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Background. Often motivated by optimization objectives, software products are characterized by different subsequent releases and deployed through different strategies. The impact of these two aspects of software on energy consumption has still to be completely understood and can be improved by carrying out ad-hoc analyses for specific software products. Aims. In this research we report on an industrial...
The energy consumption in residential section accounts for over 30% of the world and cost-effective indoor energy management is important. Visible light communication based on variable pulse position modulation (VPPM) is a scheme for efficient and convenient indoor energy management. This paper proposes methods for detecting dimmable packet. First, we describe a method based on correlation bank expanding...
Honeynet is deployed to trap attackers and learn their behavior patterns and motivations. Conventional honeynet is implemented by dedicated hardware and software. It suffers from inflexibility, high CAPEX and OPEX. There have been several virtualized honeynet architectures to solve those problems. But they lack a standard operating environment and common architecture for dynamic scheduling and adaptive...
Green building plays an important role in the development of the construction industry. The key issue of green building is how to operate itself. This paper presents a building management cloud platform for green buildings. Its goal is to realize building operation management by using the technologies of cloud computing and Internet of things. The cloud servers provide data storage, computing and...
Today, artificial neural networks (ANNs) are widely used in a variety of applications, including speech recognition, face detection, disease diagnosis, etc. And as the emerging field of ANNs, Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) which contains complex computational logic. To achieve high accuracy, researchers always build large-scale LSTM networks which are time-consuming...
Inefficient mobile software kills battery life. Yet, developers lack the tools necessary to detect and solve energy bugs in software. In addition, developers are usually tasked with the creation of software features and triaging existing bugs. This means that most developers do not have the time or resources to research, build, or employ energy debugging tools.We present a new method for predicting...
In the big data era, data-intensive applications have growing demand for the capacity of DRAM main memory, but the frequent DRAM refresh, high leakage power, and high unit cost bring serious design issues on scaling up DRAM capacity. To address this issue, NVDIMM, which is a hybrid memory module, becomes a possible alternative to replace DRAM as main memory in some data-intensive applications. NVDIMM...
Surrounding autonomous embedded devices are in a constant expansion. The advent and the rise of Internet of Things (IoT) enable these objects to take a giant step forward, especially regarding their large scale deployment in real-world applications of the everyday life. A significant part of these objects are battery-powered and energy-dependent. Thus, energy is a critical resource which greatly complicates...
Modern high performance computing and cloud computing infrastructures often leverage Graphic Processing Units (GPUs) to provide accelerated, massively parallel computational power. This performance gain, however, may also introduce higher energy consumption. The energy challenge has become more and more pronounced when the system scales. To address this challenge, we propose Archon, a framework for...
With the development of GPU and GPGPU, GPU heterogeneous cluster has been widely used in parallel data processing in data center. In data green and sustainable computing, power consumption is a problem worthy of consideration. Power consumption may cause temperature rise and the problems of reliability and performance will probably occur. So temperature management and controlling has become a new...
Deep convolutional neural networks (CNNs) are indispensable to state-of-the-art computer vision algorithms. However, they are still rarely deployed on battery-powered mobile devices, such as smartphones and wearable gadgets, where vision algorithms can enable many revolutionary real-world applications. The key limiting factor is the high energy consumption of CNN processing due to its high computational...
Energy consumption is one of the most important aspects of mobile apps. During energy testing, it is important for developers to understand not only the energy consumption rate of an app, but also why energy is consumed. However, existing energy testing tools are more concerned about the accuracy of energy estimation, while typically not providing explanations on why and how exactly energy has been...
The Internet of Things has created a need for embedded devices that, despite being battery powered, can perform complex algorithms when needed. To fulfil this need we present the Elastic Node, a hardware platform that combines embedded field programmable gate arrays (FPGAs) with conventional microcontroller units (MCUs). These nodes can adapt their processing resources when their requirements change...
We propose a kernel-level energy profiling tool KLEP that can work with diverse APIs of Android. KLEP addresses the challenges of the tail energy problem and the complex interrelation between hardware components in the device energy consumption profile. KLEP collects energy-sensitive events in the kernel and measures real energy consumption of the device at the same time, and employs a LSTM neural-network-based...
Even though the concept of Demand Response (DR) is not really widespread across Romania yet, the Technical University of Cluj-Napoca (TUCN) is currently implementing a pilot project in its own buildings within the Horizon 2020 Project named DR-BoB (project no. 696114/2016). The main purpose of this pilot project is to develop an energy monitoring and targeting system, along with a DR program in load...
The huge energy consumption in data centers produces not only high electricity bill but also tremendous carbon footprints. Although today's servers and data centers of leading internet companies are more energy efficient than ever before, the fluctuations in external workload and internal resource utilization calls for energy proportional computing. Insight into server energy proportionality can help...
Energy saving management in multi-core embedded environments has been a challenge for designers. To achieve energy efficiency, most studies consider dynamic frequency scaling on one hardware component only, such as processor or memory — which will most likely also affect performance. This work proposes the use of frequency scaling considering the three most important hardware components altogether:...
Computer vision enables a wide range of applications in robotics/drones, self-driving cars, smart Internet of Things, and portable/wearable electronics. For many of these applications, local embedded processing is preferred due to privacy and/or latency concerns. Accordingly, energy-efficient embedded vision hardware delivering real-time and robust performance is crucial. While deep learning is gaining...
The need for performance and energy efficiency in mobile devices is apparent with the obvious shifting of more intensive computation to mobile platforms. In this paper, we first make a clear distinction between performance and energy issues. Apart from showing that performance efficiency is neither co-related with energy-efficiency nor inefficiency, we focus on programming methodologies and software...
This paper addresses energy efficient VNF placement and chaining over NFV enabled infrastructures. VNF placement and chaining are formulated as a decision tree search to overcome this NP-Hard problem complexity. The proposed approach is an extension of the Monte Carlo Tree Search(MCTS) method to achieve energy savings using physical resourceconsolidation and sharing VNFs between multiple tenants....
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