Evolutionary Data Systems
Stratos Idreos, Lukas M. Maas, Mike S. Kester

TL;DR
Evolutionary data systems dynamically adapt their architectures over time to changing workloads, reducing the need for expert tuning and enabling more flexible, cost-effective data management.
Contribution
This paper introduces the concept of evolutionary data systems that automatically evolve their architecture in response to workload changes, inspired by biological evolution.
Findings
Prototype demonstrates seamless evolution between key-value and column-store architectures.
Systems can adapt architecture based on workload properties over time.
Evolutionary approach reduces manual tuning and configuration efforts.
Abstract
Anyone in need of a data system today is confronted with numerous complex options in terms of system architectures, such as traditional relational databases, NoSQL and NewSQL solutions as well as several sub-categories like column-stores, row-stores etc. This overwhelming array of choices makes bootstrapping data-driven applications difficult and time consuming, requiring expertise often not accessible due to cost issues (e.g., to scientific labs or small businesses). In this paper, we present the vision of evolutionary data systems that free systems architects and application designers from the complex, cumbersome and expensive process of designing and tuning specialized data system architectures that fit only a single, static application scenario. Setting up an evolutionary system is as simple as identifying the data. As new data and queries come in, the system automatically evolves…
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Taxonomy
TopicsAdvanced Database Systems and Queries · Evolutionary Algorithms and Applications · Algorithms and Data Compression
