Research Article
ACEA: A Queueing Model-Based Elastic Scaling Algorithm for Container Cluster
Input: achievement rate of historical tasks , task processing interval , number of containers in the current cluster . | Output: indicator information for container cluster, volume of autoscaling, mode of adaptive scaling. | 1. global var , Fitness; //Fitness: real-time comprehensive resource utilization rate of cluster. | 2. const var , , ;// : maximum waiting time of tasks. | 3. define func getConfig(): ; | 4. Input | 5. par[Pnum]= | 6. PSOInit(Pnum): ParticleInit(Pnum), ParticleEvaluate(); | 7. Fitness = Wq(getUall(s), getUlimit()); | 8. PSORun(): | 9. For each par. | 10. ParticleUpdate(); | 11. ParticleEvaluate(); | 12. End For; | 13. For Iteration times do. | 14. If (): | 15. Contract(service); | 16. End if; | 17. If(()|| ): | 18. Expand(service); | 19. End if; | 20. If(): | 21. PSOshowresult(); | 22. End if; | 23. End For; |
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