12 citations · 13 across the 3 of their papers we have counts for
9 papers
Towards a General Framework for ML-based Self-tuning Databases
Thomas Schmied, Diego Didona, Andreas Döring +2
Machine learning (ML) methods have recently emerged as an effective way to perform automated parameter tuning of databases. State-of-the-art approaches include Bayesian optimizatio…
Toward a Better Understanding and Evaluation of Tree Structures on Flash SSDs
Diego Didona, Nikolas Ioannou, Radu Stoica +1
Solid-state drives (SSDs) are extensively used to deploy persistent data stores, as they provide low latency random access, high write throughput, high data density, and low cost.…
Lynceus: Cost-efficient Tuning and Provisioning of Data Analytic Jobs
Maria Casimiro, Diego Didona, Paolo Romano +3
Modern data analytic and machine learning jobs find in the cloud a natural deployment platform to satisfy their notoriously large resource requirements. Yet, to achieve cost effici…
Distributed Transactional Systems Cannot Be Fast
Diego Didona, Panagiota Fatourou, Rachid Guerraoui +2
We prove that no fully transactional system can provide fast read transactions (including read-only ones that are considered the most frequent in practice). Specifically, to achiev…
PaRiS: Causally Consistent Transactions with Non-blocking Reads and Partial Replication
Kristina Spirovska, Diego Didona, Willy Zwaenepoel
Geo-replicated data platforms are at the backbone of several large-scale online services. Transactional Causal Consistency (TCC) is an attractive consistency level for building suc…
Distributed Transactions: Dissecting the Nightmare
Diego Didona, Rachid Guerraoui, Jingjing Wang +1
Many distributed storage systems are transactional and a lot of work has been devoted to optimizing their performance, especially the performance of read-only transactions that are…