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Quality Attribute Concerns for Microservices at the Edge

In this webcast, Marc Novakouski and Grace Lewis reviewed characteristics of edge environments with a focus on architectural qualities.

Software Engineering Institute



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.

About the Speaker

Marc Novakouski

Marc Novakouski

Marc Novakouski is a Principal Engineer at the Software Engineering Institute at Carnegie Mellon University. He currently is part of the Tactical and AI-enabled Systems Initiative. Novakouski has over 20 years of professional software development experience, spanning defense, commercial, and academic fields. He has expertise across a wide set of …

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Grace Lewis

Grace Lewis

Grace Lewis is a Principal Researcher and the lead for the Tactical and AI-Enabled Systems (TAS) Initiative at the Carnegie Mellon Software Engineering Institute (SEI). She is a Principal Investigator for two projects in the growing field of software engineering for machine-learning (ML) systems: “Characterizing and Detecting Mismatch in ML-Enabled …

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