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The Bio-Networking Architecture: Adaptation of Network Applications through Biological Evolution Jun Suzuki and Tatsuya Suda {jsuzuki, suda}@ics.uci.edu http://netresearch.ics.uci.edu/bionet/ Dept. of Information and Computer Science University of California, Irvine

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Goals of the Simulation Study To show that the Bio-Networking Architecture adapts to diverse network conditions through behavioral evolution of autonomous cyber-entities (CEs) To show that evolutionary mechanisms (diversity generation and natural selection) allow CEs to increase their fitness to diverse network conditions.

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Cyber-Entity (CE) Each CE behavior policy consists of factors (F), weights (W), and a threshold. If > threshold, then reproduce. Example reproduction factors: StoredEnergyFactor contributes to the tendency for CEs to reproduce more often when they have enough energy. RequestRateFactor contributes to the tendency for CEs to reproduce more often when they receive a large number of service requests. RequestChangeRateFactor contributes to the tendency for CEs to reproduce more often when request rate is increasing. Behavior Attributes Body GUID energy level age non-exec. data executable code Cyber-entity migration replication reproduction pheromone emission resource sensing energy exchange social networking relationship relationship list Each CE stores and expends energy in exchange for performing service. for using resources.

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Evolutionary Mechanisms Diversity generation A CE behavior may be implemented by a number of algorithms/policies Manual diversity generation by human designers Automatic diversity generation through mutation and crossover during replication and reproduction Natural selection keeps entities with beneficial features alive CEs that adapt to environment well will contribute more to evolution. Energy used as a natural selection mechanism abundance induces replication and reproduction scarcity induces death

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Automatic Diversity Generation Weight and threshold values in each behavior policy change dynamically through mutation. Mutation occurs during replication and reproduction. Behavior Policy Parameter Set weight 1 weight 2 threshold Migration Policy Params weight 1 weight 2 Weight 3 threshold Reproduction Policy Params...... When reproducing, a CE selects a mate whose fitness to the current network condition is high. Fitness is a function of distance to users, response time to user requests, and energy utility. A child CE inherits different behaviors from different parents through crossover. Behavior Policy Parameter Set weight 1 weight 2 threshold Migration Policy Params weight 1 weight 2 Weight 3 threshold Reproduction Policy Params Behavior Policy Parameter Set weight 1 weight 2 threshold Migration Policy Params weight 1 weight 2 Weight 3 threshold Reproduction Policy Params Behavior Policy Parameter Set weight 1 weight 2 threshold Migration Policy Params weight 1 weight 2 Weight 3 threshold Reproduction Policy Params parents reproduced child

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Example Simulation Results Investigates the impact of mutation/crossover by comparing fitness of 2 populations of CEs; one with mutation/crossover, and the other without mutation/crossover Observation: Mutation/crossover allows CEs to gradually shorten response time to user requests and reduce distance to users. response time to user requests (mutation/crossover on) response time to user requests (mutation/crossover off) users movement Network configuration hop counts between CEs and users (mutation/crossover on) hop counts between CEs and users (mutation/crossover off)

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Investigates fitness under different distributions of resource cost. (Config. 1) Same resource cost on all the platforms (Config. 2) Different resource costs on different platforms Energy utility resource cost Observation: CEs gradually shorten response time to user requests in both config 1 and 2. The number of platforms hosting CEs approaches toward 1 in config 1. This does not happen in config 2. This means that CEs avoid to move to platforms whose resource cost is high. CEs increase energy utility in config 2 than in config 1. This means CEs save their energy in config 2 by running on platforms whose resource cost is low. # of platforms hosting CEs Response time