Next Generation Real-Time Methods for the Supervision and Contol of Industrial Environments

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The objectives of this research application are to investigate selected theoretical and experimental topics related to the design and implementation of specific components of the next-generation of Information Technology Infrastructures for supervisory control of enterprise processes. It is common knowledge that Information and Communication Technologies are the central piece of the new digital economy due to the commoditization of computer and networking technologies. Recently, the World Economic Forum predicted that the new digital economy will be the major industrial component in any developed country. More and more, applications and data are migrating away from customer premises and into cloud environments. Reports show that an average Cloud Computing infrastructure consumes the same amount of energy like 25,000 households. The advancements made in past years in the area of distributed and cloud computing have led to the production of massive software technologies, hosted by large and extremely costly data centres, while the “green computing” movement promotes improving cloud efficiency in regards to the energy consumed. Despite sustained research efforts, a formal model of the dynamics of the energy consumption in Cloud Computing infrastructures, due to the computing, storage and networking resource utilization, is still lacking. We propose to investigate the modeling of resource allocation processes in Cloud Computing by Stochastic Differential Equations systems. This new stochastic model will be devised by analyzing large amounts of non-stationary cloud data for model identification. Because the manual effort needed to control a growing cloud computing infrastructure is extremely high and inefficient, making the Cloud unmanageable, we will devise an Autonomic Computing environment, protocols and control algorithms based on the new Stochastic Differential model to control cloud resources. This system will implement self-optimization, self-provisioning, and self-learning algorithms, which mimic human operator decisions and interactions with the cloud. The Autonomic Computing for cloud resource control will manage optimally the cloud resources in real-time reducing the computational power and the energy consumption. We propose to investigate the devising of complex Autonomic Computing system implementing intelligent, local and global controllers for clouds computing self-governance. We will also produce adaptive algorithms for the cloud controller to implement self-organizing strategies in order to optimize virtual servers’ usage. A self-awareness feature will provide information about the state of resources and the resources it links to. A light prototype will be implemented on the cloud hosted in the applicant’s laboratory. Recent advances on Software Defined Networking and Software Defined Services will be used for prototype implementation.