scholarly journals An Adaptive Routing Framework for Efficient Power Consumption in Software-Defined Datacenter Networks

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3027
Author(s):  
Mohammed Nsaif ◽  
Gergely Kovásznai ◽  
Anett Rácz ◽  
Ali Malik ◽  
Ruairí de Fréin
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Optimization Problems ◽  
Programming Model ◽  
Traffic Load ◽  
Computer Networking ◽  
Learning Approaches ◽  
Internet Applications ◽  
And Performance ◽  
Efficient Power

Data Center Networks (DCNs) form the backbone of many Internet applications and services that have become necessary in daily life. Energy consumption causes both economic and environmental issues. It is reported that 10% of global energy consumption is due to ICT and network usage. Computer networking equipment is designed to accommodate network traffic; however, the level of use of the equipment is not necessarily proportional to the power consumed by it. For example, DCNs do not always run at full capacity yet the fact that they are supporting a lighter load is not mirrored by a reduction in energy consumption. DCNs have been shown to unnecessarily over-consume energy when they are not fully loaded. In this paper, we propose a new framework that reduces power consumption in software-defined DCNs. The proposed approach is composed of a new Integer Programming model and a heuristic link utility-based algorithm that strikes a balance between energy consumption and performance. We evaluate the proposed framework using an experimental platform, which consists of an optimization tool called LinGo for solving convex and non-convex optimization problems, the POX controller and the Mininet network emulator. Compared with the state-of-the-art approach, the equal cost multi-path algorithm, the results show that the proposed method reduces the power consumption by up to 10% when the network is experiencing a high traffic load and 63.3% when the traffic load is low. Based on these results, we outline how machine learning approaches could be used to further improve our approach in future work.

scholarly journals Power and Performance Evaluation of Memory-Intensive Applications

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4089
Author(s):  
Kaiqiang Zhang ◽  
Dongyang Ou ◽  
Congfeng Jiang ◽  
Yeliang Qiu ◽  
Longchuan Yan
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Power Consumption ◽  
Job Scheduling ◽  
Memory System ◽  
Processor Core ◽  
Memory Efficiency ◽  
And Performance ◽  
Reasonable Use ◽  
Server System

In terms of power and energy consumption, DRAMs play a key role in a modern server system as well as processors. Although power-aware scheduling is based on the proportion of energy between DRAM and other components, when running memory-intensive applications, the energy consumption of the whole server system will be significantly affected by the non-energy proportion of DRAM. Furthermore, modern servers usually use NUMA architecture to replace the original SMP architecture to increase its memory bandwidth. It is of great significance to study the energy efficiency of these two different memory architectures. Therefore, in order to explore the power consumption characteristics of servers under memory-intensive workload, this paper evaluates the power consumption and performance of memory-intensive applications in different generations of real rack servers. Through analysis, we find that: (1) Workload intensity and concurrent execution threads affects server power consumption, but a fully utilized memory system may not necessarily bring good energy efficiency indicators. (2) Even if the memory system is not fully utilized, the memory capacity of each processor core has a significant impact on application performance and server power consumption. (3) When running memory-intensive applications, memory utilization is not always a good indicator of server power consumption. (4) The reasonable use of the NUMA architecture will improve the memory energy efficiency significantly. The experimental results show that reasonable use of NUMA architecture can improve memory efficiency by 16% compared with SMP architecture, while unreasonable use of NUMA architecture reduces memory efficiency by 13%. The findings we present in this paper provide useful insights and guidance for system designers and data center operators to help them in energy-efficiency-aware job scheduling and energy conservation.

scholarly journals THE EFFECT OF DATA-REUSE TRANSFORMATIONS ON MULTIMEDIA APPLICATIONS FOR APPLICATION SPECIFIC PROCESSORS

2014 ◽  
pp. 102-109
Author(s):  
N. Vassiliadis ◽  
A. Chormoviti ◽  
N. Kavvadias ◽  
S. Nikolaidis
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Multimedia Applications ◽  
Data Reuse ◽  
Instruction Set ◽  
Specific Instruction ◽  
Data Transfers ◽  
And Performance ◽  
And Storage ◽  
Application Specific

