Abstract—The growing heterogeneity and decentralization in the modern computing paradigm of edge-cloud continuum introduces new constraints on storage systems, such as storage type, associated processors, privacy, scarce resources, compliance, GDPR and geographical restrictions. While existing distributed data and object stores can ensure data availability and fault tolerance, they are not flexible or dynamic enough to address these diverse set of constraints. In this paper, we introduce a modular policy-driven data placement framework, CATER, designed to seamlessly integrate with existing storage systems and overcome the aforementioned limitations. CATER formulates the data placement problem as an optimization model, incorporating data collocation and hardware constraints. We integrated a prototype of CATER with Apache Ozone and conducted experiments and simulations. Results show a 23% improvement in data placement while respecting 100% of the constraints.
https://zenodo.org/records/11128962
