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.
@cite_34 performed a quantitative analysis of the costs and benefits of spinning down a disk drive as a power management technique. The main idea behind the power consumption measurement movement is to be followed by suggestions or actions taken in order to find solutions to any undesirable outcomes. @cite_33 applied methods to analyze the relationship between global variable usage and the efforts required by software maintenance and examined the effects of optimizations upon power usage. @cite_49 employed source code change techniques to decrease the energy overheads accompanying application OS connections and modified the source code changes and compiler optimizations in order to reduce power usage. @cite_38 introduced a framework for studying the power-performance efficiency of the NAS parallel benchmarks on a 32-node Beowulf cluster.
@cite_38 @cite_34 @cite_33 @cite_49
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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. With the advent and subsequent popularity of portable computers, power management of system components has become an important issue. Current portable computers implement a number of power reduction techniques to achieve a longer battery life. Included among these is spinning down a disk during long periods of inactivity. In this paper, we perform a quantitative analysis of the potential costs and benefits of spinning down the disk drive as a power reduction technique. Our conclusion is that almost all the energy consumed by a disk drive can be eliminated with little loss in performance. Although on current hardware, reliability can be impacted by our policies, the next generation of disk drives will use technology (such as dynamic head loading) which is virtually unaffected by repeated spinups. We found that the optimal spindown delay time, the amount of time the disk idles before it is spun down, is 2 seconds. This differs significantly from the 3-5 minutes in current practice by industry. We will show in this paper the effect of varying the spindown delay on power consumption; one conclusion is that a 3-5 minute delay results in only half of the potential benefit of spinning down a disk. Current capacity planning practices based on heavy over-provisioning of power infrastructure hurt (i) the operational costs of data centers as well as (ii) the computational work they can support. We explore a combination of statistical multiplexing techniques to improve the utilization of the power hierarchy within a data center. At the highest level of the power hierarchy, we employ controlled underprovisioning and over-booking of power needs of hosted workloads. At the lower levels, we introduce the novel notion of soft fuses to flexibly distribute provisioned power among hosted workloads based on their needs. Our techniques are built upon a measurement-driven profiling and prediction framework to characterize key statistical properties of the power needs of hosted workloads and their aggregates. We characterize the gains in terms of the amount of computational work (CPU cycles) per provisioned unit of power Computation per Provisioned Watt (CPW). Our technique is able to double the CPWoffered by a Power Distribution Unit (PDU) running the e-commerce benchmark TPC-W compared to conventional provisioning practices. Over-booking the PDU by 10 based on tails of power profiles yields a further improvement of 20 . Reactive techniques implemented on our Xen VMM-based servers dynamically modulate CPU DVFS states to ensure power draw below the limits imposed by soft fuses. Finally, information captured in our profiles also provide ways of controlling application performance degradation despite overbooking. The 95th percentile of TPC-W session response time only grew from 1.59 sec to 1.78 sec--a degradation of 12 . Reducing energy consumption for high end computing can bring various benefits such as, reduce operating costs, increase system reliability, and environment respect. This paper aims to develop scheduling heuristics and to present application experience for reducing power consumption of parallel tasks in a cluster with the Dynamic Voltage Frequency Scaling (DVFS) technique. In this paper, formal models are presented for precedence-constrained parallel tasks, DVFS enabled clusters, and energy consumption. This paper studies the slack time for non-critical jobs, extends their execution time and reduces the energy consumption without increasing the task’s execution time as a whole. Additionally, Green Service Level Agreement is also considered in this paper. By increasing task execution time within an affordable limit, this paper develops scheduling heuristics to reduce energy consumption of a tasks execution and discusses the relationship between energy consumption and task execution time. Models and scheduling heuristics are examined with a simulation study. Test results justify the design and implementation of proposed energy aware scheduling heuristics in the paper.