Cost models for view materialization in the cloud

Nguyen, Thi-Van-Anh; Bimonte, Sandro; d’Orazio, Laurent; Darmont, Jérôme

doi:10.1145/2320765.2320788

Cited by 29 publications

(22 citation statements)

References 23 publications

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“…), development platforms and services [26] with different pricing policies. This paper relies on a limited, yet representative enough model that include the main, commonly billed elements in data sets management, i.e., CPU, storage and bandwidth consumption.…”

Section: Data Sets Management Pricing Policiesmentioning

confidence: 99%

Combination Replicas Placements Strategy for Data sets from Cost-effective View in the Cloud

Wu¹

2017

IJCIS

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In the cloud storage system, data sets replicas technology can efficiently enhance data availability and thereby increase the system reliability by replicating commonly used data sets in geographically different data centers. Most current approaches largely focus on system performance improvement by placing replicas for an independent data set, omitting the generation relationship among data sets. Furthermore, cost is an important element in deciding replicas number and their stored places, which can cause great financial burden for cloud clients because the cost for replicas storage and consistency maintenance may lead to high overhead with the number of new replicas increased in a pay-as-you-go paradigm. In this paper, we propose a combination strategy of real-replicas and pseudo-replicas (by computation from its provenance) from cost-effective view in order to achieve the minimum data set management cost, not only for the independent data sets but also for related data sets with generation relationships. We first define cost models that fit into the cloud computing paradigm, including data sets storage, computation and transfer costs, and then develop a new data set management cost model, helping to achieve a multi-criteria optimization of data set management. After that, a minimum cost benchmarking approach for the best trade-off between real-replicas and pseudo-replicas is proposed once decision to add a replica has been made. Then, a more practical and reasonable genetic algorithm as an alternative procedure for generating optimal or nearoptimal solution is given in order to identify the suitable replicas storage places. Finally, we present simulations setups and results that provide a first validation of our strategy. Both the theoretical analysis and simulations conducted on general (random) data sets as well as specific real world applications have shown efficiency and effectiveness of the improved system brought by the proposed strategy in cloud computing environment.

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Section: Data Sets Management Pricing Policiesmentioning

confidence: 99%

Combination Replicas Placements Strategy for Data sets from Cost-effective View in the Cloud

Wu¹

2017

IJCIS

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“…Cloud computing may be defined as a payper-use model for enabling on-demand access to reliable and configurable resources that can be quickly provisioned and released with minimal management. Users only pay for the resources they use [5].…”

Section: A Cloud Computingmentioning

confidence: 99%

“…Figure 6 shows different execution plans for a query Q1executed in data warehouse hosted on cloud. Previous works proposed cost models related to materialized views in a data warehouse arranged in the cloud [5] [4]. The main objective of these works is to optimize the time response of queries in the cloud.…”

Section: ) Queries Execution Planmentioning

confidence: 99%

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Optimisation of the queries execution plan in cloud data warehouses

Ettaoufik

Ouzzif

2015

2015 5th World Congress on Information and Communication Technologies (WICT)

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Our everyday massive data processing activities and making correct decisions require a specific work environment. The cloud computing provides a flexible environment for customers to host and process their information through an outsourced infrastructure. This information was habitually located on local servers. Many applications dealing with massive data is routed to the cloud. Data Warehouse (DW) also benefit from this new paradigm to provide analytical data online and in real time. DW in the Cloud benefited of its advantages such flexibility, availability, adaptability, scalability, virtualization, etc. Improving the DW performance in the cloud requires the optimization of data processing time. The classical optimization techniques (indexing, materialized views and fragmentation) are still essential for DW in the cloud. The DW is partitioned before being distributed across multiple servers (nodes) in the Cloud. When queries containing multiple joins or ask voluminous data stored on multiple nodes, inter-node communication increases and consequently the DW performance degrades. In this paper, we propose an approach for improving the performance of DW in the cloud. Our approach is based on a classification technique of requests received by the nodes. For this purpose we use an algorithm based on the MapReduce programming model, this algorithm allows to identify the list of requests sent to the DW hosted in the cloud, and classify queries by the publication frequency. From the list of search queries we propose a partitioning scheme of DW on different nodes in order to reduce the inter-node communication and therefore minimize the queries processing time.

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“…There are existing analytical models that estimate the usage of various resources for maintaining a materialized view, based on update rate, join selectivity, data location, etc. (e.g., [19]). Furthermore, the resource usage should also vary with the staleness SLA of the sharing.…”

Section: Cost Modelmentioning

confidence: 99%

Cost exploration of data sharings in the cloud

Al-Kiswany¹,

Hacıgümüş

Liu

et al. 2013

Proceedings of the 16th International Conference on Extending Database Technology

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Enabling data sharing among mobile apps hosted in the same cloud infrastructure can provide a competitive advantage to the mobile apps by giving them access to rich information as well as increasing the revenue for the cloud provider. We introduce a costing tool that allows application owners (i.e., consumers) and the cloud service provider to assess the cost of a desired data sharing. The costing tool enables the consumers to effectively explore the cost space by choosing between alternative configurations of varying data qualities, specified by the staleness and the accuracy of the data sharing. In other words, staleness and accuracy requirements on the data sharing are used as levers for controlling costs. These capabilities are implemented in a What-if analysis tool, which has been integrated with a large data-sharing platform. We conducted extensive experiments on the integrated platform with a sharing ecosystem created around Twitter data and show the effectiveness of the results produced by the What-if tool.

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Cost models for view materialization in the cloud

Cited by 29 publications

References 23 publications

Combination Replicas Placements Strategy for Data sets from Cost-effective View in the Cloud

Combination Replicas Placements Strategy for Data sets from Cost-effective View in the Cloud

Optimisation of the queries execution plan in cloud data warehouses

Cost exploration of data sharings in the cloud

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