Dynamic Runtime Software Architecture Adaptation

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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. **