Adel Noureddine

A preliminary study of the impact of software engineering on GreenIT

GreenIT has emerged as a discipline concerned with the optimization of software solutions with regards to their energy consumption. In this domain, most of state-of-the-art solutions offer limited or constraining approaches to monitor the energy consumption of a device or a process. In this paper, we therefore report on a runtime energy monitoring framework we developed to easily report on the energy consumption of system processes. Concretely, our approach adopts an OS-level library, called PowerAPI, which estimates the power consumption of processes according to different dimensions (CPU, network, etc.). In order to better understand potential energy leaks of legacy software, we use this library to study the impact of programming languages and algorithmic choices on the energy consumption. This preliminary study is based on an empirical evaluation of a eight implementations of the Towers of Hanoi problem.

A review of energy measurement approaches

Reducing the energy footprint of digital devices and software is a task challenging the research in Green IT. Researches have proposed approaches for energy management, ranging from reducing usage of software and hardware, compilators optimization, to server consolidation and software migration. However, optimizing the energy consumption requires knowledge of that said consumption. In particular, measuring the energy consumption of hardware and software is an important requirement for efficient energy strategies. In this review, we outline the different categories of approaches in energy measurements, and provide insights into example of each category. We draw recommendations from our review on requirements on how to efficiently measure energy consumption of devices and software.

Runtime monitoring of software energy hotspots

GreenIT has emerged as a discipline concerned with the optimization of software solutions with regards to their energy consumption. In this domain, most of the state-of-the-art solutions concentrate on coarse-grained approaches to monitor the energy consumption of a device or a process. However, none of the existing solutions addresses in-process energy monitoring to provide in-depth analysis of a process energy consumption. In this paper, we therefore report on a fine-grained runtime energy monitoring framework we developed to help developers to diagnose energy hotspots with a better accuracy than the state-of-the-art. Concretely, our approach adopts a 2-layer architecture including OS-level and process-level energy monitoring. OS-level energy monitoring estimates the energy consumption of processes according to different hardware devices (CPU, network card). Process-level energy monitoring focuses on Java-based applications and builds on OS-level energy monitoring to provide an estimation of energy consumption at the granularity of classes and methods. We argue that this per-method analysis of energy consumption provides better insights to the application in order to identify potential energy hotspots. In particular, our preliminary validation demonstrates that we can monitor energy hotspots of Jetty web servers and monitor their variations under stress scenarios.

Monitoring energy hotspots in software

Green IT has emerged as a discipline concerned with the optimization of software solutions with regards to their energy consumption. In this domain, most of the state-of-the-art solutions concentrate on coarse-grained approaches to monitor the energy consumption of a device or a process. In this paper, we report on a fine-grained runtime energy monitoring framework we developed to help developers to diagnose energy hotspots with a better accuracy. Concretely, our approach adopts a two-layer architecture including OS-level and process-level energy monitoring. OS-level energy monitoring estimates the energy consumption of processes according to different hardware devices (CPU, network card). Process-level energy monitoring focuses on Java-based applications and builds on OS-level energy monitoring to provide an estimation of energy consumption at the granularity of classes and methods. We argue that this per-method analysis of energy consumption provides better insights to the application in order to identify potential energy hotspots. In particular, our preliminary validation demonstrates that we can monitor energy hotspots of Jetty web servers and monitor their variations under stress scenarios.

Optimising energy consumption of design patterns

Software design patterns are widely used in software engineering to enhance productivity and maintainability.However, recent empirical studies revealed the high energy overhead in these patterns. Our vision is to automatically detect and transform design patterns during compilation for better energy efficiency without impacting existing coding practices. In this paper, we propose compiler transformations for two design patterns, Observer and Decorator, and perform an initial evaluation of their energy efficiency.

A Review of Middleware Approaches for Energy Management in Distributed Environments

Energy management solutions and approaches for computer systems are becoming broadly available as energy concerns are becoming mainstream. Many approaches have been proposed to manage the energy consumption of the hardware, operating system, or software layers. The widespread usage of ubiquitous devices and the high coverage of networks (Wi‐Fi and 3G) have led to a new generation of communicating and mobile devices that uses complex middleware platform functionalities. Therefore, energy management has emerged as a topic of research interest in the middleware layer, and solutions specific to this layer are proposed along the more traditional ones existing at the other levels. In this article, we report on a review of state‐of‐the‐art approaches for energy management middleware platforms. This article defines also an architectural taxonomy and compares existing approaches on the basis of this taxonomy. In particular, we review middleware platforms and detail a number of approaches where energy management is handled. Finally, we review application scenarios where the energy management concepts at the middleware layer are applied in intelligent environments. Copyright

Data Center Energy Demand: What Got Us Here Won't Get Us There

Given environmentalisms rising tide and increasing energy prices and IT workloads, architects must determine whether they can continue designing systems without considering energy and power efficiency.

Supporting energy-driven adaptations in distributed environments

The rise of the usage of digital devices and software services contribute to the increase of energy consumption of IT infrastructures. However, energy is still largely produced by limited resources. Therefore, optimizing and reducing its consumption is an economic and human necessity. Related works addressing energy optimization in computer science are widespread, but at the middleware layer, existing approaches are limited in their scope, adaptability and autonomous functioning. In this paper, we propose the foundations of a middleware architecture capable of handling various types of energy techniques and in different contexts. The distributed nature of our approach fits in a ubiquitous environment and covers the energy dimensions of both devices and software services. We also present the experimental results of a prototype implementing a subset of our proposed architecture. These results illustrate the potential of our energy-aware and autonomous approach.

A Study on the Influence of Software and Hardware Features on Program Energy

Software energy consumption has emerged as a growing concern in recent years. Managing the energy consumed by a software is, however, a difficult challenge due to the large number of factors affecting it -- namely, features of the processor, memory, cache, and other hardware components, characteristics of the program and the workload running, OS routines, compiler optimisations, among others. In this paper we study the relevance of numerous architectural and program features (static and dynamic) to the energy consumed by software. The motivation behind the study is to gain an understanding of the features affecting software energy and to provide recommendations on features to optimise for energy efficiency. In our study we used 58 subject desktop programs, each with their own workload, and from different application domains. We collected over 100 hardware and software metrics, statically and dynamically, using existing tools for program analysis, instrumentation and run time monitoring. We then performed statistical feature selection to extract the features relevant to energy consumption. We discuss potential optimisations for the selected features. We also examine whether the energy-relevant features are different from those known to affect software performance. The features commonly selected in our experiments were execution time, cache accesses, memory instructions, context switches, CPU migrations, and program length (Halstead metric). All of these features are known to affect software performance, in terms of running time, power consumed and latency.

Measuring energy footprint of software features

With the proliferation of Software systems and the rise of paradigms such the Internet of Things, Cyber-Physical Systems and Smart Cities to name a few, the energy consumed by software applications is emerging as a major concern. Hence, it has become vital that software engineers have a better understanding of the energy consumed by the code they write. At software level, work so far has focused on measuring the energy consumption at function and application level. In this paper, we propose a novel approach to measure energy consumption at a feature level, cross-cutting multiple functions, classes and systems. We argue the importance of such measurement and the new insight it provides to non-traditional stakeholders such as service providers. We then demonstrate, using an experiment, how the measurement can be done with a combination of tools, namely our program slicing tool (PORBS) and energy measurement tool (Jolinar).