Multimedia applications are characterized by a high number of data transfers and storage operations. Appropriate transformations can be applied at the algorithmic level to improve crucial implementation characteristics. In this paper, the effect of data-reuse transformations on power consumption and performance of multimedia applications, realized on an Application Specific Instruction set Processor (ASIP), is examined. An ASIP for multimedia applications designed based on a complete methodology is used to evaluate this effect. Results prove the efficiency of the ASIP solution and indicate benefits from the use of the data-reuse transformations in terms of energy consumption and performance. Also, preliminary results from the exploitation of instruction buffering technique to reduce the energy consumption of the ASIP are presented.

scholarly journals A DEVELOPMENT OF OPTICAL NETWORK UNIT POWER CONSUMPTION MODEL CONSIDERING TRAFFIC LOAD EFFECT

2018 ◽  
Vol 80 (4) ◽  
Author(s):  
Arnidza Ramli ◽  
Nadiatulhuda Zulkifli ◽  
Auwalu Usman ◽  
Sevia Mahdaliza Idrus
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Optical Network ◽  
Sampling Rate ◽  
Access Network ◽  
Passive Optical Network ◽  
Traffic Load ◽  
Optical Network Unit ◽  
Load Effect ◽  
Consumption Model

Accurate and precise measurement of energy consumption for the deployment of fiber-to-the-home (FTTH) network using Gigabit passive optical network (GPON) is vital to the research community to develop models for the synthesis of energy-efficient protocols and algorithms for the access network. However, lack of power consumption measurement of optical network devices in the past has led to unrealistic and/or oversimplified model being used in simulations. Usually the access network devices are assumed always on and their consumption is both traffic and time independent. Therefore, in this paper we propose an experimentally-driven approach to i) characterize the Optical Network Unit (ONU) from the power consumption standpoint and ii) develop more accurate power consumption model for the ONU. We focus on ONU since it represents the main contributor to the energy consumption of optical access network. The real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a GPON network testbed. The measurement is limited to a maximum 100 Mbps data rate due to a limitation in the sampling rate and precision of the measurement device. However, validation has been done with theoretical power consumption model in order to prove the feasibility of the proposed model. Our measurements show that the power consumption of the ONU exhibits a linear dependence on the traffic in which the power consumption at idle mode is 11.51 W while in low power mode the power consumption is around 7.52 W.

scholarly journals Machine learning-based energy consumption modeling and comparison of H.264/AVC and google VP8 encoders

2021 ◽  
Vol 11 (2) ◽  
pp. 1303
Author(s):  
Yousef O. Sharrab ◽  
Mohammad Alsmirat ◽  
Bilal Hawashin ◽  
Nabil Sarhan
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Performance Metrics ◽  
Prediction Models ◽  
Video Encoding ◽  
Video Sequences ◽  
Learning Approaches ◽  
Video Encoder ◽  
Wide Range ◽  
And Performance

Advancement of the prediction models used in a variety of fields is a result of the contribution of machine learning approaches. Utilizing such modeling in feature engineering is exceptionally imperative and required. In this research, we show how to utilize machine learning to save time in research experiments, where we save more than five thousand hours of measuring the energy consumption of encoding recordings. Since measuring the energy consumption has got to be done by humans and since we require more than eleven thousand experiments to cover all the combinations of video sequences, video bit_rate, and video encoding settings, we utilize machine learning to model the energy consumption utilizing linear regression. VP8 codec has been offered by Google as an open video encoder in an effort to replace the popular MPEG-4 Part 10, known as H.264/AVC video encoder standard. This research model energy consumption and describes the major differences between H.264/AVC and VP8 encoders in terms of energy consumption and performance through experiments that are based on machine learning modeling. Twenty-nine raw video sequences are used, offering a wide range of resolutions and contents, with the frame sizes ranging from QCIF(176x144) to 2160p(3840x2160). For fairness in comparison analysis, we use seven settings in VP8 encoder and fifteen types of tuning in H.264/AVC. The settings cover various video qualities. The performance metrics include video qualities, encoding time, and encoding energy consumption.

