Context: Information Technology consumes up to 10 of the world's electricity generation, contributing to CO2 emissions and high energy costs. Data centers, particularly databases, use up to 23 of this energy. Therefore, building an energy-efficient (green) database engine could reduce energy consumption and CO2 emissions. Goal: To understand the factors driving databases' energy consumption and execution time throughout their evolution. Method: We conducted an empirical case study of energy consumption by two MySQL database engines, InnoDB and MyISAM, across 40 releases. We examined the relationships of four software metrics to energy consumption and execution time to determine which metrics reflect the greenness and performance of a database. Results: Our analysis shows that database engines' energy consumption and execution time increase as databases evolve. Moreover, the Lines of Code metric is correlated moderately to strongly with energy consumption and execution time in 88 of cases. Conclusions: Our findings provide insights to both practitioners and researchers. Database administrators may use them to select a fast, green release of the MySQL database engine. MySQL database-engine developers may use the software metric to assess products' greenness and performance. Researchers may use our findings to further develop new hypotheses or build models to predict greenness and performance of databases.
There are several studies about the power consumption of devices. @cite_15 produced power models for the complete system depending on processor performance events. @cite_1 measured and modeled the power consumption of hard drives. The hard disk state model provides both the quantitative data and insight necessary to design an efficient power management system. @cite_45 studied two types of optimization (namely, transport-level and application-level) of network interfaces to decrease their energy consumption.
@cite_15 @cite_45 @cite_1
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Recently, a large effort has been made to reduce the power consumed by computer-systems. Multiple power states have been defined, and mechanisms have been developed to allow system software to control transitions between these states. Unfortunately, little work has been done to determine effective times to change states. Statistical models of the power utilized by individual subsystems call provide a basis for making such decisions. The hard disk state model described provides both the quantitative data and insight necessary to design an efficient power management system. > Balancing the performance and the energy consumption of the servers is one of the important issues in large-scale computing infrastructure such as data centers. Measuring or accurately estimating power consumption of a server is one of the most fundamental and enabling technologies for enhancing energy efficiency of a server because how the server consumes the supplied power is essential for constructing a power management policy. For the purpose, power models for server systems have been extensively studied. However, most of existing works are too complex to be used real-time, because gathering the data for estimating the power consumption causes much overhead. In this paper, we propose a simple power model for a multicore server. Our model is simple enough to gather only four parameters: operating frequency, the number of active cores, the number of cache accesses and the number of the last level cache misses. We show our model is simple but relatively accurate by experiments that show the model has over 90 accuracy. Power consumption is a major obstacle for High Performance Computing HPC systems in their quest towards the holy grail of ExaFLOP performance. Significant advances in power efficiency have to be made before this goal can be attained and accurate modeling is an essential step towards power efficiency by optimizing system operating parameters to match dynamic energy needs. In this paper we present a study of power consumption by jobs in Eurora, a hybrid CPU-GPU-MIC system installed at the largest Italian data center. Using data from a dedicated monitoring framework, we build a data-driven model of power consumption for each user in the system and use it to predict the power requirements of future jobs. We are able to achieve good prediction results for over 80i?ź of the users in the system. For the remaining users, we identify possible reasons why prediction performance is not as good. Possible applications for our predictive modeling results include scheduling optimization, power-aware billing and system-scale power modeling. All the scripts used for the study have been made available on GitHub. The power consumption of presently available Internet servers and data centers is not proportional to the work they accomplish. The scientific community is attempting to address this problem in a number of ways, for example, by employing dynamic voltage and frequency scaling, selectively switching off idle or underutilized servers, and employing energy-aware task scheduling. Central to these approaches is the accurate estimation of the power consumption of the various subsystems of a server, particularly, the processor. We distinguish between power consumption measurement techniques and power consumption estimation models. The techniques refer to the art of instrumenting a system to measure its actual power consumption whereas the estimation models deal with indirect evidences (such as information pertaining to CPU utilization or events captured by hardware performance counters) to reason about the power consumption of a system under consideration. The paper provides a comprehensive survey of existing or proposed approaches to estimate the power consumption of single-core as well as multicore processors, virtual machines, and an entire server.