitter, loss, throughput, etc.
Source Type Properties Typical Voice Source Behavior MPEG1 Video Source Model The MPEG1 MMRP model can be extremely bursty, in addition to has long range dependency behavior due to the deterministic frame sequence Diagram Source: Mark W. Garrett in addition to Walter Willinger, Analysis, Modeling, in addition to Generation of Self-Similar VBR Video Traffic, BELLCORE, 1994
Hybrid Simulation Results as long as Target Flow Total traffic volume 500 Mbps Time modeled 35 minutes Simulation duration 14 minutes Comparison: Hybrid vs Explicit Simulation References Networking Bertsekas in addition to Gallager, Data Networks, Prentice-Hall, 1992 Device Queuing Implementations Vegesna, IP Quality of Service, Ciscopress.com, 2001 http://www.juniper.net/techcenter/techpapers/200020.pdf Probability in addition to Queuing Models Bertsekas in addition to Tsitsiklis, Introduction to Probability, Athena Scientific, 2002, http://www.athenasc.com/probbook.html Cohen, The Single Server Queue, North-Holl in addition to , 1992 Takagi, Queuing Analysis: A Foundation of Per as long as mance Evaluation. (3 Volumes), North-Holl in addition to , 1991 Gross in addition to Harris, Fundamentals of Queuing Theory, Wiley, 1985 Cooper, Introduction to Queuing Theory, CEEPress, 1981 OPNET Hybrid Simulation in addition to Micro Simulation See Case Studies papers in http://secure.opnet.com/services/muc/mtdlogis-cse-stdies-81.html
McCarthy, Todd Chief Film Critic
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