scholarly journals Design Sleep Mode and Wakeup Method for OpenFlow Switches

2015 ◽  
pp. 10
Author(s):  
Tran Hoang Vu ◽  
Vu Cong Luc
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Performance Evaluations ◽  
Total Power ◽  
Sleep Mode ◽  
Total Power Consumption ◽  
Switch Mode ◽  
Reduce Energy Consumption ◽  
Management Modes ◽  
And Performance

In  this  paper,  we  present  a  design  and  an evaluation  of  two  power  management  modes  that reduce the  energy  consumption  of OpenFlow switches. First,  we  define  two  new  low  power  modes:  SLEEP PORT  and  SLEEP  SWITCH,  which  reduce   energy consumption   in  cases  where  packets  on  port  or switches  are  absent.  Second,  we  present  a  Wake  on LAN  (WOL)  method  for  OpenFlow  Switches  to  wake up  Ethernet  ports  or  the  whole  switch  from  inactive states.  Finally,  we  describe  our  design,  experimental results and  performance evaluations. Our results show that the control SLEEP PORT mode on a switch might save  about 9.8% power consumption per  port,  and  up to about 60% of total power consumption of the switch with SLEEP  SWITCH mode.  In  addition,  we  will implement  this  method  to  Openflow  Switch  bases  on NetFPGA- 10 Gigabit in the future.

scholarly journals Utilization of ICT and AI techniques in harnessing residential energy consumption for an energy-aware smart city: A review

2021 ◽  
Vol 8 (7) ◽  
pp. 50-66
Author(s):  
Mahmood et al. ◽  
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Information And Communication Technologies ◽  
Energy Management ◽  
Optimization Problems ◽  
Consumption Patterns ◽  
Management Systems ◽  
Residential Sector ◽  
Energy Management Systems ◽  
Home Energy Management

Fusion of Information and Communication Technologies (ICT) in traditional grid infrastructure makes it possible to share certain messages and information within the system that leads to optimized use of energy. Furthermore, using Computational Intelligence (CI) in the said domain opens new horizons to preserve electricity as well as the price of consumed electricity effectively. Hence, Energy Management Systems (EMSs) play a vital role in energy economics, consumption efficiency, resourcefulness, grid stability, reliability, and scalability of power systems. The residential sector has its high impact on global energy consumption. Curtailing and shifting load of the residential sector can result in solving major global problems and challenges. Moreover, the residential sector is more flexible in reshaping power consumption patterns. Using Demand Side Management (DSM), end users can manipulate their power consumption patterns such that electricity bills, as well as Peak to Average Ratio (PAR), are reduced. Therefore, it can be stated that Home Energy Management Systems (HEMSs) is an important part of ground-breaking smart grid technology. This article gives an extensive review of DSM, HEMS methodologies, techniques, and formulation of optimization problems. Concluding the existing work in energy management solutions, challenges and issues, and future research directions are also presented.

scholarly journals Mobility-Included DNN Partition Offloading from Mobile Devices to Edge Clouds

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 229
Author(s):  
Xianzhong Tian ◽  
Juan Zhu ◽  
Ting Xu ◽  
Yanjun Li
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Wireless Network ◽  
Deep Neural Networks ◽  
Mobile User ◽  
Computation Offloading ◽  
Long Latency ◽  
Total Latency ◽  
And Performance

