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A partition model and strategy based on the Stoer–Wagner algorithm for SaaS multi-tenant data

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  • Springer Nature
<![CDATA[<Heading>Abstract</Heading><Para ID="Par1">Partition technology is the key step to realize the extensional architecture in the cloud and support the data placement on multiple nodes. This paper proposes a multi-tenant data partition model and algorithm for SaaS (Software as a Service) application. It solves the problem that data partitions would produce lots of distributed transactions caused by the existing cloud data management. The management is unconscious of SaaS tenants during the transformation from a single node to multiple nodes in the cloud to obtain the dynamic extension of the system’s scale. With the increase of tenants and data, the single node becomes the bottleneck of the whole system. Fortunately, the scale of the whole system can be expanded by data partition. This paper puts forward a multi-tenant data partition model with three-layer structure:<Emphasis Type="Italic">Tenant layer</Emphasis>,<Emphasis Type="Italic">Relevance</Emphasis>,<Emphasis Type="Italic">Group layer</Emphasis>and<Emphasis Type="Italic">Tenant Partition layer</Emphasis>. Furthermore, we propose the concepts of<Emphasis Type="Italic">Relevance</Emphasis>,<Emphasis Type="Italic">Relevance Value</Emphasis>and<Emphasis Type="Italic">Relevance Matrix</Emphasis>. The customized tables for one tenant accessed by the same transactions can form a minimum high-relevance granularity based on the<Emphasis Type="Italic">Relevance Group</Emphasis>algorithm. Then we construct an abstracted graph, where<Emphasis Type="Italic">group</Emphasis>is the basic unit and transaction accessing is weight. Through the Stoer–Wagner algorithm, the multi-tenant partition with<Emphasis Type="Italic">group</Emphasis>as granularity is obtained. The partition algorithm proposed in this paper enables the greatest reduction of distributed transactions between partitions while realizing the dynamic extension on multiple nodes for multi-tenant data based on shared storage. Experiments show that the numb

SaaSMulti-tenant dataPartitionShared schema

Xiaona Li、Junli Zhao、Yumei Ma、Pingping Wang、Hongyi Sun、Yi Tang

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School of Data Science and Software Engineering, Qingdao University

Department of Mathematics, Guangzhou University

2017

10.1007/s00500-016-2169-z

Soft computing

Soft computing

AHCIEI
ISSN：1432-7643
年,卷(期)：2017.21(20)
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