Grant: Discovery Grants Program - Individual

Current and emerging complex systems of many types--including big data systems, cloud data infrastructure, Internet of Things and cyber-physical systems have increasingly distributed and dynamic architecture that provide unprecedented flexibility in creating and supporting applications. However, such highly distributed architectures also increase the complexity of managing performance, configuration, maintenance and security. The emergence of software-defined environments and recent progress in machine intelligence make it possible to enable distributed software systems to self-manage while conforming to the end-users' objectives. The proposed research pushes the frontiers of the theory, practice, and applications of autonomic computing to make self-adaptive systems smart enough to guarantee their proper functioning in real-world scenarios.The autonomic management system (AMS) is the component that provides self-management in self-adaptive systems. We will leverage white-box modeling (i.e., queuing and control theories) and black-box modeling (i.e., machine learning models) to build efficient and reliable AMSs for target distributed systems. The white-box models capture the essence of the system while the black-box models estimate the runtime uncertainties and unknown parameters that white-box models fail to capture. Although this hybrid approach is a promising research direction that may lead to widespread use of self-adaptive systems in real world, the following challenges need to be addressed in parallel:1) Large-scale distributed systems require decentralized management which raises the need for coordination among multiple AMSs. Resolving the conflicts among multiple AMSs and defining relationships among them have emerged as evident challenges in this area. 2) So far, AMSs have been concentrated on change management rather than goal management. Goals may change by the continuous human-driven process of development, integration and delivery, aka software evolution, which needs to be incorporated in the architecture of AMSs. 3) While optimal configuration is not attainable, acceptable configuration is feasible but challenging to achieve. There is a need to design AMSs that can make acceptable configurations and deployment a reality for any distributed system. In summary, this research program 1) contributes a notable advancement in autonomic computing, 2) trains skilful HQPs with rightful and timely expertise and 3) paves the path for widespread use of self-adaptive systems in management of health, energy and cities here in Canada and abroad. Such smart self-adaptive systems improve the efficiency, accuracy, reliability and availability of services offered in the above-mentioned sectors simultaneously. Therefore, this research program will impact not only academic research but also industries that ultimately lead to the betterment of society.