Source-Level Energy Consumption Estimation for Cloud Computing Tasks

Abstract

In the cloud computing environment, the source-level energy consumption (EC) estimation is employed to approximately measure the EC of a cloud computing task before it is executed. The EC estimation on tasks is critical to task scheduling and source-code improvement in the aspect of EC optimization. The existing studies treat a task as a program, and EC of the task as the simple summation of each statement’s EC. However, EC of two tasks consisting of the same statements with different structures is unequal; therefore, the code structure should be highlighted in source-level EC estimation. In this paper, an abstract energy consumption (AEC) model, which is static and runtime-independent, is proposed. For the model, the two quantitative measurements, “cross-degree” and “reuse-degree,” are proposed as the code structure features, and the relationship between EC and the measurements is formulated. Although AEC is not a precise EC measurement, it can properly represent the EC of a task, compare with other tasks, and verify the optimization effect. Experimental results show that the ratios between the EC and AEC with 50 test cases are stable; the standard deviation is 0.0002; and the mean value is 0.005. The regularities of EC and code structures, represented as “cross-degree” and “reuse-degree,” are also validated. Though AEC, it is easier to schedule the cloud computing tasks properly and further reduce the consumed energy.