1. CMPT 401 Summer 2007 Dr. Alexandra Fedorova Lecture XVII: Distributed Systems Algorithms Inspired by Biology

2. 2 CMPT 401 Summer 2007 © A. Fedorova Problem Statement Load balancing in telecommunication networks Calls originate and end nodes and are destined to end nodes Calls are routed through intermediate switching stations or nodes Each node has a certain capacity – can support only a limited number of calls routed through it Many routes for each call Routing tables determine the route If the call is routed via a congested node, it must be dropped Goal: construct routing tables that minimize the number of dropped calls under changing load conditions

3. 3 CMPT 401 Summer 2007 © A. Fedorova Potential Solutions Central controller: knows about the entire system, updates routing tables at nodes –Nodes must communicate with the controller –The controller is a single point of failure Use shortest-path routing –Determine the shortest path from each source to each destination –Construct routing tables to reflect shortest path routes (this can be done because network topology does not change) –This will occupy the fewest nodes for each call, but will not necessarily result in routing along the least congested path Mobile agents –Software agents (worms) move from node to node. Update routing tables based on their observations of the network

4. 4 CMPT 401 Summer 2007 © A. Fedorova Structure of the Paper Schoonderwoerd et al. Ant-based load balancing in telecommunications networks Present a new solution – a new kind of distributed mobile agent –Behaviour inspired by that observed in colonies of ants Evaluate –A simulated network –Measure the rate of dropped calls Compare with –A different kind of mobile agent –Static routing table

5. 5 CMPT 401 Summer 2007 © A. Fedorova Inspired by Nature Ants are silly animals that accomplish sophisticated results as a team –Regulating nests temperature within limits of 1˚C –Forming bridges –Raiding particular areas for food –Building and protecting their nest –Cooperating in carrying large items –Finding the shortest routes from the nest to a food source Mobile agents: we want them to be silly (i.e., simple), but accomplish sophisticated things (load balancing in the communications network)

6. 6 CMPT 401 Summer 2007 © A. Fedorova How Ants Cooperate Stigmetry – indirect communication through the environment –Produce specific actions in response to local environmental stimuli –These actions in turn affect the environmental stimuli that caused those actions –The new stimuli affect actions of the ants that come to that location Sematectonic stigmetry –Produce the environmental change: i.e., deposit a ball of mud –Causes other ants to repeat the action, i.e., deposit another ball of mud Sign-based stigmetry –Deposit pheromones (smelly substances) that cause other ants to behave differently, responding to the presence of pheromones

7. 7 CMPT 401 Summer 2007 © A. Fedorova Example: Laying a Trail (cont.) Ants lay pheromones as they travel along a trail A trail’s strength is determined by the amount of pheromones on the trail Amount of pheromones depends on: –The rate at which pheromones are laid –The amount of pheromones laid – how many ants laid them –How much time has passed since the pheromones were last laid (pheromones evaporate over time) If many ants follow along the same trail the total amount of pheromones is high – the trail’s strength is high: –Rate of deposit is high –Pheromones laying is recent

8. 8 CMPT 401 Summer 2007 © A. Fedorova Example: Laying a Trail (cont.) Ants started on the right Ants started on the left Shorter path has more pheromones

9. 9 CMPT 401 Summer 2007 © A. Fedorova Potential Problems Blocking problem –An available route is suddenly blocked –It may take a while to find a new route Shortcut problem –A better route becomes available –It may take a while to adapt to the new route

10. 10 CMPT 401 Summer 2007 © A. Fedorova ABC: Ant-Based Control Routing tables are replaced with pheromone tables Each node in the network has a pheromone table for every other node Each table has an entry for each neighbour, indicating the probability of using that neighbour as the next hop Pheromone laying is updating probabilities

11. 11 CMPT 401 Summer 2007 © A. Fedorova Updating Pheromone Tables At every time step ants can be launched from any node in the network The destination node is random Ants move from node to node, selecting the next node according to pheromone tables for their destination node At each node they update probabilities of the entry corresponding to their source node They increase the probability associated with the node where they came from

12. 12 CMPT 401 Summer 2007 © A. Fedorova Updating Pheromone Tables (cont.) 1 2 4 3 source destination current location Update routing table at node 1 for node 3 24 prob(2) = Xprob(4) = Y increase by Δp the probability of taking 4 as next hop

13. 13 CMPT 401 Summer 2007 © A. Fedorova Ageing and Delaying Ants Recall the system’s objectives: –Find routes that are short; avoid routes that are congested This is accomplished by ageing and delaying ants Ageing ants: –Age: the number of time steps the ant has travelled –Δp reduces progressively with the age of the ant –This biases the system to ants who use shorter trails Delaying ants: –Delay ants at nodes that are congested –Degree of delay correlated with the degree of congestion –This delays updates to pheromone tables leading to congested nodes –Increases age of ants travelling through congested nodes, so their pheromones have a smaller influence on pheromone tables

