A Dynamic Load Balancing Strategy Based on Improved Ant Colony Algorithm

Load balancing technique plays a fundamental role in distribution, while facing numerous challenges. It is difficult for ordinary load algorithms to cope with complex scenarios, and it is likely that uneven load will lead to downtime of a single server, thus causing an avalanche effect and eventually breaking down the whole cluster. This paper proposes a load balancing strategy based on improved ant colony algorithm, considers the influence of task length and processing efficiency in the algorithm based on the original ant colony algorithm, and takes the load rate of nodes as an important index for ants to select path. In addition, the load coefficient includes CPU, memory, IO, and network bandwidth into the calculation range, which makes the obtained server load condition more accurate. Finally, test data of different algorithms are obtained through experiments, and the advantages and disadvantages of this load balancing strategy are verified after horizontal comparison.