HASLab - Indexed Articles in Journals

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Browsing HASLab - Indexed Articles in Journals by Author "5602"
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    Databases in Edge and Fog Environments: A Survey
    ( 2024) Luís Manuel Ferreira ; José Orlando Pereira ; Fábio André Coelho ; 7372 ; 5602 ; 6059
    While a significant number of databases are deployed in cloud environments, pushing part or all data storage and querying planes closer to their sources (i.e., to the edge) can provide advantages in latency, connectivity, privacy, energy, and scalability. This article dissects the advantages provided by databases in edge and fog environments by surveying application domains and discussing the key drivers for pushing database systems to the edge. At the same time, it also identifies the main challenges faced by developers in this new environment and analyzes the mechanisms employed to deal with them. By providing an overview of the current state of edge and fog databases, this survey provides valuable insights into future research directions.
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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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    Detailed Black-Box Monitoring of Distributed Systems
    ( 2021) Ricardo Pereira Vilaça ; 5602
    Modern containerized distributed systems, such as big data storage and processing stacks or micro-service based applications, are inherently hard to monitor and optimize, as resource usage does not directly match hardware resources due to multiple virtualization layers. For instance, inter-application traffic is an important factor in as it directly indicates how components interact, it has not been possible to accurately monitor it in an application independent way and without severe overhead, thus putting it out of reach of cloud platforms. In this paper we present an efficient black-box monitoring approach for gathering detailed structural information of collaborating processes in a distributed system that can be queried for various purposes, as it includes both information about processes, containers, and hosts, as well as resource usage and amount of data exchanged. The key to achieving high detail and low overhead without custom application instrumentation is to use a kernel-aided event driven strategy. We validate a prototype implementation by applying it to multi-platform microservice deployments, evaluate its performance with micro-benchmarks, and demonstrate its usefulness for container placement in a distributed data storage and processing stack (i.e., Cassandra and Spark).
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    MRVs: Enforcing Numeric Invariants in Parallel Updates to Hotspots with Randomized Splitting
    ( 2023) Nuno Filipe Faria ; José Orlando Pereira ; 8253 ; 5602
    Performance of transactional systems is degraded by update hotspots as conflicts lead to waiting and wasted work. This is particularly challenging in emerging large-scale database systems, as latency increases the probability of conflicts, state-of-the-art lock-based mitigations are not available, and most alternatives provide only weak consistency and cannot enforce lower bound invariants. We address this challenge with Multi-Record Values (MRVs), a technique that can be layered on existing database systems and that uses randomization to split and access numeric values in multiple records such that the probability of conflict can be made arbitrarily small. The only coordination needed is the underlying transactional system, meaning it retains existing isolation guarantees. The proposal is tested on five different systems ranging from DBx1000 (scale-up) to MySQL GR and a cloud-native NewSQL system (scale-out). The experiments explore design and configuration trade-offs and, with the TPC-C and STAMP Vacation benchmarks, demonstrate improved throughput and reduced abort rates when compared to alternatives.
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    TiQuE: Improving the Transactional Performance of Analytical Systems for True HybridWorkloads
    ( 2023) Ana Nunes Alonso ; Ricardo Pereira Vilaça ; Nuno Filipe Faria ; José Orlando Pereira ; 5692 ; 5635 ; 8253 ; 5602
    Transactions have been a key issue in database management for a long time and there are a plethora of architectures and algorithms to support and implement them. The current state-of-the-art is focused on storage management and is tightly coupled with its design, leading, for instance, to the need for completely new engines to support new features such as Hybrid Transactional Analytical Processing (HTAP). We address this challenge with a proposal to implement transactional logic in a query language such as SQL. This means that our approach can be layered on existing analytical systems but that the retrieval of a transactional snapshot and the validation of update transactions runs in the server and can take advantage of advanced query execution capabilities of an optimizing query engine. We demonstrate our proposal, TiQuE, on MonetDB and obtain an average 500x improvement in transactional throughput while retaining good performance on analytical queries, making it competitive with the state-of-the-art HTAP systems.
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    When Amnesia Strikes: Understanding and Reproducing Data Loss Bugs with Fault Injection
    ( 2024) José Orlando Pereira ; Tânia Conceição Araújo ; João Tiago Paulo ; Ricardo Gonçalves Macedo ; 5602 ; 7401 ; 5621 ; 6941
    We present LazyFS, a new fault injection tool that simplifies the debugging and reproduction of complex data durability bugs experienced by databases, key-value stores, and other data-centric systems in crashes. Our tool simulates persistence properties of POSIX file systems (e.g., operations ordering and atomicity) and enables users to inject lost and torn write faults with a precise and controlled approach. Further, it provides profiling information about the system’s operations flow and persisted data, enabling users to better understand the root cause of errors. Weuse LazyFS to study seven important systems: PostgreSQL, etcd, Zookeeper, Redis, LevelDB, PebblesDB, and Lightning Network. Our fault injection campaign shows that LazyFS automates and facilitates the reproduction of five known bug reports containing manual and complex reproducibility steps. Further, it aids in understanding and reproducing seven ambiguous bugs reported by users. Finally, LazyFS is used to find eight new bugs, which lead to data loss, corruption, and unavailability.