Description

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 the task scheduling and source code improvement in the aspect of EC optimization. The existing researches 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 same statements with different structures are unequal, therefore, 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 shows that the ratios between the EC and AEC with 50 test cases are stable; the standard deviation is 0.0002; 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