HASLab

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http://localhost:4000/handle/123456789/30
This service produces reliable software systems in contexts where correctness, responsiveness, robustness and security are essential. It develops integrated research in three lines: formal methods for software development, reliable distributed systems and information security.

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Browsing HASLab by Author "5594"
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    CAT: content-aware tracing and analysis for distributed systems
    ( 2021) Rui Carlos Oliveira ; João Tiago Paulo ; Francisco Teixeira Neves ; Tânia Conceição Araújo ; 5594 ; 5621 ; 6125 ; 7401
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    Decentralized Privacy-Preserving Proximity Tracing
    ( 2020) Binns,R ; Barrat,A ; Fiore,D ; Manuel Barbosa ; Rui Carlos Oliveira ; José Orlando Pereira ; Basin,DA ; Beutel,J ; Jackson,D ; Roeschlin,M ; Leu,P ; Preneel,B ; Smart,NP ; Abidin,A ; Gürses,SF ; Veale,M ; Cremers,C ; Backes,M ; Tippenhauer,NO ; Cattuto,C ; Troncoso,C ; Payer,M ; Hubaux,JP ; Salathé,M ; Larus,JR ; Bugnion,E ; Lueks,W ; Stadler,T ; Pyrgelis,A ; Antonioli,D ; Barman,L ; Chatel,S ; Paterson,KG ; Capkun,S ; 5602 ; 5604 ; 5594
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    Diagnosing applications' I/O behavior through system call observability
    ( 2023) João Tiago Paulo ; Ricardo Gonçalves Macedo ; Tânia Conceição Araújo ; Rui Carlos Oliveira ; 5621 ; 6941 ; 7401 ; 5594
    We present DIO, a generic tool for observing inefficient and erroneous I/O interactions between applications and in-kernel storage systems that lead to performance, dependability, and correctness issues. DIO facilitates the analysis and enables near real-time visualization of complex I/O patterns for data-intensive applications generating millions of storage requests. This is achieved by non-intrusively intercepting system calls, enriching collected data with relevant context, and providing timely analysis and visualization for traced events. We demonstrate its usefulness by analyzing two production-level applications. Results show that DIO enables diagnosing resource contention in multi-threaded I/O that leads to high tail latency and erroneous file accesses that cause data loss.
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    Evaluating Cassandra as a manager of large file sets
    ( 2013) Gomes,P ; 5635
    All companies developing their business on the Web, not only giants like Google or Facebook but also small companies focused on niche markets, face scalability issues in data management. The case study of this paper is the content management systems for classified or commercial advertisements on the Web. The data involved has a very significant growth rate and a read-intensive access pattern with a reduced update rate. Typically, data is stored in traditional file systems hosted on dedicated servers or Storage Area Network devices due to the generalization and ease of use of file systems. However, this ease in implementation and usage has a disadvantage: the centralized nature of these systems leads to availability, elasticity and scalability problems. The scenario under study, undemanding in terms of the system's consistency and with a simple interaction model, is suitable to a distributed database, such as Cassandra, conceived precisely to dynamically handle large volumes of data. In this paper, we analyze the suitability of Cassandra as a substitute for file systems in content management systems. The evaluation, conducted using real data from a production system, shows that when using Cassandra, one can easily get horizontal scalability of storage, redundancy across multiple independent nodes and load distribution imposed by the periodic activities of safeguarding data, while ensuring a comparable performance to that of a file system. Copyright © 2013 ACM.
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    SOTERIA: Preserving Privacy in Distributed Machine Learning
    ( 2023) Cláudia Vanessa Brito ; Rui Carlos Oliveira ; 7516 ; 5594
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    Toward a Practical and Timely Diagnosis of Application's I/O Behavior
    ( 2023) Ricardo Gonçalves Macedo ; Tânia Conceição Araújo ; Rui Carlos Oliveira ; João Tiago Paulo ; 6941 ; 7401 ; 5594 ; 5621
    We present DIO, a generic tool for observing inefficient and erroneous I/O interactions between applications and in-kernel storage backends that lead to performance, dependability, and correctness issues. DIO eases the analysis and enables near real-time visualization of complex I/O patterns for data-intensive applications generating millions of storage requests. This is achieved by non-intrusively intercepting system calls, enriching collected data with relevant context, and providing timely analysis and visualization for traced events. We demonstrate its usefulness by analyzing four production-level applications. Results show that DIO enables diagnosing inefficient I/O patterns that lead to poor application performance, unexpected and redundant I/O calls caused by high-level libraries, resource contention in multithreaded I/O that leads to high tail latency, and erroneous file accesses that cause data loss. Moreover, through a detailed evaluation, we show that, when comparing DIO's inline diagnosis pipeline with a similar state-of-the-art solution, our system captures up to 28x more events while keeping tracing performance overhead between 14% and 51%.