Immune Based Bio-Network Architecture and its Simulation Platform for Future Internet

Immune Based Bio-Network Architecture and its Simulation Platform for Future Internet

Yong-Sheng Ding (Ministry of Education, China), Xiang-Feng Zhang (Ministry of Education, China & Shanghai Dianji University, China) and Li-Hong Ren (Ministry of Education, China)
DOI: 10.4018/978-1-60566-310-4.ch010
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Future Internet should be capable of extensibility, survivability, mobility, and adaptability to the changes of different users and network environments, so it is necessary to optimize the current Internet architecture and its applications. Inspired by the resemble features between the immune systems and future Internet, the authors introduce some key principles and mechanisms of the immune systems to design a bio-network architecture to address the challenges of future Internet. In the bio-network architecture, network resources are represented by various bioentities, while complex services and application can be emerged from the interactions among bio-entities. Also, they develop a bio-network simulation platform which has the capability of service emergence, evolution, and so forth. The simulation platform can be used to simulate some complex services and applications for Internet or distributed network. The simulators with different functions can be embedded in the simulation platform. As a demonstration, this chapter provides two immune network computation models to generate the emergent services through computer simulation experiments on the platform. The experimental results show that the bio-entities on the platform provide quickly services to the users’ requests with short response time. The interactions among bio-entities maintain the load balance of the bio-network and make the resources be utilized reasonably. With the advantages of adaptability, extensibility, and survivability, the bio-network architecture provides a novel way to design new intelligent Internet information services and applications.
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The Requirements of Future Internet

Future Internet should exhibit a strong sense of automation: 1) Support for survivability from massive failures and attacks; 2) Ability to configure and reconfigure system dynamically; 3) Awareness of Internet system environment; 4) Seeking of behavior optimization to achieve its goal; and 5) Requirement to detailed knowledge of system components and status.

The requirements of future Internet resemble the self-organizing and the self-healing properties of biological systems. There is a strong similarity between the complex interaction of organisms in biological systems and that of components in a networked system (Bhalla & Lyengar, 1999; Girvan & Newman, 2002). This makes us to study the relationships among components in the Internet environment by associating it with biological systems, especially with some key concepts and principles in biological immune systems. As such, we can introduce some immune mechanisms to study evolutionary Internet systems with those desirable properties.

Key Terms in this Chapter

Immune Symmetrical Network: The symmetrical network theory for the immune system is a tractable first approximation. The principle lymphocytes fall into just two specificity classes. The first class is the antigen-binding set, denoted T+ and B+ for T cells and B cells respectively. The second set is minus or anti-idiotypic set, T- and B-. There are three types of interaction between the plus and minus sets as follows: stimulation, inhibition, and killing. Stimulation can occur when two lymphocytes encounter each other. The receptors of one lymphocyte (‘+’) can cross-link the receptors of a second lymphocyte (‘-’), the converse is also true. So stimulation is assumed to be symmetrical in both directions between the two sets. Specific T cells factors could inhibit receptors. Finally, antibody molecules are assumed to be killed in a symmetrical fashion. According to the interaction among B cells and T cells, we can receive a set of four stable steady states for the system of T+, B+, T- and B- cells. The steady states are the initial state, the suppressed or unresponsive state, the immune state and the anti-immune state.

P2P Network: In P2P network, every node (peer) of the system acts as both client and server and provides part of the overall resources/information available from the system. In a pure P2P system, no central coordination or central database exists and no peer has a global view of the system. Participating peers are autonomous and self-organize the system’s structure, i.e., global behavior emerges from local interactions. P2P technologies have many applications, such as file sharing and exchanging, distributed computing, collaborative system, P2P computing, and enterprise applications.

Mutual-Coupled Immune Network: The mutual-coupled immune network hypothesis shows that the immune systems are constructed by forming large-scale immune networks with the interactions among small-scale networks. According to the hypothesis, we consider that each small-scale network has a specific task, and can be regarded as a local immune network (LIN). The interactions among the small-scale networks form a global network, and the global network has its remarkable ability to accomplish a complex task.

Biological Immune Systems: The biological immune system in our body is an efficient adaptive system. According to the immunologists, the components such as cells, molecules and organs in the biological immune system can prevent the body from being damaged by pathogens, known as antigens. The basic components of the immune system are lymphocytes that have two major types, B cells (B lymphocytes) and T cells (T lymphocytes). Biological immune systems are adaptive systems and their learning behaviors take place through evolutionary mechanisms similar to biological evolutions. They are distributed systems without central control. They can survive local failures and external attacks and maintain balance because of emergent behaviors of the interactions of many local elements, like immune cells. In the immune systems, the whole is more than the sum of the systems’ parts because of the interactions among the parts, just as emergent behaviors in other complex systems.

Bio-Network Platform: The bio-network simulation platform is a software framework fully implemented by Java language based on the bio-network architecture by utilizing multi-agent systems. The ideal model would place the bio-network platform on every device as a network node. The bio-network simulation platform has the capability of service emergence, evolution etc.. The platform can be used to simulate some complex services and applications for Internet or distributed systems.

Web Service: Web services are self-contained, modular applications that can be described, published, located, and accessed over network by using open standards. The functionality of the individual Web service is limited and cannot satisfy some practical requirements. The potential of Web services can only be achieved if they are used to dynamically compose some new Web services that provide more sophisticated functionalities compared to existing ones. The Web service composition is a highly complex task, and it is already beyond the human capability to deal with the whole process manually. Some methods for automatic composition and management of Web services have been proposed. They are conducted to fall into the realm of workflow composition or artificial intelligence planning methods.

Emergent Computation: Emergent computation is generally characterized by the interaction of relatively simple entities, forming a system to exhibit emergent properties. Emergent computation has three aspects: self-organization, aggregative behavior, and cooperative behavior.

Future Internet: The future Internet will be the core of the worldwide information infrastructure and the general service platform with computation, communication, entertainment, e-business, and so on. Future Internet should configure and reconfigure its network services dynamically to satisfy demanders and Internet application can adapt to the change of different network environments. Internet nodes also should be secure and can survive failures and attacks. Obviously, future Internet should be capable of extensibility, survivability, mobility, and adaptability to the changes of different users and network environments.

Bio-Network Architecture: We consider the biological immune system as a set of active computational components interacting in a dynamic and often unpredictable environment. Then, the behaviors of the biological immune systems can be modeled in terms of bio-entities and society-entities. Bio-entities in the bio-network architecture are regarded as autonomous agents and possess the characteristics such as interaction, no central control, diversity, mobility, and evolution. The bio-network architecture will make it possible to emerge the complexity of the biological immune systems from the interactions of bio-entities. We only need design simple behaviors of bio-entities, while complex biological behaviors are the emergence of bio-entities behaviors. The layered infrastructure of the bio-network architecture consists of bio-entities, bio-entity survivable environment, bio-network core services, bio-network low-level functional modules, and bio-network simulators.

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