Cost-Effective Dynamic Optimisation for Multi-Cloud Queries

Wojtowicz, Damien T; Yin, Shi; Morvan, Franck; Hameurlain, Adbelkader

doi:10.1109/cloud53861.2021.00052

Cited by 2 publications

(8 citation statements)

References 28 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We reached the same conclusions while working on a middleware that processes SQL queries written on a multi-cloud relational schema aggregating databases from different DBaaS providers [3]. We identified two key design requirements for such a middleware.…”

Section: Related Workmentioning

confidence: 60%

“…In the literature, the most accurate results so far were obtained using a k-neighbours regression [11]. It was adapted in an online learning fashion in our middleware to post-process the response time component of the quotations [3], along with an online linear regression to post-process the monetary component. Nevertheless, both models can make errors, occasionally by an order of magnitude.…”

Section: Related Workmentioning

confidence: 99%

“…The topic of multi-cloud data management raised interests in the industry as well as in the DB research community (as highlighted by the Seattle Report [1]). Specialised middlewares for multi-cloud database integration have consequently been introduced [2], [3] to let users write multi-cloud queries independently from provider-arranged multi-cloud federations. In previous work [3], we identified two main design requirements for such a middleware.…”

Section: Introductionmentioning

confidence: 99%

“…Specialised middlewares for multi-cloud database integration have consequently been introduced [2], [3] to let users write multi-cloud queries independently from provider-arranged multi-cloud federations. In previous work [3], we identified two main design requirements for such a middleware. First, it should be able to quote the monetary cost of the multi-cloud queries, so as to let its users control their expenses.…”

Section: Introductionmentioning

confidence: 99%

“…With the middleware presented in [3], we succeeded to meet these requirements for a small number of providers. Experimental results encouraged us to try with many more simulated providers, and therefore to overcome some limitations of the original proposal.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Multi-Cloud Query Optimisation with Accurate and Efficient Quoting

Wojtowicz

Yin

Martinez‐Gil

et al. 2022

2022 IEEE International Conference on Big Data (Big Data)

Self Cite

View full text Add to dashboard Cite

A recent trend among major organisations is to release their datasets in the cloud over various Database-as-a-Service (DBaaS) providers' premises, creating a use case for multi-cloud querying. As identified in the literature, middlewares with such capabilities should quote the monetary cost and the response time of the queries in order to gain the trust of their users, and also optimise the queries so as to avoid cost overruns and meet the quotations. Considering those requirements, this paper introduces an accurate cost model and an efficient execution plan search strategy for dealing with large-scale multi-cloud queries. The former is an ensemble learning stack leveraging online machine learning models, and the latter is a randomised method inspired by iterative improvement. We evaluated our middleware over simulated providers by using the Join Order Benchmark. Experiments showed that the cost model manages to correct the estimations from the providers. The randomised strategy can produce more efficiently execution plans that yield better performances and a lower monetary cost compared to an exhaustive approach from previous work.

show abstract

Section: Related Workmentioning

confidence: 60%

Section: Related Workmentioning

confidence: 99%