A bi-objective cost model for optimizing database queries in a multi-cloud environment

Gounaris, Anastasios; Karampaglis, Zisis; Naskos, Athanasios; Manolopoulos, Yannis

doi:10.1016/j.jides.2015.02.002

Cited by 4 publications

(2 citation statements)

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“…Gounaris et al [13] propose a bi-objective cost model in order to optimize queries in multi-cloud environments. The queries are in a form of DAGs and are divided into strides where each stride represents a step in the execution and consists either of one operator or a group of operators running in parallel.…”

Section: A Cost Model Tailored To Stage-by-stage Analytics In a Cloud...mentioning

confidence: 99%

“…The cost models in [37,20,4] do not cover heterogeneity in compute node characteristics and/or locations. The models in [15,29,13] do not account for massive parallelism. Therefore, they are inadequate and unsuitable to be adopted for our case, which focuses on complex DAGs that are executed over geo-distributed heterogeneous resources while benefiting from partitioned parallelism.…”

Section: Summary Of Techniquesmentioning

confidence: 99%

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Cost models for geo-distributed massively parallel streaming analytics

Michailidou,

Gounaris,

Tsichlas

2021

Preprint

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This report is part of the DataflowOpt project on optimization of modern dataflows and aims to introduce a data quality-aware cost model that covers the following aspects in combination: (1) heterogeneity in compute nodes, (2) geo-distribution, (3) massive parallelism, (4) complex DAGs and (5) streaming applications. Such a cost model can be then leveraged to devise cost-based optimization solutions that deal with task placement and operator configuration.

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Section: A Cost Model Tailored To Stage-by-stage Analytics In a Cloud...mentioning

confidence: 99%