M
Marco Serafini
Researcher at University of Massachusetts Amherst
Publications - 67
Citations - 1888
Marco Serafini is an academic researcher from University of Massachusetts Amherst. The author has contributed to research in topics: Scalability & Load balancing (computing). The author has an hindex of 18, co-authored 65 publications receiving 1469 citations. Previous affiliations of Marco Serafini include Qatar Airways & Qatar Foundation.
Papers
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Proceedings ArticleDOI
Zab: High-performance broadcast for primary-backup systems
TL;DR: Zab is a crash-recovery atomic broadcast algorithm designed for the ZooKeeper coordination service that guarantees that if it delivers a given state change, then all other changes it depends upon must be delivered first.
Proceedings ArticleDOI
Arabesque: a system for distributed graph mining
Carlos H. C. Teixeira,Alexandre J. Fonseca,Marco Serafini,Georgos Siganos,Mohammed J. Zaki,Ashraf Aboulnaga +5 more
TL;DR: Arabesque is presented, the first distributed data processing platform for implementing graph mining algorithms that automates the process of exploring a very large number of subgraphs and defines a high-level filter-process computational model that simplifies the development of scalableGraph mining algorithms.
Journal ArticleDOI
E-store: fine-grained elastic partitioning for distributed transaction processing systems
Rebecca Taft,Essam Mansour,Marco Serafini,Jennie Duggan,Aaron J. Elmore,Ashraf Aboulnaga,Andrew Pavlo,Michael Stonebraker +7 more
TL;DR: E-Store is presented, an elastic partitioning framework for distributed OLTP DBMSs that automatically scales resources in response to demand spikes, periodic events, and gradual changes in an application's workload.
Proceedings ArticleDOI
The Power of Both Choices: Practical Load Balancing for Distributed Stream Processing Engines
Muhammad Anis Uddin Nasir,Gianmarco De Francisci Morales,David García-Soriano,Nicolas Kourtellis,Marco Serafini +4 more
TL;DR: In this article, partial key grouping (PKG) is proposed to adapt the classical power of two choices to a distributed streaming setting by leveraging two novel techniques: key splitting and local load estimation.
Proceedings ArticleDOI
The power of both choices: Practical load balancing for distributed stream processing engines
Muhammad Anis Uddin Nasir,Gianmarco De Francisci Morales,David García-Soriano,Nicolas Kourtellis,Marco Serafini +4 more
TL;DR: Partial Key Grouping (PKG), a new stream partitioning scheme that adapts the classical “power of two choices” to a distributed streaming setting by leveraging two novel techniques: key splitting and local load estimation, achieves better load balancing than key grouping while being more scalable than shuffle grouping.