Smart Distributed Software and Systems

当前和新兴的许多类型的复杂系统--包括大数据系统、云数据基础设施、物联网和网络--物理系统越来越分布式和动态的体系结构，在创建和支持应用程序方面提供了前所未有的灵活性。然而，这种高度分布式的体系结构也增加了管理性能、配置、维护和安全性的复杂性。软件定义环境的出现和机器智能的最新进展使分布式软件系统能够在符合最终用户目标的同时进行自我管理。这项拟议的研究推动了自主计算的理论、实践和应用的前沿，使自适应系统足够智能，以确保它们在真实世界的场景中正常运行。 自主管理系统(AMS)是在自适应系统中提供自我管理的组件。我们将利用白盒建模(即排队论和控制论)和黑盒建模(即机器学习模型)为目标分布式系统构建高效可靠的AMSS。白盒模型反映了系统的本质，而黑盒模型估计了白盒模型未能捕捉到的运行时不确定性和未知参数。尽管这种混合方法是一个很有前途的研究方向，可能会导致自适应系统在现实世界中的广泛使用，但需要同时解决以下挑战： 1)大型分布式系统需要分散管理，这增加了多个AMS之间的协调需求。解决多个军事管理机构之间的冲突并确定它们之间的关系已成为这一领域的明显挑战。 2)到目前为止，AMSS一直专注于变革管理，而不是目标管理。目标可能会随着持续的人为驱动的开发、集成和交付过程而改变，也就是软件进化，这需要纳入AMSS的体系结构中。 3)虽然不能实现最佳配置，但可接受的配置是可行的，但实现起来具有挑战性。需要设计能够使任何分布式系统的可接受配置和部署成为现实的AMSS。 总而言之，这项研究计划1)促进了自主计算的显著进步，2)培训了具有正确和及时的专业知识的熟练的HQP，3)为自适应系统在加拿大和国外的健康、能源和城市管理中的广泛使用铺平了道路。这种智能自适应系统同时提高了上述部门提供的服务的效率、准确性、可靠性和可用性。因此，这一研究计划不仅将影响学术研究，还将影响最终导致社会改善的行业。

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.