An energy-efficient task scheduling method for heterogeneous cloud computing systems using capuchin search and inverted ant colony optimization algorithm

Cloud computing (CC) is a computing paradigm to satisfy end users' computing and storage needs. Cloud data centers (DC) must continuously improve their performance due to the exponential rise in service demand. Task scheduling is an essential part of CC to achieve optimal resource utilization, reduced energy consumption (EC), minimum response time, and maximum efficiency. Scheduling algorithms are crucial for task scheduling and resource mapping in distributed and parallel systems. This study proposes a novel approach for migrating virtual machines (VMs) using a capuchin search algorithm (CapSA). The proposed approach seeks to utilize the strengths of migration and scheduling based on a hybrid multi-objective CapSA and inverted ant colony optimization (IACO) algorithms and selects an optimal algorithm to apply to the succeeding task by adopting a decision-making framework according to the received tasks' conditions. The proposed approach outperforms the earlier approaches regarding EC, execution time (ET), and load balancing by 15-20%.