Load balanced transaction scheduling in on-demand computing using cuckoo search-ant colony optimization

Load balanced transaction scheduling in on-demand computing system is known to be NP-hard problem. In order to solve this problem, this paper introduces a hybrid approach named cuckoo search-ant colony optimization. The approach dynamically generates an optimal schedule by clustering the on-demand computing resources considering their load and completes the transaction execution within their deadlines. The approach also balances the load of the system before scheduling the transactions. For clustering the resources we use cuckoo search method. We use ant colony optimization for selecting the appropriate and optimal resources. We evaluate the performance of the proposed algorithm with six existing algorithms.