Multi-objective job scheduling in green cloud with big data: a hybrid heuristic approach for minimizing energy in datacenters

Abstract

Job scheduling has become one of the most challenging issues in the research field of data centers within the cloud computing sector. Green cloud computing is a paradigm that employs efficient techniques to significantly reduce carbon emissions, CPU frequency scaling, and overall energy consumption. The large volume of data processed necessitates the consideration of data center strategies to mitigate energy consumption, along with other factors such as environmental impact, operational cost, and system reliability. Thus, this research aims to develop a new job scheduling scheme in a big data-assisted green cloud environment by formulating a multi-objective optimization problem that considers makespan, power consumption, latency, and resource utilization. For handling big data, the MapReduce framework is employed. To implement the green cloud, a Hybrid Firefly Water Strider Optimization (HFWSO) algorithm is developed by combining the Firefly Algorithm (FFA) and Water Strider Algorithm (WSA). This algorithm optimizes the number of servers and jobs allocated based on data volume, ensuring efficient job distribution across servers. Task scheduling is performed to solve a multi-objective function under various constraints. The makespan of the developed model is 148.3 s, energy consumption is 6.72 kWh, and resource utilization is 76.50%. Thus, the findings demonstrate that it effectively minimizes the energy consumption in data centers and also appropriately allocates jobs to machines. These comparative findings also reinforce the suitability of HFWSO as a powerful metaheuristic for multi-objective optimization in energy-efficient cloud computing environments, making it a promising candidate for practical applications requiring balanced trade-offs among makespan, energy consumption, latency, and resource utilization.