Variability models for generating efficient configurations of functional quality attributes

Combined Prediction Energy Model at Software Architecture Level

“…After getting potential quality attribute, they provides a means to quantify energy consumption aspects related to software. Horcas [26] has pointed that different configurations of quality attributes in software architecture influenced energy efficiency. They represented the variability of quality attributes, as well as the energy efficiency and performance experiment results as a constraint satisfaction problem to help software developers to build more energy efficient software.…”

“…For example, at the bottom of the hardware, saving energy are conducted by improving the manufacturing process, optimizing the circuit structure. At instruction level, the instructions optimized by compiler, instruction conversion and rearrangement, and loop structure optimization [9][10][11][12][13] are used for energy management; in the source code layer, code expression change, data representation and program structure rearrangement are optimized, and redundant calculation is eliminated, and data storage space are compressed [14][15][16][17][18][19][20]; at component level, and process level energy management [21][22][23] are used; at architecture layer, macro-modeling, system structure selection, transformation and simplification [24][25][26] are used.…”

“…At present, this work is mainly carried out at several levels. At the hardware level, the power consumption is measured by power meter and power sensor [6][7][8]; at the instruction level, the energy consumption is mainly obtained by statistical method, that is, the energy of software is obtained by accumulating the energy of each instruction or each kind of instructions, instruction pairs, pipeline or cache and other hardware structures during the execution [9][10][11][12][13]; at the source code level, the single line code energy consumption is mainly obtained by linear regression, and then the software energy consumption is obtained by accumulating the energy of each single line code [14][15][16][17][18][19][20]; at the process level, the relationship between the resource utilization rate and energy of the process is established, and then, it accumulates time to obtain process energy consumption [21][22][23]; at the architecture level, the relationship between software high-level features and energy consumption is explored to predict software energy consumption [24][25][26]. Generally, software can be considered as a complex system, and then its architecture can be represented by complex network to study different characteristics of software [28][29][30].…”

Context-aware energy-efficient applications for cyber-physical systems

“…So, the intention is to store the energy consumption obtained following different approaches, and provide this information to the developer. Certainly, we could gather results from many already published experimental studies [10,34] , store them in the HADAS Green Repository and provide advice based on these results.…”

“…Generating all the possible configurations of the energy consuming concerns and performing all the required experiments may sound an intractable task. However, it is demonstrated [34] that the number of contextual features and configurable parameters of the energy consuming concerns that affect the energy consumption is usually low and the total number of configurations and experiments is manageable. For instance, as shown in Table 2 , for compression that is the more complicated concern, there are only 6 contextual features and 7 implementation features that can affect the energy consumption.…”