The Bio-Networking Architecture: Adaptation of Network Applications through Biological Evolution

1. The Bio-Networking Architecture:Adaptation of Network Applicationsthrough Biological Evolution Jun Suzuki and Tatsuya Suda{jsuzuki, suda}@ics.uci.eduhttp://netresearch.ics.uci.edu/bionet/ Dept. of Information and Computer ScienceUniversity of California, Irvine

2. 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.

3. Cyber-Entity (CE) • Each CE stores and expends energy • in exchange for performing service. • for using resources. • 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. Cyber-entity GUID energy level Attributes age relationship list executable code Body non-exec. data energy exchange migration replication reproduction Behavior pheromone emission resource sensing relationship social networking

4. 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

5. Weight and threshold values in each behavior policy change dynamically through mutation. Mutation occurs during replication and reproduction. 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. Automatic Diversity Generation Behavior Policy Parameter Set parents Behavior Policy Parameter Set Behavior Policy Parameter Set Migration Policy Params Migration Policy Params Migration Policy Params weight 1 weight 2 threshold weight 1 weight 2 threshold weight 1 weight 2 threshold Reproduction Policy Params Reproduction Policy Params weight 1 weight 2 Weight 3 threshold weight 1 weight 2 Weight 3 threshold Behavior Policy Parameter Set Reproduction Policy Params Migration Policy Params weight 1 weight 2 Weight 3 threshold weight 1 weight 2 threshold reproducedchild Reproduction Policy Params weight 1 weight 2 Weight 3 threshold . . .

6. 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. Example Simulation Results response time to user requests (mutation/crossover on) Network configuration response time to user requests (mutation/crossover off) users’ movement hop counts between CEs and users (mutation/crossover on) hop counts between CEs and users (mutation/crossover off)

7. (Config. 1) Same resource coston all the platforms Response time • Investigates fitness under different distributions of resource cost. # of platformshosting CEs resource cost Energy utility (Config. 2) Different resourcecosts on different platforms 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.