The latest results in Deep Neural Networks (DNNs) have greatly improved the accuracy and performance of a variety of intelligent applications. However, running such computation-intensive DNN-based applications on resource-constrained mobile devices definitely leads to long latency and huge energy consumption. The traditional way is performing DNNs in the central cloud, but it requires significant amounts of data to be transferred to the cloud over the wireless network and also results in long latency. To solve this problem, offloading partial DNN computation to edge clouds has been proposed, to realize the collaborative execution between mobile devices and edge clouds. In addition, the mobility of mobile devices is easily to cause the computation offloading failure. In this paper, we develop a mobility-included DNN partition offloading algorithm (MDPO) to adapt to user’s mobility. The objective of MDPO is minimizing the total latency of completing a DNN job when the mobile user is moving. The MDPO algorithm is suitable for both DNNs with chain topology and graphic topology. We evaluate the performance of our proposed MDPO compared to local-only execution and edge-only execution, experiments show that MDPO significantly reduces the total latency and improves the performance of DNN, and MDPO can adjust well to different network conditions.

scholarly journals Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1055
Author(s):  
Qian Sun ◽  
William Ampomah ◽  
Junyu You ◽  
Martha Cather ◽  
Robert Balch
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
History Matching ◽  
Optimization Problems ◽  
Learning Technologies ◽  
Petroleum Engineering ◽  
Support Vector ◽  
Learning Approaches ◽  
Field Development ◽  
Proxy Models

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

scholarly journals Practical Model for Energy Consumption Analysis of Omnidirectional Mobile Robot

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1800
Author(s):  
Linfei Hou ◽  
Fengyu Zhou ◽  
Kiwan Kim ◽  
Liang Zhang
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Mobile Robot ◽  
Load Capacity ◽  
Working Environment ◽  
Energy Consumption Analysis ◽  
Mecanum Wheel ◽  
Save Energy ◽  
Energy Consumption Modeling ◽  
Robot Platform

The four-wheeled Mecanum robot is widely used in various industries due to its maneuverability and strong load capacity, which is suitable for performing precise transportation tasks in a narrow environment. While the Mecanum wheel robot has mobility, it also consumes more energy than ordinary robots. The power consumed by the Mecanum wheel mobile robot varies enormously depending on their operating regimes and environments. Therefore, only knowing the working environment of the robot and the accurate power consumption model can we accurately predict the power consumption of the robot. In order to increase the applicable scenarios of energy consumption modeling for Mecanum wheel robots and improve the accuracy of energy consumption modeling, this paper focuses on various factors that affect the energy consumption of the Mecanum wheel robot, such as motor temperature, terrain, the center of gravity position, etc. The model is derived from the kinematic and kinetic model combined with electrical engineering and energy flow principles. The model has been simulated in MATLAB and experimentally validated with the four-wheeled Mecanum robot platform in our lab. Experimental results show that the accuracy of the model reached 95%. The results of energy consumption modeling can help robots save energy by helping them to perform rational path planning and task planning.

scholarly journals High-Level Parallel Ant Colony Optimization with Algorithmic Skeletons

2021 ◽  
Author(s):  
Breno A. de Melo Menezes ◽  
Nina Herrmann ◽  
Herbert Kuchen ◽  
Fernando Buarque de Lima Neto
Keyword(s):  
Ant Colony Optimization ◽  
High Performance ◽  
Optimization Problems ◽  
Programming Model ◽  
Parallel Implementation ◽  
Ant Colony ◽  
Algorithmic Skeletons ◽  
Low Level ◽  
Programming Patterns ◽  
High Level

AbstractParallel implementations of swarm intelligence algorithms such as the ant colony optimization (ACO) have been widely used to shorten the execution time when solving complex optimization problems. When aiming for a GPU environment, developing efficient parallel versions of such algorithms using CUDA can be a difficult and error-prone task even for experienced programmers. To overcome this issue, the parallel programming model of Algorithmic Skeletons simplifies parallel programs by abstracting from low-level features. This is realized by defining common programming patterns (e.g. map, fold and zip) that later on will be converted to efficient parallel code. In this paper, we show how algorithmic skeletons formulated in the domain specific language Musket can cope with the development of a parallel implementation of ACO and how that compares to a low-level implementation. Our experimental results show that Musket suits the development of ACO. Besides making it easier for the programmer to deal with the parallelization aspects, Musket generates high performance code with similar execution times when compared to low-level implementations.

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