Journals
  Publication Years
  Keywords
Search within results Open Search
Please wait a minute...
For Selected: Toggle Thumbnails
Data center flow scheduling mechanism based on differential evolution and ant colony optimization algorithm
Rongrong DAI, Honghui LI, Xueliang FU
Journal of Computer Applications    2022, 42 (12): 3863-3869.   DOI: 10.11772/j.issn.1001-9081.2021101766
Abstract305)   HTML10)    PDF (2071KB)(114)       Save

As the traditional flow scheduling method for data center network is easy to cause network congestion and link load imbalance, a dynamic flow scheduling mechanism based on Differential Evolution (DE) and Ant Colony Optimization (ACO) algorithm (DE-ACO) was proposed to optimize elephant flow scheduling in data center networks. Firstly, Software Defined Network (SDN) technology was used to capture the real-time network status information and set the optimization objectives of flow scheduling. Then, DE algorithm was redefined by the optimization objectives, several available candidate paths were calculated and used as the initialized global pheromone of the ACO algorithm. Finally, the global optimal path was obtained by combining with the global network status, and the elephant flow on the congested link was rerouted. Experimental results show that compared with Equal-Cost Multi-Path routing (ECMP) algorithm and network flow scheduling algorithm of SDN data center based on ACO algorithm (ACO-SDN), the proposed algorithm increases the average bisection bandwidth by 29.42% to 36.26% and 5% to 11.51% respectively in random communication mode, reducing the Maximum Link Utilization (MLU) of the network, and achieving better load balancing of the network.

Table and Figures | Reference | Related Articles | Metrics