14. 14 CMPT 401 Summer 2007 © A. Fedorova Routing Calls in ABC Network Route call to destination D At the current node, look up the pheromone table for node D Choose the highest probability in the table The node corresponding to the largest probability is chosen as the next hop The call is placed if the route is not congested, otherwise the call is dropped

15. 15 CMPT 401 Summer 2007 © A. Fedorova Solving Blocking And Shortcut Problems Add a noise factor to ants movement protocol With probability f ant chooses a random path This ensures that –Useless routes are used occasionally (so they can be rediscovered if they suddenly become good) –Encourage more rapid discovery of a new route (if it becomes available)

16. 16 CMPT 401 Summer 2007 © A. Fedorova ABC: Putting it All Together Ants are regularly launched with random destinations on every part of the system Ants walk according to probabilities in pheromone tables from their destination Ants update the probabilities in the pheromone table for their source location They increase the probability of selecting their previous node on the path as the next hop (to their source node) The increase in probability is a decreasing function of the ant’s age The ants are delayed on parts of the system that are congested

17. 17 CMPT 401 Summer 2007 © A. Fedorova Other Mobile Agents Mobile software agent –Load management agent –Parent agent Travels from node to node Updates routing table to find the least congested route Two variations: –Largest minimum capacity (LMC) –Minimum sum of squared utilizations (MSSU)

18. 18 CMPT 401 Summer 2007 © A. Fedorova LMC S 5 Node utilization Total capacity = 10 Spare capacity = 10 - utilization Spare capacity 6464 5 1919 3737 Minimum capacity of red route: 4 Minimum capacity of blue route: 5 Route with largest minimal capacity: blue

19. 19 CMPT 401 Summer 2007 © A. Fedorova LMC Algorithm Travel from node to node Label nodes as permanent and temporary For each node maintain the following fields: –Node ID –Largest minimum capacity of the route from that route to the node’s source agent –The neighbour of the node on this route Update routing tables to make the node along the LMC route as the next hop Node along the LMC route is made permanent

20. 20 CMPT 401 Summer 2007 © A. Fedorova LMC Algorithm (Illustration) S 5 6464 5 1919 3737 TP P P P Link colour indicates the next hop on the way to the S node The algorithm will choose route with largest minimal spare capacity Problem: can result in long routes, occupy many nodes along the way as a result. Does not look at total utilization of the route

21. 21 CMPT 401 Summer 2007 © A. Fedorova MSSU S 5 Node utilization MSSU: Minimal sum of squared utilizations (SU) 6 51 3 MSSU of red route: 37 MSSU of blue route: 50 Route with minimal SSU: red SU = 25 SU = 36 SU = 1

22. 22 CMPT 401 Summer 2007 © A. Fedorova MSSU Algorithm S 56 51 3 T(0) T(25) T(50) T(36) T(59) P P P T(37) Numbers in parenthesis indicate the SSU of the route from the node to S Will make nodes permanent after learning the MSSU of all possible routes Will choose the route with the minimal SSU

23. 23 CMPT 401 Summer 2007 © A. Fedorova Network Simulation A software simulator Node representation: –A node ID –A capacity – number of simultaneous calls that the node can handle (40) –Routing table with n-1 entries, one for each node. The routing table entry tells us the next hope to take for a given destination node –Probability of being the end node (source or destination of a call) –Spare capacity DestinationNext hop AD BD C D AB Routing table at node C

24. 24 CMPT 401 Summer 2007 © A. Fedorova Network Simulation (cont.) Calls are generated by a traffic generator –Call parameters: source node, destination node, call duration (170 time steps average) Call is routed using routing tables, spare capacity of intermediate nodes is reduced If there is no spare capacity on the route, the call will fail

25. 25 CMPT 401 Summer 2007 © A. Fedorova Experimental Setup Call probability set: a particular distribution of calls Adaptation period: run a load balancing mechanism Test period: measure network performance for the number of dropped calls

26. 26 CMPT 401 Summer 2007 © A. Fedorova Results What do these numbers indicate? Which load balancing method performed the best?

27. 27 CMPT 401 Summer 2007 © A. Fedorova Results (cont.) Percentage of failed calls after stopping load balancing (call probabilities remain unchanged) What does this tell us about the system?

28. 28 CMPT 401 Summer 2007 © A. Fedorova Results (cont.)

29. 29 CMPT 401 Summer 2007 © A. Fedorova Results (cont.)

30. 30 CMPT 401 Summer 2007 © A. Fedorova Summary In general ants performed better than other mobile agents –ABC system stores information not only about good current routes, but about good recent alternative routes –This allows it to adapt quickly to changes in network conditions Ants consume less network resources than mobile agents (ants don’t need to store info about all nodes visited) Ants can work concurrently without affecting each other; only one mobile agent can be active at once A failure of ant does not hurt the system – other ants will update pheromone tables: the failure of mobile agent affects launching of future agents, so the failure has to be detected