Quality Attribute Concerns for Using Microservices at the Edge

Bringing computation and data storage closer to the edge, such as disaster and tactical environments, has challenging quality attribute requirements. These include improving response time, saving bandwidth, and implementing security in resource-constrained nodes.

In this webcast we review characteristics of edge environments with a focus on architectural qualities. The characteristics and quality attribute concerns that we present are generalized from and informed by multiple customer experiences that we have undertaken in recent years.

We present an overview of edge environments, in both military and civilian contexts, and provide a discussion about edge-specific challenges and how they can differ based on the context. We discuss architectural quality attributes that are well suited to address the edge-specific challenges, and provide examples of how each apply. A microservices architecture provides an opportunity to address several of the quality attribute concerns at the edge. Through a final consolidated scenario as an exemplar, we discuss how the presented qualities can be addressed using microservices.

This webcast should be useful for anyone interested in better understanding the challenges of edge environments and learning about representative scenarios of work currently being done.

Speaker and Presenter Information

Marc Novakouski is a Senior Engineer at the Software Engineering Institute at Carnegie Mellon University. He currently is part of the Tactical and AI-Enabled Systems Initiative. Novakouski has more than 20 years of professional software development experience spanning defense, commercial, and academic fields. He has expertise across a wide set of technical domains, covering all phases of the software development lifecycle, and a variety of current and legacy platforms, processes, tools, and techniques. His current areas of expertise include distributed computing, mobile platforms and computing, context awareness and processing, and hardware/software integration. Previous focuses included service-oriented architecture (SOA), identity management, e-Government, data interoperability, cloud computing, and certification.

Grace Lewis is principal researcher and lead of the Tactical and AI-Enabled Systems (TAS) initiative at the Software Engineering Institute at Carnegie Mellon University. Lewis is the principal investigator for the Characterizing and Detecting Mismatch in ML-Enabled Systems research project. She is also a member of the High Assurance Software-Defined IoT Security research project and leads the SEI’s work in tactical cloudlets. Lewis’s current areas of expertise and interest include software engineering for AI/ML systems, IoT security, edge computing, software architecture (in particular the development of software architecture practices for systems that integrate emerging technologies), and software engineering in society.