LLCG: A High Performance Implement for Multi-tenant Data Placement

Author

Wu Na ; Zhang Shidong ; Kong Lanju

Author_Institution

Dept. of Comput. Sci. & Technol., Shandong Univ., Jinan, China

fYear

2013

Firstpage

7

Lastpage

10

Abstract

How to optimally place the tenant replication data to retain the load-balance and reduce cost of communication and distributed transaction, it is an important issue to achieve the high performance and availability of multi-tenant data, there are plenty of issues need to be solved. This paper proposes the Multi-Objective Genetic Algorithm. It uses a rank-based fitness assignment method for MOGAs to placement and adjustment the multi-tenant data called LLCG. Then we validate the effectiveness and performance of our algorithm compared with LRCG and LLC in simulation experiment.

Keywords

genetic algorithms; resource allocation; software architecture; LLCG; MOGA; communication cost reduction; distributed transaction; high performance implement; load-balance; multiobjective genetic algorithm; multitenant data adjustment; multitenant data placement; rank-based fitness assignment method; software architecture; tenant replication data; Availability; Distributed databases; Educational institutions; Heuristic algorithms; Memory; Nickel; load-balance; multi-object genetic optimal; multi-tenant;

fLanguage

English

Publisher

ieee

Conference_Titel

Web Information System and Application Conference (WISA), 2013 10th

Conference_Location

Yangzhou

Print_ISBN

978-1-4799-3218-4

Type

conf

DOI

10.1109/WISA.2013.9

Filename

6778600