Read Chapter 4 of Kurose-Ross

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1 CSE 422 Notes, Set 4 These slides contain materials provided with the text: Computer Networking: A Top Down Approach,5th edition, by Jim Kurose and Keith Ross, Addison-Wesley, April Additional figures are repeated, with permission, from Computer Networks, 2nd through 4th Editions, by A. S. Tanenbaum, Prentice Hall. Some figures are repeated from an OPNET Technologies presentation by Dmitri Bertsekas and course notes from Rutgers University. The remainder of the materials were developed by Philip McKinley and Dennis Phillips at Michigan State University Assignment: Read Chapter 4 of Kurose-Ross Transport Layer 1

2 Chapter 4: Introduction (forwarding and routing) Review of queueing theory Routing algorithms Link state, Distance Vector Router design and operation IP: Internet Protocol IPv4 (datagram format, addressing, ICMP, NAT) Ipv6 Routing in the Internet Autonomous Systems Routing protocols (RIP, OSPF, BGP) Network layer deliver segment from sending to receiving host on sending side encapsulates segments into datagrams on receiving side, delivers segments to transport layer layer protocols in every host, router router examines header fields in all IP datagrams passing through it application transport application transport 2

3 Two Key Network-Layer Functions routing: determine route taken by packets from source to destination Distributed routing in the Internet Could also be centralized (drawbacks?) forwarding: move packets from router s input to appropriate router output Interplay between routing and forwarding routing algorithm local forwarding table header value output link value in arriving packet s header

4 Network layer connection and connection-less service Flavors of service datagram provides -layer connectionless service (e.g., IP) virtual circuit (such as ATM) provided -layer connection service analogous to the transport-layer services UDP and TCP, but: service: host-to-host no choice: provides one or the other implementation: in core, not on hosts A quick word about ATM Asynchronous Transfer Mode started in late 1980s attempt to merge telecom and computer s Based on virtual circuit packet switching reserve the route between source and destination do not reserve capacity All data broken into tiny, fixed size packets these ATM cells were only 53 bytes long (!) virtual circuit identifiers (not full destination addresses) in cell headers used to switch data streams through the 4

5 ATM Discussion Why small, fixed-size packets? Advantages of ATM? Disadvantages? So, what happened to ATM? Currently dominating: Datagrams no call setup at layer routers: no state about end-to-end connections no -level concept of connection packets forwarded using destination host address packets between same source-dest pair may take different paths application transport 1. Send data 2. Receive data application transport 5

6 Datagram Issues Since capacity is not reserved, datagrams compete for resources: Communication links Router buffers Bursty traffic creates periods of high and low demand, resulting in queueing of datagrams at routers. Performance depends on complex interactions. Advent of packet switching produced need to: analyze performance of existing s predict performance of new designs Chapter 4: Introduction (forwarding and routing) Review of queueing theory Routing algorithms Link state, Distance Vector Router design and operation IP: Internet Protocol IPv4 (datagram format, addressing, ICMP, NAT) Ipv6 Routing in the Internet Autonomous Systems Routing protocols (RIP, OSPF, BGP) 6

7 Brief Review of Queueing Theory Definition: Any system in which arrivals place demands upon a finite-capacity resource may be termed a queueing system. Applications of Queueing Theory analysis of computer s telephone switching systems customer service centers automobile traffic control almost any application involving entities waiting for and receiving service For studying computer s View as collections of queues First-in, first-out (FIFO) data structures Queueing theory provides probabilistic analysis of these queues Examples: Average queue length Average waiting (and service) time Probability that queue is at a certain length Probability of queue overflow (packets will be dropped) 7

8 References: Definitive text on queueing theory: Kleinrock, Queueing Systems, Volume I: Theory, John Wiley, New York, Little s Law Parameters and metrics N -average number of customers in the system (basically, queue size plus customer currently being serviced) T -average delay per customer (time that the customer waits plus time that customer is serviced) - average customer arrival rate 8

9 Little s Law Relationship: Proof A customer arrives at the queue After a period of time T (average), the customer is at the front of the queue How many new customers have queued behind this one? Examples Example 1 Meijer at 5:30 p.m. N =6, =0.33 Example 2 Meijer at 1:00 a.m. N =0.25, =0.1 9

10 Networking Example Arrival of packet at a router is 2000 pps Average time in the router 6 ms. Average number of packets in router? Summary System Arrivals Departures Little s Law: Mean number tasks in system = mean arrival rate x mean response time Observed for centuries, Little was first to prove Applies to any queueing system in equilibrium, as long as nothing in black box is creating or destroying tasks 10

11 Packet Queueing If arrivals are regular or sufficiently spaced apart, no queueing delay occurs Regular Traffic Irregular but Spaced Apart Traffic Burstiness causes interference Note that the departures are less bursty 11

12 Burstiness Example Same packet rate, different burstiness Length variation Length variation also causes interference and, hence, delays Queueing Delays 12

