Profiling Power Consumption on Desktop Computer Systems

Background. Energy awareness in the ICT has become an important issue: ICT is both a key player in energy efficiency, and a power drainer. Focusing on software, recent work suggested the existence of a relationship between power consumption, software configuration and usage patterns in computer systems. Aim. The aim of this work was collecting and analysing power consumption data of a general-purpose computer system, simulating common usage scenarios, in order to extract a power consumption profile for each scenario. Methods. We selected a desktop system running Windows XP as a test machine. Meanwhile, we developed 11 usage scenarios, classified by their functionality, and automated by a GUI testing tool. Then, we conducted several test runs of the scenarios, collecting power consumption data by means of a power meter. Results. Our analysis resulted in an estimation of a power consumption value for each scenario and software application used, obtaining that each single scenario introduced an overhead from 2 to 11 Watts, corresponding to an increase of about 12%. Conclusions. We determined that software and its usage patterns impacts consistently on the power consumption of computer systems. Further work will be devoted to evaluate how power consumption is affected by the usage of specific system resources, like processors, disks, memory et

An Early-Stage Statement-Level Metric for Energy Characterization of Embedded Processors

Abstract This work presents an early stage statement-level metric for energy characterization of embedded processors. Definition and the framework for metric evaluation are provided. In particular, such a metric is based on an existing assembly-level analysis and some profiling activities performed on a given C benchmark, and it is related to the average energy consumption of a generic C statement, for a given target processor. Its evaluation is performed with a one-time effort and, once available, it can be used to rapidly estimate the energy consumption of a given C function for all the considered processors. Two reference embedded processors are then considered in order to show an example of usage of the proposed metric and framework

PowTrAn: An R Package for power trace analysis

Energy efficiency is an increasingly important non-functional property of software, especially when it runs on mobile or IoT devices. An engineering approach demands a reliable measurement of energy consumption of software while performing computational tasks. In this paper, we describe PowTrAn, an R package supporting the analysis of the power traces of a device executing software tasks. The tool analyzes traces with embedded markers, a non-invasive technique that enables gauging software efficiency based on the energy consumed by the whole device. The package effectively handles large power traces, detects work units, and computes correct energy measures, even in noisy conditions, such as those caused by multiple processes working simultaneously. PowTrAn was validated on applications in realistic conditions and multiple hardware configurations. PowTrAn also provides data visualization that helps the user to assess the measurement consistency, and it also helps to highlight possible energy outliers

Approximation Algorithms for Energy Minimization in Cloud Service Allocation under Reliability Constraints

We consider allocation problems that arise in the context of service allocation in Clouds. More specifically, we assume on the one part that each computing resource is associated to a capacity constraint, that can be chosen using Dynamic Voltage and Frequency Scaling (DVFS) method, and to a probability of failure. On the other hand, we assume that the service runs as a set of independent instances of identical Virtual Machines. Moreover, there exists a Service Level Agreement (SLA) between the Cloud provider and the client that can be expressed as follows: the client comes with a minimal number of service instances which must be alive at the end of the day, and the Cloud provider offers a list of pairs (price,compensation), this compensation being paid by the Cloud provider if it fails to keep alive the required number of services. On the Cloud provider side, each pair corresponds actually to a guaranteed success probability of fulfilling the constraint on the minimal number of instances. In this context, given a minimal number of instances and a probability of success, the question for the Cloud provider is to find the number of necessary resources, their clock frequency and an allocation of the instances (possibly using replication) onto machines. This solution should satisfy all types of constraints during a given time period while minimizing the energy consumption of used resources. We consider two energy consumption models based on DVFS techniques, where the clock frequency of physical resources can be changed. For each allocation problem and each energy model, we prove deterministic approximation ratios on the consumed energy for algorithms that provide guaranteed probability failures, as well as an efficient heuristic, whose energy ratio is not guaranteed

Macromodeling and characterization of filesystem energy consumption for diskless embedded systems

The use and application of embedded systems in everyday life has proliferated in the past few years. These systems are constrained in terms of power consumption, available memory and processing requirements. Typical embedded systems like handheld devices, cell phones, single board computer based systems are diskless and use flash for secondary storage. The choice of filesystem for these diskless systems can greatly impact the performance and the energy consumption of the system as well as lifetime of flash. In this thesis work, the energy consumption of flash based filesystems has been characterized. Both the processor and flash energy consumption are characterized as a function of filesystem specific operations. The work is aimed at helping a system designer compare and contrast different filesystems based on energy consumption as a metric. The macromodel can be used to characterize and estimate the energy consumption of applications due to filesystem running on flash. The study is done on a StrongARM based processor running Linux. Two of the popular filesystems JFFS2 and ext3 are profiled