Multi-objective workflow scheduling optimization for energy efficiency in cloud computing

Task scheduling in cloud computing systems is a critical and challenging problem requiring decisions regarding resource allocation to tasks to optimize a performance criterion. This problem has required researchers and developers to overcome significant challenges. Our goal in this paper is to minimize the makespan and reduce energy consumption in cloud computing systems by efficiently scheduling workflows.To achieve this, we first proposed a dynamic multi-objective modeling which is transformed into a mono-objective one using dynamic weights. Then, we proposed a dynamic genetic algorithm (DGA) to solve the problem.The results are compared with the HEFT algorithm and signifi-cantly reduced the total energy consumption.