GSAS 2019

Otras charlas de Global Software Architecture Summit 2019 también en podcast: https://lk.autentia.com/GSAS19-Podcast Debate moderated by Ávaro García. Some of the topics and questions we want to tackle during this panel discussion are: Silver Bullets: What are they? Have we seen one in the past 10-15 years? Do you expect to see one? Many good ideas that seem to be good on paper, but turn out to be the opposite. How to detect them early on? Are we trending towards more common principles? More specialized? Is architecture becoming more specific or more generalized? Is it really evolving? How to make this evolution happen? What kind of evolution are we seeing here? Natural selection or Human selection?

Otras charlas de Global Software Architecture Summit 2019 también en podcast: https://lk.autentia.com/GSAS19-Podcast Debate moderated by Jakub Marchwicki. Some of the topics and questions we want to tackle during this debate are: What is "reactive architecture"? Why is it so important today, yet not as widely applied despite being so adamantly advocated by software architects? What are its benefits? And tradeoffs? What are some of its core patterns? What are the consequences of some of these patterns? Are there cases where they shouldn't be used? What are the differences between reactive architecture and reactive programming?

Otras charlas de Global Software Architecture Summit 2019 también en podcast: https://lk.autentia.com/GSAS19-Podcast Debate moderated by Álvaro García. Some of the topics and questions we want to tackle during this debate are: Effectiveness vs Efficiency How to measure the effectiveness & efficiency of certain practices? How can we generate our KPIs? How can we measure waste? Comparison between the Architecture Tradeoff Analysis Method (ATAM) and the Scenario Based Analysis Method Evolution of a software architecture

Otras charlas de Global Software Architecture Summit 2019 también en podcast: https://lk.autentia.com/GSAS19-Podcast An advantage of evolutionary architectures is their facilitation of experimentation and data-driven development. This becomes especially important for cloud-deployed applications as they scale to meet greater loads, while continually balancing costs. Commercial clouds offer a myriad of powerful, competing services that can facilitate scalability through incremental change. However, the performance and scalability characteristics of these services are a priori unknown. They require careful evaluation and testing to ensure they deliver the required quality of service within application budgets. In this talk, we’ll describe the results from a set of experiments on cloud-based micro-benchmarks that explicitly compare cloud services behavior under experimental conditions. The results show how variability in microservice programming language, database platform, database model and runtime configuration settings dramatically effect scalability and associated costs. The results from these experiments can help architects reason about the effects of their architectural decisions and cloud services selections and configurations. We’ll conclude by discussing the implications of this work for evolutionary architecture, and our progress on publishing these results online for architects to access and utilize.

Otras charlas de Global Software Architecture Summit 2019 también en podcast: https://lk.autentia.com/GSAS19-Podcast We used to build software like bridges. There was time for careful collection of requirements and the analysis of design options. But we don’t do that anymore, in part because the time we spent did not ensure we hit our targets or avoided risks. Today, most companies use some form of continuous design where the software changes in small steps, often weekly or even daily, which makes those companies responsive to environmental changes. The problem is that in just a few years the software becomes over-complicated and usually needs to be rewritten, even when the team refactors. The longer you let the team code, the worse the code gets. That’s a shame because other kinds of engineering don’t have this problem. Car engines, for example, improve every year. I’ll primarily discuss some ideas about why things work out this way and also some promising ideas for how we can practice continuous design longer, if not forever.