lecture xvii: distributed systems algorithms inspired by biology

CMPT 401 Summer 2007

Dr. Alexandra Fedorova

Lecture XVII: Distributed Systems Algorithms Inspired by Biology

2CMPT 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


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


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


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)


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


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


Example: Laying a Trail (cont.)

Ants started on the right

Ants started on the left

Shorter path has more pheromones


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


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


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


Updating Pheromone Tables (cont.)

12

4

3

source

destinationcurrent location

Update routing table at node 1 for node 3

2 4

prob(2) = X prob(4) = Y increase by Δp the probability of

taking 4 as next hop


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


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


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)


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


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)


LMC

S

55

Node utilization

Total capacity = 10Spare capacity =

10 - utilization

Spare capacity

64

5519

37

Minimum capacity of red route: 4Minimum capacity of blue route: 5Route with largest minimal capacity: blue


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


LMC Algorithm (Illustration)

S

5564

5519

37

T P

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


MSSU

S

5Node

utilization

MSSU: Minimal sum of squared utilizations (SU)

6

51

3

MSSU of red route: 37MSSU of blue route: 50Route with minimal SSU: red

SU = 25

SU = 25

SU = 36

SU = 1


MSSU Algorithm

S

56

51

3

T(0) 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


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

Destination Next hop

A D

B D

C

D

A B

Routing table at node C


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


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


Results

• What do these numbers indicate? • Which load balancing method performed the best?


Results (cont.)

• Percentage of failed calls after stopping load balancing (call probabilities remain unchanged)

• What does this tell us about the system?


Results (cont.)


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

lecture xvii: distributed systems algorithms inspired by biology

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