Manos Athanassoulis

About

Manos Athanassoulis is an Associate Professor of Computer Science at Boston University, where he is the Director and Founder of the BU Data-intensive Systems and Computing (DiSC) lab and a member of the BU MiDAS group. His research focuses on data systems, specifically novel cloud data management architectures, complex hybrid transactional/analytical workloads, and new hardware technologies such as non-volatile memories and heterogeneous computation units. Prior to joining Boston University, he was a Research Associate and Postdoctoral Researcher at Harvard University in the Data Systems Lab, supported by a SNSF Postdoc Mobility Fellowship. He earned his PhD in 2014 from EPFL, where he worked on data systems architectures for analytics exploiting new storage hardware under the guidance of Anastasia Ailamaki. He also holds an MSc in Computer Systems Technology and a BSc in Informatics and Telecommunications from the University of Athens, Greece. Additionally, he has experience as a PhD Intern at IBM Research and as a junior researcher at the University of Athens and the National Technical University of Athens. Outside of his academic work, he enjoys biking, playing basketball, and studying history.

Research topics

• Computer Science
• Operating system
• Distributed computing
• Telecommunications
• Data Mining
• Artificial Intelligence
• Parallel computing
• Theoretical computer science
• Database
• Data science

Selected publications

• Lethe: A Tunable Delete-Aware LSM Engine

  2020 · 60 citations

  Senior authorCorresponding

  Data-intensive applications fueled the evolution of log structured merge (LSM) based key-value engines that employ the out-of-place paradigm to support high ingestion rates with low read/write interference. These benefits, however, come at the cost of treating deletes as a second-class citizen. A delete inserts a tombstone that invalidates older instances of the deleted key. State-of-the-art LSM engines do not provide guarantees as to how fast a tombstone will propagate to persist the deletion.…

  DOI

• Constructing and analyzing the LSM compaction design space

  Proceedings of the VLDB Endowment · 2021 · 44 citations

  Senior authorCorresponding

  Log-structured merge (LSM) trees offer efficient ingestion by appending incoming data, and thus, are widely used as the storage layer of production NoSQL data stores. To enable competitive read performance, LSM-trees periodically re-organize data to form a tree with levels of exponentially increasing capacity, through iterative compactions. Compactions fundamentally influence the performance of an LSM-engine in terms of write amplification, write throughput, point and range lookup performance, s…

  DOI

• Dissecting, Designing, and Optimizing LSM-based Data Stores

  Proceedings of the 2022 International Conference on Management of Data · 2022 · 23 citations

  Senior authorCorresponding

  Log-structured merge (LSM) trees have emerged as one of the most commonly used disk-based data structures in modern data systems. LSM-trees employ out-of-place ingestion to support high throughput for writes, while their immutable file structure allows for good utilization of disk space. Thus, the log-structured paradigm has been widely adopted in state-of-the-art NoSQL, relational, spatial, and time-series data systems. However, despite their popularity, there is a lack of pedagogical textbook-…

  DOI

• The LSM Design Space and its Read Optimizations

  2023-04-01 · 17 citations

  articleSenior author

  Log-structured merge (LSM) trees have emerged as one of the most commonly used storage-based data structures in modern data systems as they offer high throughput for writes and good utilization of storage space. However, LSM-trees were not originally designed to facilitate efficient reads. Thus, state-of-the-art LSM engines employ numerous optimization techniques to make reads efficient. The goal of this tutorial is to present the fundamental principles of the LSM paradigm along with the various…

  PublisherDOI

• Endure

  Proceedings of the VLDB Endowment · 2022 · 17 citations

  Senior authorCorresponding

  Log-Structured Merge trees (LSM trees) are increasingly used as the storage engines behind several data systems, frequently deployed in the cloud. Similar to other database architectures, LSM trees consider information about the expected workload (e.g., reads vs. writes, point vs. range queries) to optimize their performance via tuning. However, operating in a shared infrastructure like the cloud comes with workload uncertainty due to the fast-evolving nature of modern applications. Systems with…

  DOI

Recent grants

• CAREER: Robust LSM-Based Data Stores

  NSF · $600k · 2022–2027

• CRII: III: Optimal Data Organization for Hybrid Transactional/Analytical Processing Data Systems

  NSF · $175k · 2019–2022

Frequent coauthors

• Anastasia Ailamaki

  18 shared

• Subhadeep Sarkar

  Brandeis University

  13 shared

• Tarikul Islam Papon

  Boston University

  13 shared

• Stratos Idreos

  13 shared

• Radu Stoica

  IBM Research - Zurich

  10 shared

• Ju Hyoung Mun

  7 shared

• Zichen Zhu

  7 shared

• Ryan Johnson

  Databricks (United States)

  7 shared

Labs

• DiSC labPI

Education

• B.S., Informatics and Telecommunications

  University of Athens, Greece

  2005

• M.S., Computer Systems Technology

  University of Athens, Greece

  2008

• Ph.D.

  École Polytechnique Fédérale de Lausanne

  2014

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