Systematic Data Placement Optimization in Multi-Cloud Storage for Complex Requirements

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 stora...

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Veröffentlicht in:	IEEE transactions on computers Jg. 65; H. 6; S. 1964 - 1977
Hauptverfasser:	Su, Maomeng, Zhang, Lei, Wu, Yongwei, Chen, Kang, Li, Keqin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.06.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Cloud computing Coding complex requirements Cost engineering data placement optimization Data storage Encoding Fault tolerance Fault tolerant systems Linear programming Mathematical models multi-cloud storage Optimization Placement Systematic model Systematics data placement optimization multi-cloud storage complex requirements Systematic model
ISSN:	0018-9340, 1557-9956
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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.
AbstractList	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 $\mu$[formula graphic omitted, see PDF] LibCloud. For complex requirements, Triones can improve fault-tolerance level by 2 $\times$ [formula graphic omitted, see PDF] and reduce access latency and vendor lock-in level by 30$\sim$ [formula graphic omitted, see PDF]70 percent and 49.85 percent respectively with about 19.19 percent more cost, compared with the model only optimizing cost in Scalia. 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. 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 [Formula Omitted]LibCloud. For complex requirements, Triones can improve fault-tolerance level by 2[Formula Omitted] and reduce access latency and vendor lock-in level by 30[Formula Omitted]70 percent and 49.85 percent respectively with about 19.19 percent more cost, compared with the model only optimizing cost in Scalia.
Author	Lei Zhang Keqin Li Maomeng Su Yongwei Wu Kang Chen
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SubjectTerms	Cloud computing Coding complex requirements Cost engineering data placement optimization Data storage Encoding Fault tolerance Fault tolerant systems Linear programming Mathematical models multi-cloud storage Optimization Placement Systematic model Systematics
Title	Systematic Data Placement Optimization in Multi-Cloud Storage for Complex Requirements
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