an improved ant colony algorithm based on the cost of energy consumption and user is proposed to solve the problem of load imbalance in the process of cloud resource allocation. First
the energy consumption cost function model of users and operators is established in the cloud environment. Secondly
we have introduced the dynamic adjustment factor DRF in pheromone update mechanism
which makes the algorithm achieves load balancing state after several iterations. Finally
the simulation test is carried out on the cloud simulation platform named Cloudsim. The experimental results show that the algorithm can reduce the task execution cost
and meet the resource load balancing well.
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references
Dean J, Ghemawat S, MapReduce: simplified data processing on large clusters[C]//Proceedings of the 6th Symposium on Operating System Design and Implementation. New York: ACM, 2004: 137-152.
Dorigo M, Gambardella L M. Ant colony system: a cooperative learning approach to the traveling salesman problem[J]. IEEE Transaction on Evolutionary Computation, 1997, 1(1): 53-66. DOI: 53
Mahesh A. A new common subexpression elimination algorithm for realizing low-complexity higher order digital filters[J]. IEEE Trtans. Compt. -Aided Design Integr. Circuits Syst, 2008, 27(2): 217-229. DOI: 217