Dynamic Runtime Software Architecture Adaptation

随着社会计算基础设施的普及，数据不断产生、获取和分析的方式已经发生了范式转变。据报道，世界上90%的数据是在过去两年中产生的。预测表明，在未来几年内，数据增长率本身将呈指数级增长。数据量的空前增长给处理如此大量数据的软件平台带来了令人兴奋的新挑战。从软件开发的角度来看，软件应用程序需要表现出理想的特性，如可伸缩性、弹性和可靠性，以满足与数据量和数据增长率相关的最低要求。虽然社区已经认识到对创新软件平台的迫切需求，但越来越多的解决方案并没有解决软件架构层的可扩展性、弹性和可靠性问题，而是通过数据分区、数据复制和分布模型（如MapReduce）等技术将这些重要问题转移到数据管理层；因此，将数据管理问题与应用程序可伸缩性和弹性纠缠在一起。***这个关于软件平台运行时重构和架构适应的研究计划将开发架构模型、算法、技术和工具，以有效地处理和解决与数据的新特征（如体积、种类、速度和准确性）相关的挑战。主要目标将是开发动态运行时可重构和自适应的软件体系结构模型，这些模型将自动响应和适应不同的数据生产和消费模式。我们在这个研究项目中的具体目标是：a)通过开发模型验证和验证技术来实现实时架构适应，这些技术可以在没有事先规则设置的情况下自动评估架构替代方案；B)通过对采样的低开销应用程序分析数据进行分析来提供可伸缩性和弹性，这些数据将指向架构级应用程序分区或合并需求；C)支持运行时成本有效的软件架构回滚到已经配置好的和稳定的架构检查点，以便主动防止或被动地解决不希望出现的质量或功能状态；d)通过建模软件应用程序的历史行为来实现预测适应性可靠性评估，因为它们与可测量的软件架构度量相关。***该计划为HQP培训提供了坚实的基础，包括2名博士和4名MASc学生，他们将获得最先进的软件工程，自适应系统和大数据应用方面的专业知识。**

There has been a paradigm shift in the way data is constantly produced, procured and analyzed specially with the prevalence of social computing infrastructure. It has already been reported that 90% of all the data in the world has been generated over the last two years. Predictions indicate that the data growth rate itself will be exponential in the next few years. The unprecedented growth of data volumes has introduced exciting new challenges for the software platforms that deal with such voluminous amounts of data. ***From a software development perspective, software applications need to exhibit desirable characteristics such as scalability, elasticity, and reliability in order to meet the minimum requirements associated with data volume and data growth rates. While the community has acknowledged the pressing need for innovative software platforms, a growing number of solutions do not address issues of scalability, elasticity and reliability at the software architecture layer, offloading these important considerations onto the data management layer through techniques such as data partitioning, data replication and distribution models such as MapReduce; therefore, entangling issues of data management with application scalability and elasticity. ***This proposed research program on runtime reconfiguration and architectural adaptation of software platforms will develop architectural models, algorithms, techniques and tools that will efficiently handle and address challenges associated with the novel characteristics of data such as volume, variety, velocity and veracity. The main objective will be to develop dynamic runtime reconfigurable and adaptive software architectural models that would automatically react and adapt to varying data production and consumption patterns.***The concrete objectives of our work in this research program will be to: a) enable real-time architecture adaptation by developing model verification and validation techniques that automatically evaluate architectural alternatives without prior rule setting; b) provide scalability and elasticity by performing analysis on sampled low-overhead application profiling data that would point to architecture-level application partitioning or merging requirements; c) support runtime cost-efficient rollback of software architecture into already profiled and stable architecture checkpoints in order to proactively prevent or reactively resolve an undesirable quality or functional state; and d) enable predictive adaptation reliability estimation through modeling historical behavior of software applications as they correlate with measurable software architecture metrics.***The proposed program provides a solid foundation for HQP training including 2 PhD and 4 MASc students who will gain expertise in state-of-the-art software engineering, self-adaptive systems, and big data applications. **