Dept. of Information and Computer Science
University of California, Irvine
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Bio-Networking
Evolution Simulator
Bio-Networking
Discovery Simulator
Bio-Networking
platform
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Bio-Networking Evolution Simulator has been developed to investigate the adaptation and evolution aspects of the Bio-Networking Architecture. A cyber-entity in this simulator consists of a set of behavioral factors that determines when to reproduce, replicate, migrate, and die. Cyber-entities interact with a platform, other cyber-entities and users in their surrounding environment, and invoke the behaviors based on information locally available to them. When a cyber-entity replicates or two cyber-entities reproduce, the evolutionary mechanism applied in this simulator modifies a behavioral factor in new offspring, creating diverse behavioral cyber-entities. By natural selection mechanism and through successive generations, only beneficial cyber-entities are retained on the network, enabling a network application to well-adapt in changing environments. The Bio-Networking Evolution Simulator is designed to demonstrate such evolvability and adaptability in the Bio-Networking Architecture.Bio-Networking Discovery SimulatorYou can find more details in Bio-Networking Evolution Simulator at this page.
We have developed two distinct discovery mechanisms. In both discovery mechanisms, objects (cyber-entities) contain relationships (links or pointers) to one another. These relationships form a network on which discovery queries are forwarded. Each object also contains a set of keywords that describe the contents of the object. Relationships of objects also include information (e.g., keywords) regarding the relationship partner, providing a mechanism to guide discovery queries.The Bio-Networking PlatformIn the first discovery mechanism, keyword-similarity and discovery history guide discovery. Keyword-similarity represents the degree of similarity between two objects and is defined as the ratio of keywords that are in common between an object and its relationship partner. Relationship history summarizes information on how a relationship partner performed in discoveries in the past and is defined as the ratio of successful discovery queries on a relationship relative to all the discovery queries forwarded on that relationship. Keyword similarity and relationship history are used at each object to determine which relationships have priority in forwarding discovery queries. A query is forwarded with greater priority to objects that are more similar to a query and for objects that are equally similar, history is used as a secondary priority. This discovery mechanism has been evaluated through simulations. Simulations demonstrate that including both keyword similarity and discovery history in the relationships of objects improves discovery performance. The papers that describe this first discovery mechanism are available at publications page.
In addition to the discovery mechanism based on keyword similarity between objects and relationship history of objects (i.e, the discovery mechanism described in the section above), we have developed another discovery mechanism that guides discovery through user's (i.e., discovery originator's) preference for the received discovery hits. (For instance, a user may prefer discovery hits from a reputable web site to discovery hits from an unfamiliar web site.) User preference is defined as the user's degree of satisfaction with the returned hit, and allows the user to reward better hits from the discovery hits the user receives. This allows subsequent discoveries to obtain hits with greater user preference, and therefore users are likely to have greater satisfaction with these discovery hits.
In the discovery mechanism using user preference, each relationship is associated with one or more keywords, and for each keyword, a strength value is also associated. Keyword strength represents the usefulness of the relationship in discovering an object that contains the given keyword and satisfied many users. In the proposed discovery mechanism, both the new keywords added to relationships and the strength values of the keywords are adjusted based on user preference. After a user has received discovery hits, the user's evaluation is forwarded along the same path that the discovery hit returned along. Over time, the keyword strength of a relationship represents how useful the relationship was at discovering an object that contained the given keyword and also satisfied many users. Through simulations, we have examined how the discovery mechanism using user preference improves the average user preference for hits over time. The papers that describe the second discovery mechanism are available at publications page. You can also download a simulator implementing the second discovery mechanism.
The Bio-Networking platform is a middleware system that provides reusable software components for deploying and executing cyber-entities. The documentation for its design and implementation is available from here.Slides and Documents