References of "Almeida, Eduardo Cunha"
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See detailDatabase Processing-in-Memory: A Vision
Kepe, Tiago rodrigo; Almeida, Eduardo Cunha; Alves, Marcos A.Z. et al

in Database Processing-in-Memory: A Vision (2019, August 03)

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See detailAn Elastic Multi-Core Allocation Mechanism for Database Systems
Dominico, Simone; Almeida, Eduardo Cunha; Meira, Jorge Augusto UL et al

in International Conference on Data Engineering (ICDE) (2018)

During the parallel execution of queries in Non-Uniform Memory Access (NUMA) systems, he Operating System (OS) maps the threads (or processes) from modern database systems to the available cores among the ... [more ▼]

During the parallel execution of queries in Non-Uniform Memory Access (NUMA) systems, he Operating System (OS) maps the threads (or processes) from modern database systems to the available cores among the NUMA nodes using the standard node-local policy. However, such non-smart mapping may result in inefficient memory activity, because shared data may be accessed by scattered threads requiring large data movements or non-shared data may be allocated to threads sharing the same cache memory, increasing its conflicts. In this paper we present a data-distribution aware and elastic multi-core allocation mechanism to improve the OS mapping of database threads in NUMA systems. Our hypothesis is that we mitigate the data movement if we only hand out to the OS the local optimum number of cores in specific nodes. We propose a mechanism based on a rule-condition-action pipeline that uses hardware counters to promptly find out the local optimum number of cores. Our mechanism uses a priority queue to track the history of the memory address space used by database threads in order to decide about the allocation/release of cores and its distribution among the NUMA nodes to decrease remote memory access. We implemented and tested a prototype of our mechanism when executing two popular Volcano-style databases improving their NUMA-affinity. For MonetDB, we show maximum speedup of 1.53 × , due to consistent reduction in the local/remote per-query data traffic ratio of up to 3.87 × running 256 concurrent clients in the 1 GB TPC-H database also showing system energy savings of 26.05%. For the NUMA-aware SQL Server, we observed speedup of up to 1.27 × and reduction on the data traffic ratio of 3.70 ×. [less ▲]

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See detailMind Your Dependencies for Semantic Query Optimization
Pena, Eduardo H. M.; Falk, Eric UL; Meira, Jorge Augusto UL et al

in Journal of Information and Data Management (2018)

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See detailPeer-to-Peer Load Testing
Meira, Jorge Augusto UL; Almeida, Eduardo Cunha; Le Traon, Yves UL et al

in Software Testing, Verification and Validation (ICST), 2012 IEEE Fifth International Conference on (2012)

Nowadays the large-scale systems are common-place in any kind of applications. The popularity of the web created a new environment in which the applications need to be highly scalable due to the data ... [more ▼]

Nowadays the large-scale systems are common-place in any kind of applications. The popularity of the web created a new environment in which the applications need to be highly scalable due to the data tsunami generated by a huge load of requests (i.e., connections and business operations). In this context, the main question is to validate how far the web applications can deal with the load generated by the clients. Load testing is a technique to analyze the behavior of the system under test upon normal and heavy load conditions. In this work we present a peer-to-peer load testing approach to isolate bottleneck problems related to centralized testing drivers and to scale up the load. Our approach was tested in a DBMS as study case and presents satisfactory results. [less ▲]

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