13 Traffic volume High utilization exacerbates interference and leads to more queueing Queueing Delays Bottlenecks Types of bottlenecks At entry points At routers within the core Bottlenecks result from overloads caused by: High load sessions, or Convergence of sufficient number of moderate load sessions at the same queue 13

14 Analysis How to capture the randomness associated with: Packet volume? Packet arrival patterns? Packet lengths? Little s Law is useful for analyzing an existing system, but designing a new system (e.g., a router) requires deeper analysis. Example: how many buffers are needed, given expected traffic patterns and loads? Stochastic Processes Definition: A random variable is a variable value depends on the outcome of a random experiment For each outcome ω of an experiment, we associate a real number X(ω), X(ω) is the value the random variable takes on when the experimental outcome is ω. Example: payoffs for various rolls of dice Definition: A stochastic process is a function of time X(t, ω), usually written as simply X(t) values are random variables can be discrete or continuous 14

15 Examples Dow Jones closing averages. Number of cars that have pulled into Meijer s parking lot by time t. Number of packets generated by a host by time t. Number of packets arriving at a router by time t. Poisson Processes Definition: A stochastic process {A(t) t > 0} taking on nonnegative integer values is said to be a Poisson process with rate if A(t) is a counting process that represents the total number of arrivals that have occurred from 0 to time t. The numbers of arrivals that occur in disjoint time intervals are independent. The number of arrivals in any interval of length is Poisson distributed with parameter P {A(t + ) A(t) = n} = for n =0, 1, 2,... 15

16 Example Customers join Meijer s checkout lane with an average arrival rate of one customer every 2 minutes. What is the probability that exactly 2 customers join the queue in a period of 5 minutes? What is the probability that no customers join the queue in this time? Poisson Process Properties Interarrival times are independent and exponentially distributed with parameter P{ n t} = 1 e t, t 0 Why? Graph? 16

17 Different values of Probability that interarrival time is less than t (x axis) 1 e t time (t) Example Assume that phone call times are exponentially distributed about some mean 1/λ, that is, P{call duration t} = 1 e t 1/λ = mean length of a call. Why? Suppose 1/λ = 2 minutes, so λ = 0.5/min P{call duration 2 minutes} = 1 e -0.5*2 = Hmm. Not 0.5? P{call duration 10 minutes} = 1 e -0.5*10 = P{call duration 20 minutes} = 1 e -0.5*20 =

18 Consider this situation: You are attending a retreat in a remote location No cell phone service!?! Two public phones are available in the lobby of the lodge You need to make a call, but both phones are currently busy A person nearby tells you that one person just started his/her call, while the other has been on the phone for 30 minutes. Which phone should you wait for? The Markov Property The exponential distribution is said to be: Specifically, Why does this property hold? 18

19 Queueing System Characterization Five components Interarrival-time pdf Service-time pdf Number of servers Queuing discipline, that is, how customers are taken from the queue Number of buffers (for customers) We will consider infinite-buffer, single-server systems, using a FIFO queueing discipline Notation Notation for first 3 components: A/B/m, with A and B chosen from M -Markov (exponential pdf) D -Deterministic (all customers have same value) G -General (arbitrary pdf) Example of M/M/1? Example of M/D/1? Example of D/D/1? 19

20 The M/M/1 Queueing System Both arrival-time pdf and service-time pdf are exponential, with a single server = arrival rate, µ = server capacity Let the state i of the queueing system be the number of customers in the system When a customer arrives or a customer departs the system, the system moves to an adjacent state (i +1 or i 1) P i = P {system is in state i} M/M/1 Queue N Nq m Tq 1 m T 20

21 M/M/1 (cont.) In equilibrium, P i does not change over time! Such queueing systems are known as birthdeath systems. State diagram: n-1 n n+1 m m m m State probabilities Diagram: P 0 = mp 1 21

22 Solve for P 0 M/M/1 at a glance What is ρ? Since and m are constants, P i alone describes the current state of the system. Why can we describe the ENTIRE state of the system by a single parameter? 22

23 Computing N Computing T 23

24 Example Checkout lane. = 0.25 and m = 1.0 M/M/1 Results The analysis gives the steady-state probabilities of number of packets in queue or transmission P{n packets} = r n (1-r) where r = /m From this we can get the averages: N = r/(1 - r) T = N/ = r/ (1 - r) = 1/(m - ) 24

25 Networking Example On a router, measurements show that packets arrive at a mean rate of 125 packets per second (pps) and the router takes about 2 milliseconds to forward them. Assuming an M/M/1 model, What is the probability of buffer overflow if the router had only 13 buffers? How many buffers are needed to keep packet loss below one packet per million? Example (cont.) Arrival rate λ = Service rate μ = Gateway utilization ρ = λ/μ = Prob. of n packets in gateway = Mean number of packets in router = 25

26 Example (cont.) Probability of buffer overflow: = P(more than 13 packets in gateway) Example (cont.) To limit the probability of loss to less than 10-6 : 26