Injecting the Architectural Resiliency into Distributed Autonomic Systems using DIME Network Architecture Rao Mikkilineni KawaObjects Inc Los Altos, CA (USA) rao@kawaobjects.com Giovanni Morana University of Catania Catani, Italy giovanni.morana@dieei.unict.it Abstract— By introducing signaling and self-management in a Turing node and a signaling network as an overlay over the computing network, the current von-Neumann computing model is evolved to bring the architectural resiliency of cellular organisms to computing infrastructure. The DIME computing model introduces the genetic transactions of replication, repair, recombination and reconfiguration to program self- resiliency in distributed computing systems executing a managed workflow. The injection of parallelism and network based composition of “Self” identity are the first steps in introducing the elements of homeostasis and self-management in the computing infrastructure. DIMEs inject the architectural resiliency of cellular organisms to create a new class of distributed autonomic computing systems using managed Turing machine networks. Keywords-component; DIME; Distributed Autonomic Systems; Turing Machine; Bio-inspired Computing I. INTRODUCTION Autonomic computing by definition implies two components in the system: the observer (or the Self) and the observed (or the environment) with which the observer interacts by monitoring and controlling various aspects that are of importance. It also implies that the observer is aware of systemic goals to measure and control its interaction with the observed. In living organisms, the autonomic behavior is attributed to the self and to the consciousness that contribute to defining one’s multiple tasks to reach specific goals within a dynamic environment and adapting the behavior accordingly. The concept of self and self-reflection which models the observer, the observed and their interactions is quite different from a third party observation of a set of actors or agents interacting with each other. This paper examines recent advances in biology and neuro-science that attempt to explain consciousness and apply some of the lessons learned to model distributed computing systems in incorporating the features that contribute to self-aware interactions of computing elements. Some of the familiar elements in computing and communication systems such as fault, configuration, accounting, performance, security (FCAPS) and signaling are identified as key elements to create a new class of distributed systems where autonomic behavior is incorporated in the conventional Turing computing model. The paper is organized as follows. Section II introduces the concept of consciousness. Section III explains the importance of self-reflection and the observed/observer problems. Section IV discusses the need for a new computing model and introduces the Distributed Intelligent Managed Element (DIME) computing approach. Section V concludes with some observations II. CONSCIOUSNESS IN DISTRIBUTED SYSTEMS As recent advances in neuroscience throw new light on the process of evolution of the cellular computing models, it is becoming clear that communication and collaboration mechanisms of distributed computing elements played a crucial role in the development of self-resiliency, efficiency and scaling which are exhibited by diverse forms of life from the cellular organisms to highly evolved human beings. According to Damasio [3,4], managing and safe keeping life is the fundamental premise of biological entities. At the base of these features there is the homeostasis process. Homeostasis is the property of a system that regulates its internal environment and tends to maintain a stable, constant condition of properties like temperature or chemical parameters that are essential to its survival. System-wide homeostasis goals are accomplished through a representation of current state, desired state, a comparison process and control mechanisms. Consciousness plays an important role in all these processes: the knowledge about the internal (self) and external (environment) conditions is the key element for understanding which action (or sequence of actions) has to be performed to reach a specific goal given the current situations. This brings to light the cellular computing model that: 1. spells out the computational workflow components as a stable sequence of patterns that accomplishes a specific purpose, 2. implements a parallel management workflow with another sequence of patterns that assures the successful execution of the system’s purpose (the computing network to assure biological value with management and safekeeping), 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems 978-0-7695-4687-2/12 $26.00 © 2012 IEEE DOI 10.1109/CISIS.2012.170 867