Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/598624
Title: Systematic Data Placement Optimization in Multi-Cloud Storage for Complex Requirements
Authors: Maomeng Su;Lei Zhang;Yongwei Wu;Kang Chen;Keqin Li
subject: data placement optimization|complex requirements|Systematic model|multi-cloud storage
Year: 2016
Publisher: IEEE
Abstract: Multi-cloud storage can provide better features such as availability and scalability. Current works use multiple cloud storage providers with erasure coding to achieve certain benefits including fault-tolerance improving or vendor lock-in avoiding. However, these works only use the multi-cloud storage in ad-hoc ways, and none of them considers the optimization issue in general. In fact, the key to optimize the multi-cloud storage is to effectively choose providers and erasure coding parameters. Meanwhile, the data placement should satisfy system or application developers' requirements. As developers often demand various objectives to be optimized simultaneously, such complex requirement optimization cannot be easily fulfilled by ad-hoc ways. This paper presents Triones, a systematic model to formally formulate data placement in multi-cloud storage by using erasure coding. Firstly, Triones addresses the problem of data placement optimization by applying non-linear programming and geometric space abstraction. It could satisfy complex requirements involving multi-objective optimization. Secondly, Triones can effectively balance among different objectives in optimization and is scalable to incorporate new ones. The effectiveness of the model is proved by extensive experiments on multiple cloud storage providers in the real world. For simple requirements, Triones can achieve 50 percent access latency reduction, compared with the model in μLibCloud. For complex requirements, Triones can improve fault-tolerance level by 2× and reduce access latency and vendor lock-in level by 30~70 percent and 49.85 percent respectively with about 19.19 percent more cost, compared with the model only optimizing cost in Scalia.
Description: 
URI: http://localhost/handle/Hannan/185756
http://localhost/handle/Hannan/598624
ISSN: 0018-9340
volume: 65
issue: 6
Appears in Collections:2016

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Title: Systematic Data Placement Optimization in Multi-Cloud Storage for Complex Requirements
Authors: Maomeng Su;Lei Zhang;Yongwei Wu;Kang Chen;Keqin Li
subject: data placement optimization|complex requirements|Systematic model|multi-cloud storage
Year: 2016
Publisher: IEEE
Abstract: Multi-cloud storage can provide better features such as availability and scalability. Current works use multiple cloud storage providers with erasure coding to achieve certain benefits including fault-tolerance improving or vendor lock-in avoiding. However, these works only use the multi-cloud storage in ad-hoc ways, and none of them considers the optimization issue in general. In fact, the key to optimize the multi-cloud storage is to effectively choose providers and erasure coding parameters. Meanwhile, the data placement should satisfy system or application developers' requirements. As developers often demand various objectives to be optimized simultaneously, such complex requirement optimization cannot be easily fulfilled by ad-hoc ways. This paper presents Triones, a systematic model to formally formulate data placement in multi-cloud storage by using erasure coding. Firstly, Triones addresses the problem of data placement optimization by applying non-linear programming and geometric space abstraction. It could satisfy complex requirements involving multi-objective optimization. Secondly, Triones can effectively balance among different objectives in optimization and is scalable to incorporate new ones. The effectiveness of the model is proved by extensive experiments on multiple cloud storage providers in the real world. For simple requirements, Triones can achieve 50 percent access latency reduction, compared with the model in μLibCloud. For complex requirements, Triones can improve fault-tolerance level by 2× and reduce access latency and vendor lock-in level by 30~70 percent and 49.85 percent respectively with about 19.19 percent more cost, compared with the model only optimizing cost in Scalia.
Description: 
URI: http://localhost/handle/Hannan/185756
http://localhost/handle/Hannan/598624
ISSN: 0018-9340
volume: 65
issue: 6
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7173008.pdf2.04 MBAdobe PDFThumbnail
Preview File
Title: Systematic Data Placement Optimization in Multi-Cloud Storage for Complex Requirements
Authors: Maomeng Su;Lei Zhang;Yongwei Wu;Kang Chen;Keqin Li
subject: data placement optimization|complex requirements|Systematic model|multi-cloud storage
Year: 2016
Publisher: IEEE
Abstract: Multi-cloud storage can provide better features such as availability and scalability. Current works use multiple cloud storage providers with erasure coding to achieve certain benefits including fault-tolerance improving or vendor lock-in avoiding. However, these works only use the multi-cloud storage in ad-hoc ways, and none of them considers the optimization issue in general. In fact, the key to optimize the multi-cloud storage is to effectively choose providers and erasure coding parameters. Meanwhile, the data placement should satisfy system or application developers' requirements. As developers often demand various objectives to be optimized simultaneously, such complex requirement optimization cannot be easily fulfilled by ad-hoc ways. This paper presents Triones, a systematic model to formally formulate data placement in multi-cloud storage by using erasure coding. Firstly, Triones addresses the problem of data placement optimization by applying non-linear programming and geometric space abstraction. It could satisfy complex requirements involving multi-objective optimization. Secondly, Triones can effectively balance among different objectives in optimization and is scalable to incorporate new ones. The effectiveness of the model is proved by extensive experiments on multiple cloud storage providers in the real world. For simple requirements, Triones can achieve 50 percent access latency reduction, compared with the model in μLibCloud. For complex requirements, Triones can improve fault-tolerance level by 2× and reduce access latency and vendor lock-in level by 30~70 percent and 49.85 percent respectively with about 19.19 percent more cost, compared with the model only optimizing cost in Scalia.
Description: 
URI: http://localhost/handle/Hannan/185756
http://localhost/handle/Hannan/598624
ISSN: 0018-9340
volume: 65
issue: 6
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7173008.pdf2.04 MBAdobe PDFThumbnail
Preview File