43 citations · 71 across the 9 of their papers we have counts for
13 papers
Evaluating the accuracy of KV cache reuse techniques
Samuel Cestola, Tianxiang Xia, Pengfei Zheng +4
Position-independent KV cache reuse aims to reduce latency in retrieval-augmented generation by reusing chunk-level KV caches across prompts. We show that current evaluations of KV…
An experimental study of KV cache reuse strategies in chunk-level caching systems
Samuel Cestola, Tianxiang Xia, Zheng Weiyan +2
Retrieval-augmented generation improves large language models' accuracy by adding relevant retrieved text to the prompt. Chunk level caching (CLC) accelerates inference by precompu…
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…
Causal Consistency and Latency Optimality: Friend or Foe?
Diego Didona, Rachid Guerraoui, Jingjing Wang +1
Causal consistency is an attractive consistency model for replicated data stores. It is provably the strongest model that tolerates partitions, it avoids the long latencies associa…
Size-aware Sharding For Improving Tail Latencies in In-memory Key-value Stores
Diego Didona, Willy Zwaenepoel
This paper introduces the concept of size-aware sharding to improve tail latencies for in-memory key-value stores, and describes its implementation in the Minos key-value store. Ta…
Okapi: Causally Consistent Geo-Replication Made Faster, Cheaper and More Available
Diego Didona, Kristina Spirovska, Willy Zwaenepoel
Okapi is a new causally consistent geo-replicated key- value store. Okapi leverages two key design choices to achieve high performance. First, it relies on hybrid logical/physical…
A Flexible Framework for Accurate Simulation of Cloud In-Memory Data Stores
Pierangelo Di Sanzo, Francesco Quaglia, Bruno Ciciani +5
In-memory (transactional) data stores are recognized as a first-class data management technology for cloud platforms, thanks to their ability to match the elasticity requirements i…
On Bootstrapping Machine Learning Performance Predictors via Analytical Models
Diego Didona, Paolo Romano
Performance modeling typically relies on two antithetic methodologies: white box models, which exploit knowledge on system's internals and capture its dynamics using analytical app…