Dynamic Tree Growth Algorithm for Load Scheduling in Cloud Environments

The cloud computing is an emerging paradigm that enables dynamic provision of elastic, scalable and distributed computer resources to the end-users. One of the most important tasks of the cloud service provider is to deliver services to the end-users in an efficient manner from a finite pool of available physical and virtual resources. The efficiency in terms of both, cost-efficiency and computational-efficiency, can be accomplished through the load scheduling that has significant impact on the overall cloud system performance and represents one of the most important challenges in this domain. In this paper, we introduce two implementations of the original and improved versions of the tree growth algorithm for load scheduling in cloud computing environments. Tree growth algorithm is classified as swarm intelligence metaheuristic, that are able to successfully tackle NP hard problems such as cloud load scheduling. Both algorithms are implemented in the CloudSim environment and comparative analysis with other techniques and metaheuristics for this problem was performed. According to the obtained results, the improved version of the tree growth algorithm outperformed all other techniques and can be successfully applied to load scheduling in cloud systems.