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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Bibliographic Details
Published in:IEEE transactions on computers Vol. 65; no. 6; pp. 1964 - 1977
Main Authors: Su, Maomeng, Zhang, Lei, Wu, Yongwei, Chen, Kang, Li, Keqin
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0018-9340
1557-9956
DOI:10.1109/TC.2015.2462821