Self-Evolvable Protocol Design Using Genetic Algorithms

Self-Evolvable Protocol Design Using Genetic Algorithms

Wenbing Yao (Huawei Technologies, UK), Sheng-Uei Guan (Xian Jiatong-Liverpool University, China), Zhiqiang Jiang (National University of Singapore, Singapore) and Ilias Kiourktsidis (Brunel University, UK)
DOI: 10.4018/978-1-4666-1749-0.ch003
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Abstract

Self-modifying protocols (SMP) are protocols that can be modified at run time by the computers using them. Such protocols can be modified at run time so that they can adapt to the changing communicating environment and user requirements on the fly. Evolvable protocols are SMP designed using Genetic Algorithms (GA). The purpose of this paper is to apply Genetic Algorithms (GA) to design an evolvable protocol in order to equip communication peers with more autonomy and intelligence. The next-generation Internet will benefit from the concept of evolvable protocols. In this paper, we design a Self Evolvable Transaction Protocol (SETP) with a GA executor embedded. We then use the Network Simulator (NS2) to evaluate this evolvable protocol module to demonstrate the feasibility of our new design approach.
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Introduction

Rooted in the autonomic computing research (Kephart & Chess, 2003), autonomic communications (AutoComm) aim to achieve the self-governance of a communication system or network with the assistance of the self-* capabilities, which include self-management, self-configuration (Milcher, 2004), self-optimisation, self-healing (Liu, Zhang, Jiang, Raymer & Strassner, 2008), self-protection and self-monitoring (Gu, Strassner, Xie, Wolf & Suda, 2008). The rapid development of this field has attracted considerable attention from academia and industry in recent years. In Europe, the European Commission (EC) funded a number of projects on AutoComm, e.g. CASCADAS, ANA, HAGGLE under the Situated and Autonomic Communication (SAC) Initiative of the Sixth Framework Programme (FP6). Under the Seventh Framework Program (FP7) AutoComm research received further funding from the EC (Ref. projects EFIPSANS, SOCRATES, et al.) and has been recognized as a core feature of the future Internet. As pointed out in (Strassner, Fleck, Lewis, Parashar & Donnelly, 2008), the number of international conferences that focus on “autonomics” or “autonomically inspired” topics has increased rapidly in recent years, and the voice advocating standard organizations to make the movement towards the development of interoperability standards for autonomic systems are getting louder. It is evident that this surge of enthusiasm towards AutoComm will accelerate as more industrial companies become involved and relevant standards activities start.

The ultimate goal of autonomic communications is to “achieve the autonomy of communication networks with minimum human administration” (Gu et al., 2008), and the self-* capabilities of AutoComm systems are the key to achieving this goal. Gu, et al. identified the principal characteristics of an AutoComm system that distinguish it from conventional communication systems in (Gu et al., 2008). Dobson, et al., summarised the main challenges for AutoComm and reviewed the related work towards tackling some of the challenges in their survey paper (Dobson et al., 2006). In (Raymer, Meer & Strassner, 2008), researchers from the Architecture Expert Group of the Autonomous Communication Forum presented their design of the architecture of autonomic network system. In this architecture, based on the knowledge about the local network situation that is constantly being monitored, the Autonomic Management (AM) element produces and rectifies the network management policies according to the high level business targets, and the Autonomic Control (AC) mechanism proactively configures and adjusts the operations of the network devices according to the latest technical policies. It is clear from many recent literature that, most of the current research on AutoComm has been focused on adding the autonomic management and autonomic control mechanisms into the existing policy based network management (PBNM) mechanism (Gu et al., 2008; Jennings et al., 2007; Raymer et al., 2008).

The principle of AC element is straightforward. It monitors the local network’s situation, and feeds the observed data to the AM and PBNM elements. Taking the observed network situational data and the constraints from the PBNM as the reference target, the AC element applies the adaptive control theories to adjust the Policy Enforcement Point (e.g. a server or a router). This is illustrated in Figure 1.

Figure 1.

The architecture of an autonomic communication system

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