Using a task dependency job-scheduling method to make energy savings in a cloud computing environment

Internet technology has developed rapidly, especially in the field of cloud computing. With the gradual growth of cloud computing capabilities, power consumption in data centres has become a very important issue. The development of cloud computing has made data centres the cornerstone of today’s global economic development, so data centres have also developed rapidly both in terms of construction scale and growth speed. However, large numbers of data centres consume huge amounts of power while also increasing the economic cost of cloud computing. They have led to soaring carbon dioxide emissions, which will have an unimaginable impact on the global climate. Therefore, the energy-consumption problem has become an important topic in current cloud computing research. How to save energy and reduce power consumption is a key issue, and this paper proposes an energy-saving job-scheduling method, which considers task dependency in a cloud computing environment. The proposed method considers the heterogeneous characteristics of data centres, models energy consumption based on the frequency and kernel number of the virtual machine CPU and provides new solutions to the problem of energy-consumption monitoring of cloud computing data centres. The main task is to divide each job into several tasks and then assign the tasks to virtual machines. Comparison of the simulation results, i.e. total execution time with job cutting and without job cutting, using the virtual machine (VM) (with the number of jobs set to 1000 and 2000), indicated that the total execution time and total energy consumption are better with job cutting than when the job is not cut, and this was not affected by the dependency of tasks. Moreover, job cutting also effectively reduces energy consumption and job discard rate.