Cost-, Energy-, and Performance-Aware Cloud Optimization
Performance and Scalability of Distributed Applications
Distributed Databases and Data Management
Serverless and Data-Intensive Systems
The Cloud Optimization Working Group focuses on designing and deploying cloud, edge, and hybrid infrastructures that efficiently use compute, memory, storage, and network resources. We study resource placement, autoscaling, and dynamic management of applications, data streams, and distributed databases, emphasizing cost, energy, and performance trade-offs for data-intensive and real-time workloads. Our research spans distributed systems, real-time data processing, serverless computing, and geo-distributed databases, aiming to provide predictive and adaptive mechanisms for parallelism, data locality, and load balancing. We combine theoretical modelling, experimental evaluation on real cloud platforms, and collaborations with industry to produce open-source prototypes, reproducible benchmarks, and actionable guidelines.
The complexity of database systems has increased alongside the exponential growth of data, necessitating Information Systems (IS) architects to continuously refine data models and meticulously select storage and management options that align with requirements. While existing solutions focus on data model transformation, none offer guidance in selecting the most suitable data model for a given use case. In this context, we propose DaMoOp, an automated approach for leading data model selection process. DaMoOp starts from a conceptual model and associated use case comprising queries, settings and infrastructure constraints, to generate relevant logical data models. A cost model, considering environmental, financial, and temporal factors, facilitates comparison and selection of the most suitable data model. Our cost model incorporates both data model and queries costs. Additionally, we suggest a data model selection process that enhances the ability to choose the optimal data model(s) for a specific use case, while also adapting to rapidly evolving use cases. We provide a strategic optimization approach designed to identify the most cost-efficient and stable data model as use case scenarios evolve. Moreover, we offer a simulation tool for the entire process, which enables visualizing the impact of use case variations on data model costs, thus empowering IS architects to make informed decisions.
