Data networks
Scale and performance in a distributed file system
ACM Transactions on Computer Systems (TOCS)
IEEE Transactions on Computers
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Journal of Parallel and Distributed Computing
IEEE Transactions on Knowledge and Data Engineering
Collaboration-Based Cloud Computing Security Management Framework
CLOUD '11 Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing
Data allocation strategies for the management of Quality of Service in Virtualised Storage Systems
MSST '11 Proceedings of the 2011 IEEE 27th Symposium on Mass Storage Systems and Technologies
Cloud Service Delivery across Multiple Cloud Platforms
SCC '11 Proceedings of the 2011 IEEE International Conference on Services Computing
Cost-Benefit Analysis of Virtualizing Batch Systems: Performance-Energy-Dependability Trade-Offs
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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Nowadays, more and more researchers have focused on the performance of cloud data centers. Successful development of cloud data center paradigm necessitates the best QoS for the end users and the Mean Response Time (MRT) of the data requests is one of the most important performance indicators that shall be emphasized on. A cloud data center consists clusters of Raw data Servers (RDS) that can provide raw data retrieval service. For a single data stored in the data center, there may be several RDS with the target raw data replicas. Hence, when a data request arrives, it has many potential data request paths and the system shall determine the best one for it. In this paper, we aim at answering an interesting question: {\em ``Do More Replicas of Object Data Improve the Performance of Cloud Data Centers?"}, in order to achieve the minimum MRT of all the requests. The target optimal constrained function has been formulated and two novel load balancing algorithms based on virtual routing method has been proposed, which can achieve near-optimal solutions by theoretical proof. We also find distributing the requests for the same objects among several RDS for load balancing purpose, which is widely used in most data centers, would worsen the system performance. We validate our findings via rigorous simulations with respect to several influencing factors and prove that our proposed strategy is scalable, flexible and efficient for the real-life applications.