Load Evaluation Algorithm of Cloud Database based on Shannon Entropy
Chen Qing, Yong Zhong, Liuming Xiang
Journal of Data Processing, Vol. 5, No. 4, 2015.
DOI: 
Link: http://www.dline.info/jdp/fulltext/v5n4/jdpv5n4_1.pdf
Abstract:
Due to the two-phase commit protocol, all transactions of DDBS (distributed database system) will roll
back if one of distributed nodes was overload, finally make the DDBS difficultly adapting to the Big Dataâ€™s environment,
whose data has the characters of dynamic and randomness. In order to solve this problem, Shannon entropy is proposed
to evaluate systemâ€™s load, using the maximum entropy principle of entropy with the objective function and constraints to
balance the load and maximize resourceâ€™s utilization on the demand of userâ€™s QoS. Overloaded nodeâ€™s data will be
migrated to other suitable nodes under the guidance of algorithm based on Shannon entropy, and make a step to the
further design of Cloud database system. Experimental results show that the load evolution algorithm based on Shannon
entropy can evaluate the load in Big Dataâ€™s environment, avoid single-node bottlenecks, and improve systemâ€™s
performance.