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QoS-Aware Data Replication for Data-Intensive Applications in Cloud Computing Systems
January-June 2013 (vol. 1 no. 1)
pp. 101-115
Jenn-Wei Lin, Dept. of Comput. Sci. & Inf. Eng., Fu Jen Catholic Univ., New Taipei, Taiwan
Chien-Hung Chen, Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan
J. Morris Chang, Dept. of Electr. & Comput. Eng., Iowa State Univ., Ames, IA, USA
Cloud computing provides scalable computing and storage resources. More and more data-intensive applications are developed in this computing environment. Different applications have different quality-of-service (QoS) requirements. To continuously support the QoS requirement of an application after data corruption, we propose two QoS-aware data replication (QADR) algorithms in cloud computing systems. The first algorithm adopts the intuitive idea of high-QoS first-replication (HQFR) to perform data replication. However, this greedy algorithm cannot minimize the data replication cost and the number of QoS-violated data replicas. To achieve these two minimum objectives, the second algorithm transforms the QADR problem into the well-known minimum-cost maximum-flow (MCMF) problem. By applying the existing MCMF algorithm to solve the QADR problem, the second algorithm can produce the optimal solution to the QADR problem in polynomial time, but it takes more computational time than the first algorithm. Moreover, it is known that a cloud computing system usually has a large number of nodes. We also propose node combination techniques to reduce the possibly large data replication time. Finally, simulation experiments are performed to demonstrate the effectiveness of the proposed algorithms in the data replication and recovery.
Index Terms:
Quality of service,Cloud computing,Servers,Switches,Radio frequency,Algorithm design and analysis,Heuristic algorithms,Cloud computing,network flow problem,Cloud computing,data-intensive application,quality of service,data replication
Citation:
Jenn-Wei Lin, Chien-Hung Chen, J. Morris Chang, "QoS-Aware Data Replication for Data-Intensive Applications in Cloud Computing Systems," IEEE Transactions on Cloud Computing, vol. 1, no. 1, pp. 101-115, Jan.-June 2013, doi:10.1109/TCC.2013.1
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