Efficient renewable energy-based geographical load balancing algorithms for green cloud computing

The cloud marketplace is continuously rising as enterprises desire to streamline their processes. As adaptability increases, CSPs expand their data centres to handle any UR size. It increases the fossil fuels consumed in each data centre, increasing the overall cost. Therefore, CSPs are looking for economical ways to reduce fossil fuels. Consequently, three benchmark algorithms were developed in the literature for GLB using renewable energy sources. However, they present the UR using the processor without considering memory. This paper presents two algorithms, PM-FABEF and PM-HAREF, for GCC by incorporating both processor and memory. PM-FABEF determines the processor and memory costs for assigning URs to the data centres and assigns them to the least cost data centre. PM-HAREF determines the highest renewable energy resource slots in processor and memory for assigning the URs. The proposed algorithms are compared with three algorithms using ten datasets to show their superiority in terms of three performance metrics.