ENGS Lecture 6 ENGS 4 - Lecture 6 Technology of Cyberspace Winter 2004 Thayer School of Engineering Dartmouth College Instructor: George Cybenko, x Assistant: Sharon Cooper (“Shay”), x Course webpage:

ENGS Lecture 6 Today’s Class Wrap-up of rule-based systems Jon’s mini-lecture Html tables and formatting Internet routing basics Break Bellman-Ford Routing Algorithm Abby’s mini-lecture State-based methods for “Predicting the Future”

ENGS Lecture 6 Jon’s Mini-lecture

ENGS Lecture 6 Basic HTML tables... defines a table in HTML. If the BORDER attribute is present, your browser displays the table with a border.... specifies a table row within a table. You may define default attributes for the entire row.... defines a table data cell. By default the text in this cell is aligned left and centered vertically. Table data cells may contain other attributes to determine the characteristics of the cell and/or its contents. See Table Attributes at the end of this table for more information. Attribute width=“30%” determines percentage of the table a column uses. Attribute width=“30” determines how many pixels column uses.

ENGS Lecture 6 Basic HTML tables ALIGN =“LEFT”, “CENTER”, “RIGHT” VALIGN=“TOP”, “MIDDLE”, “BOTTOM”... defines a table header cell. By default the text in this cell is bold and centered. Table header cells may contain other attributes to determine the characteristics of the cell and/or its contents. See Table Attributes at the end of this table for more information. Attributes cspan and rspan determine how many columns and rows a cell spans.

ENGS Lecture 6 Basic HTML lists delimits an “ordered” list (ie numbered) delimits an “unordered” list (ie bullets) In each case, denotes a list item. Example: Monday Tuesday Wednesday

ENGS Lecture 6 Internet Routing Basics

ENGS Lecture 6 “Switching” is necessary In the early days of telephones, telephone lines were dedicated to pairs of customers. This very quickly became unscalable. Lines had to be “switched” on demand.

ENGS Lecture 6 Circuit Switching Reserve a “circuit” and that “guarantees” services for each user Requires significant “setup time” Good for telephones, bad for web browsing!!!!

ENGS Lecture 6 Packet Switching Send “packets” into the network, routing each packet individually, like the post office. Packets are “routed” through the network, sorted at “switches”. Requires no setup time but no guarantee of service!! Bad for telephones, good for web browsing!!!!

ENGS Lecture 6 Time vs Frequency Division Multiple Access (TDMA vs FDMA) TDMA – Divide a communications resource or channel using time slots. Users take turns using the same resource by using it only in their allotted slots. FDMA – Divide a communications resource or channel using frequency division. Users simultaneously use the channel but at different frequencies. Examples? Code Division Multiple Access

ENGS Lecture 6 Resolving internet addresses Your computer LAN DHCP Server DNS Server 1. Can I get an IP address, etc? 2. Here is a temporary address and other information. 3. What is the address of is Rest of the world Bridge or gateway or router or switch

ENGS Lecture 6 IP Packets and routing IP is Internet Protocol (also Intellectual Property sometimes) IP addresses consist of four numbers between 0 and 255 What is IP address? Router Network 1 Network 2 A router can be a dedicated piece of hardware or a computer with several network interfaces. Cisco, 3Com, etc sell routers. ?

ENGS Lecture 6 Network routing Routing table in a router looks like..... PatternNext node on path *.*host *.*.*host host 3 defaulthost 4

ENGS Lecture 6 Break

ENGS Lecture 6 Bellman-Ford Routing A E C D B F Computer/host Distance/cost/delay between hosts

ENGS Lecture 6 A E C D B F A 0 B 1 C * D 2 E * F * A 2 B * C 1 D 0 E 4 F * A 1 B 0 C 3 D * E * F * A * B 3 C 0 D 1 E * F 1 A * B * C 1 D * E 8 F 0 A * B * C * D 4 E 0 F 8 Initial table has distance to each host one hop away and * otherwise.

ENGS Lecture 6 A E C D B F A 0 0 B 1 1 C * 4 D 2 2 E * 6 F * * A 2 2 B * 3 C 1 1 D 0 0 E 4 4 F * 2 A 1 1 B 0 0 C 3 3 D * 3 E * * F * 4 A * 3 B 3 3 C 0 0 D 1 1 E * 5 F 1 1 A * * B * 4 C 1 1 D * 2 E 8 8 F 0 0 A * 6 B * * C * 5 D 4 4 E 0 0 F 8 8 newdistance to X = min( distance to neighbor + distance from neighbor to X) where min is over all neighbors

ENGS Lecture 6 A E C D B F A B C * 4 3 D E * 6 6 F * * 4 A B * 3 3 C D E F * 2 2 A B C D * 3 3 E * * 7 F * 4 4 A * 3 3 B C D E * 5 5 F A * * 4 B * 4 4 C D * 2 2 E F A * 6 6 B * * 7 C * 5 5 D E F Repeat it!!!

ENGS Lecture 6 A E C D B F A B C * D E * F * * 4 4 A B * C D E F * A B C D * E * * 7 7 F * A * B C D E * F A * * 4 4 B * C D * E F A * B * * 7 7 C * D E F Repeat it...stop when the table does not change.

ENGS Lecture 6 A E C D B F A A B B C * D D D E * D F * * 4 4 D A A B * A C C D D E E F * C A A B B C C D * A E * * 7 7 A F * C A * D B B C C D D E * D F F A * * 4 4 C B * C C C D * C E C F F A * D B * * 7 7 D C * D D D E E F D The min neighbor determines the paths

ENGS Lecture 6 A E C D B F A 0 A B 1 B C 3 D D 2 D E 6 D F 4 D A 2 A B 3 A C 1 C D 0 D E 4 E F 2 C A 1 A B 0 B C 3 C D 3 A E 7 A F 4 C A 3 D B 3 B C 0 C D 1 D E 5 D F 1 F A 4 C B 4 C C 1 C D 2 C E 6 C F 0 F A 6 D B 7 D C 5 D D 4 D E 0 E F 6 D Only need the total distances and the next neighbor

ENGS Lecture 6 A E C D B F A 0 A B 1 B C 3 D D 2 D E 6 D F 4 D A 2 A B 3 A C 1 C D 0 D E 4 E F 2 C A 1 A B 0 B C 3 C D 3 A E 7 A F 4 C A 3 D B 3 B C 0 C D 1 D E 5 D F 1 F A 4 C B 4 C C 1 C D 2 C E 6 C F 0 F A 6 D B 7 D C 5 D D 4 D E 0 E F 6 D Ooops...what if the network changes?? 2 Have enough information to keep updating the table until it stops changing

ENGS Lecture 6 Abby’s Mini-lecture

ENGS Lecture 6 “Predicting the Future” Newtonian revolution (late 1600’s) : F=ma Concept of “state” introduced The “state” of a system is all that is needed to predict it’s future states. Having additional information about the system’s past states does not help to predict it’s future. This defines the notion of “state”.

ENGS Lecture 6 Example A cannonball shot from a canon. Where will it fall? ??? position + momentum of the canonball is the “state”

ENGS Lecture 6 State-based Prediction What are examples of state-based prediction? Astronomy Chemistry Biology Medicine Others?