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204 results on '"Daniel Hagimont"'

31. A Remote Memory Sharing System for Virtualized Computing Infrastructures

Author

Hrachya Astsatryan, Daniel Hagimont, Giang Son Tran, Aram Kocharyan, Boris Teabe, and Brice Ekane

Subjects
Computer Networks and Communications, Hardware and Architecture, business.industry, Computer science, Operating system, Cloud computing, Remote memory, business, computer.software_genre, computer, Software, Computer Science Applications, Information Systems
Published
2022

32. Networking in next generation disaggregated datacenters

Author

Brice Ekane, Alain Tchana, Daniel Hagimont, Boris Teabe, and Noel De Palma

Subjects
Computational Theory and Mathematics, Computer Networks and Communications, Software, Computer Science Applications, Theoretical Computer Science
Published
2023

35. FlexVF: Adaptive network device services in a virtualized environment

Author

Boris Teabe, Brice Ekane, Tu Dinh Ngoc, Noel De Palma, and Daniel Hagimont

Subjects
Software_OPERATINGSYSTEMS, Computer Networks and Communications, Computer science, Hypervisor, Virtualization, computer.software_genre, Networking hardware, Protocol stack, Hardware and Architecture, Virtual machine, Operating system, Overhead (computing), Network performance, Host (network), computer, Software
Abstract

Single-root I/O virtualization (SR-IOV) allows virtual machines direct access to physical network cards through so-called virtual functions (VFs), considerably reducing networking overhead compared to paravirtualized interfaces by avoiding the hypervisor’s network stack. However, the maximum number of VFs on each card is often limited compared to the number of VMs running on each host, leading to the problem of choosing which VMs to allocate these VFs. In this paper, we introduce FlexVF, a mechanism for dynamically allocating and switching between VFs and paravirtualized networking on VMs based on network activity monitoring. We show that FlexVF improves VM network performance by 75% without affecting network operation.

Published
2022

39. Performance-efficient Recommendation and Prediction Service for Big Data frameworks focusing on Data Compression and In-memory Data Storage Indicators

Author

Daniel Hagimont, Aram Kocharyan, Arthur Lalayan, and Hrachya Astsatryan

Subjects
General Computer Science
Abstract

The MapReduce framework manages Big Data sets by splitting the large datasets into a set of distributed blocks and processes them in parallel. Data compression and in-memory file systems are widely used methods in Big Data processing to reduce resource-intensive I/O operations and improve I/O rate correspondingly. The article presents a performance-efficient modular and configurable decision-making robust service relying on data compression and in-memory data storage indicators. The service consists of Recommendation and Prediction modules, predicts the execution time of a given job based on metrics, and recommends the best configuration parameters to improve Hadoop and Spark frameworks' performance. Several CPU and data-intensive applications and micro-benchmarks have been evaluated to improve the performance, including Log Analyzer, WordCount, and K-Means.

Published
2021

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