Publications by Diego Didona | EPFL Graph Search

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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 such platforms. TCC avoids many anomalies of eventual consistency, eschews the ...

This paper investigates the problem of deriving a white box performance model of Hardware Transactional Memory (HTM) systems. The proposed model targets TSX, a popular implementation of HTM integrated in Intel processors starting with the Haswell family in ...

Causal consistency is an attractive consistency model for geo-replicated data stores. It is provably the strongest model that tolerates network partitions. It avoids the long latencies associated with strong consistency, and, especially when using read-onl ...

Geo-replicated data platforms are the backbone of several large-scale online services. Transactional Causal Consistency (TCC) is an attractive consistency level for building such platforms. TCC avoids many anomalies of eventual consistency, eschews the syn ...

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 considered the most frequent in practice. Yet, the results obtained so far ...

The Transactional Memory (TM) paradigm promises to greatly simplify the development of concurrent applications. This led, over the years, to the creation of a plethora of TM implementations delivering wide ranges of performance across workloads. Yet, no un ...

Transactional Causal Consistency (TCC) extends causal consistency, the strongest consistency model compatible with availability, with interactive read-write transactions, and is therefore particularly appealing for geo-replicated platforms. This paper pres ...

In this paper we present a new approach to implementing causal consistency in geo-replicated data stores, which we call Optimistic Causal Consistency (OCC). The optimism in our approach lies in that updates from a remote data center are immediately made vi ...

The vast majority of data center schedulers use task runtime estimates to improve the quality of their scheduling decisions. Knowledge about runtimes allows the schedulers, among other things, to achieve better load balance and to avoid head-of-line blocki ...

We present Eagle, a new hybrid data center scheduler for data-parallel programs. Eagle dynamically divides the nodes of the data center in partitions for the execution of long and short jobs, thereby avoiding head-of-line blocking. Furthermore, it provides ...

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 achieve fast read transactions, the system has to give up support of transactions ...

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. ...

We present TRIAD, a new persistent key-value (KV) store based on Log-Structured Merge (LSM) trees. TRIAD improves LSM KV throughput by reducing the write amplification arising in the maintenance of the LSM tree structure. Although occurring in the backgrou ...

Diego Didona

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PaRiS: Causally Consistent Transactions with Non-blocking Reads and Partial Replication

An Analytical Model of Hardware Transactional Memory

Causal Consistency and Latency Optimality: Friend or Foe? [Extended Version]

PaRiS: Causally Consistent Transactions with Non-blocking Reads and Partial Replication

Distributed Transactions: Dissecting the Nightmare

ProteusTM: Abstraction Meets Performance in Transactional Memory

Wren: Nonblocking Reads in a Partitioned Transactional Causally Consistent Data Store

Optimistic Causal Consistency for Geo-Replicated Key-Value Stores

Kairos: Preemptive Data Center Scheduling Without Runtime Estimates

Job-aware Scheduling in Eagle: Divide and Stick to Your Probes

Causal Consistency and Latency Optimality: Friend or Foe?

Distributed Transactional Systems Cannot Be Fast

Size-aware Sharding For Improving Tail Latencies in In-memory Key-value Stores

TRIAD: creating synergies between memory, disk and log in log structured key-value stores