A Database Middleware Architecture for MPI-based Cloud Platform

Zhu, Lin; Guo, Yucheng

doi:10.1109/dcabes.2014.25

Cited by 2 publications

(6 citation statements)

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“…We obtained 17 papers related to 16 approaches. These papers were published in three distinct journals and seven distinct conferences and workshops.…”

Section: Related Workmentioning

confidence: 99%

“…The system enforces strong data consistency and scales linearly under a demanding workload composed of join queries and read‐write transactions. Zhu and Guo proposed an MPI cloud platform to perform SQL operations on a cloud database. This platform is based on MPI, supports massive data storage and implements a distributed database cluster based on MySQL DBMS.…”

Section: Related Workmentioning

confidence: 99%

“…We obtained 17 papers 17,18,[59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75] related to 16 approaches. These papers were published in three distinct journals and seven distinct conferences and workshops.…”

Section: Related Workmentioning

confidence: 99%

“…There are several approaches that already provide parallel query processing in clouds . However, most of them support only basic SQL operations, ie, none of them provide more complex features such as intraquery parallelism, virtual partitioning (VP), and load balancing.…”

Section: Introductionmentioning

confidence: 99%

“…* , † In this article, we assume this database-as-a-service (DBaaS) model.There are several approaches that already provide parallel query processing in clouds. [17][18][19] However, most of them support only basic SQL operations, ie, none of them provide more complex features such as intraquery parallelism, virtual partitioning (VP), and load balancing. This happens since the migration from clusters and grids to clouds poses many challenges for OLAP parallel query processing as the middleware has to consider more than one processing level: (1) cloud level, which requires the distribution of tasks in different cloud sites (eg, regions on Amazon AWS); (2) site level, which requires the distribution of tasks in different virtual clusters; and (3) virtual cluster level, which requires the redistribution of those tasks into several virtual machines.…”

mentioning

confidence: 99%

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OLAP parallel query processing in clouds with C‐ParGRES

Ribeiro

Lima

Oliveira

2019

Concurrency and Computation

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Summary The advent of big data technologies has changed the way many companies manage their data. Several companies moved their data to the cloud using the concept of database‐as‐a‐service (DBaaS). Moving databases to the cloud presents several challenges related to flexible and scalable management of data. Although some of these companies migrated to NoSQL databases, most still rely on relational databases in the cloud to manage data, especially data that is critical to the decision making process. Online analytical processing (OLAP) queries take a long time to be processed, thus demanding high‐performance capabilities from their associated database systems to get results in a feasible time. In this article, we propose a middleware solution that can be deployed in any cloud provider, named C‐ParGRES, which explores database replication and interquery and intraquery parallelism to efficiently support OLAP queries in the cloud. C‐ParGRES is an extension of ParGRES, an open‐source database cluster middleware for high‐performance OLAP query processing in clusters. C‐ParGRES exploits cloud capabilities such as on‐demand resource provisioning and elasticity. In addition, C‐ParGRES can create multiple and independent virtual clusters for different database and users. We evaluate C‐ParGRES with two real‐world OLAP applications, both from the Brazilian Institute of Geography and Statistics. Results show that C‐ParGRES is a cost‐effective solution for OLAP query processing in the cloud.

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“…We obtained 17 papers related to 16 approaches. These papers were published in three distinct journals and seven distinct conferences and workshops.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%