JACIII Vol.23 No.1 pp. 78-83
doi: 10.20965/jaciii.2019.p0078


Data Equilibrium Method of Distributed Parallel Database Under High Load

Dingxiang Zhang

Guizhou University of Commerce
No.2 Yanwu Street, Yunyan District, Guiyang, Guizhou 550014, China

May 5, 2018
June 7, 2018
January 20, 2019
distributed parallel database, high load, data equalization method

The traditional method cannot make predictive judgment on the future load of the system, which leads to the convergence speed in the local updating process and cause the waste of resources. Aiming at this problem, a data equalization method based on ant colony optimization algorithm is proposed. During the calculation of server cluster integrated load, two kinds of load information input indicators and server indexes are mainly used. A formal description of the task scheduling problem under the high load of distributed parallel database is carried out and the mathematical model is established; the independent and different resource required virtual machine in the system are deployed in the server to balance the system, which has good global convergence, and can effectively control the system resource usage. Experiments showed that the proposed method avoids the unwanted migration caused by the instantaneous peak, which reduces the overhead of the system.

Cite this article as:
D. Zhang, “Data Equilibrium Method of Distributed Parallel Database Under High Load,” J. Adv. Comput. Intell. Intell. Inform., Vol.23 No.1, pp. 78-83, 2019.
Data files:
  1. [1] N. Amelina, A. Fradkov, Y. Jiang, et al., “Approximate Consensus in Stochastic Networks with Application to Load Balancing,” IEEE Trans. on Information Theory, Vol.61, No.4, pp. 1739-1752, 2015.
  2. [2] C. Pham, N. H. Tran, C. T. Do, et al., “Joint Consolidation and Service-Aware Load Balancing for Datacenters,” IEEE Communications Letters, Vol.20, No.2, pp. 292-295, 2016.
  3. [3] J. Park, H. Byun, and J. R. Lee, “Bio-Inspired Load-Balancing Framework for Loosely Coupled Heterogeneous Server Systems,” IEEE Trans. on Computers, Vol.65, No.11, pp. 3280-3292, 2016.
  4. [4] H. Ryu, S. Park, E. Ryu, et al., “Load Balancing Based on Transform Unit Partition Information for High Efficiency Video Coding Deblocking Filter,” Etri J., Vol.39, No.3, pp. 301-309, 2017.
  5. [5] J. Singh and C. S. Rai, “An optimized prioritized load balancing approach to scalable routing (OPLBA),” Wireless Networks, Vol.22, No.1, pp. 1-16, 2015.
  6. [6] A. Wang, Q. Li, and C. S. Wang, “Research and Simulation of Data Equilibrium Strategy under High Database Loading,” Computer Simulation, Vol.33, No.3, pp.327-330, 2016.
  7. [7] F. Xia, A. M. Ahmed, L. T. Yang, et al., “Community-Based Event Dissemination with Optimal Load Balancing,” IEEE Trans. on Computers, Vol.64, No.7, pp. 1857-1869, 2015.
  8. [8] H. Q. Zhang, X. P. Zhang, H. T. Wang, et al., “Task Scheduling Algorithm Based on Load Balancing Ant Colony Optimization in Cloud Computing,” Microelectronics & Computer, Vol.32, No.5, pp. 31-35, 2015.
  9. [9] J. Y. Zheng and R. S. Ko, “Distributed De La Garza algorithm for load-balancing routing in wireless sensor networks,” Wireless Networks, Vol.21, No.1, pp. 297-314, 2015.
  10. [10] G. Yang, Y. Zhang, J. Yang, et al., “Automated classification of brain images using wavelet-energy and biogeography-based optimization,” Multimedia Tools & Applications, Vol.75, No.23, pp. 15601-15617, 2016.
  11. [11] G. Yang, W. Tan, H. Jin, et al., “Review wearable sensing system for gait recognition,” Cluster Computing, pp. 1-9, 2018.
  12. [12] D.-Y. Li, Q.-T. Shen, Z.-T. Liu, and H. Wang, “Control of a Stand-Alone Wind Energy Conversion Systemvia a Third-Harmonic Injection Indirect Matrix Converter,” J. Adv. Comput. Intell. Intell. Inform., Vol.20, No.3, pp. 438-447, 2016.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 19, 2024