Energy Efficient Storage Management Cooperated with Large Data Intensive Applications

An Internet of Things based energy efficiency monitoring and management system for machining workshop

6 - The energy efficiency management system on ships using internet of things technology for reducing environmental pollution: a case study of a bulk carrier

This paper describes more efficient home energy management system to reduce power consumption in home area. We consider the room easily controllable with an IR remote control of a home appliance. The room has power outlets, a light, and a ZigBee hub. The ZigBee hub has an IR code learning function and educates the IR remote control signal of a home appliance connected to the power outlet. Then it can control the power outlets and the light in the room. The ZigBee hubs in each room communicate with the home server and report the power consumption information to the home server. The proposed architecture gives more efficient energy-saving HEMS.

Critical review on structural architecture, energy control strategies and development process towards optimal energy management in hybrid vehicles

Exploring energy efficiency prediction method for Industry 4.0: a reconfigurable vibrating screen case study

In this paper we approach the problem of data centers' high energy consumption by proposing a prediction based controller for optimal and energy efficient allocation of the hardware computing resources to the incoming workload. The controller analyzes the incoming workload virtual tasks' requests for hardware computing resources and classifies them in predefined tasks classes. For each class, the tasks' arrival rates are computed and the next period workload is predicted using a neural network. An optimal green data center situation is determined, in which all the predicted virtual tasks are accommodated and the hardware computing resources utilization is minimized. By comparing the current and predicted data center situations, the controller decides and constructs an adaptation action plan to accommodate the incoming workload in an energy efficient manner. The controller implementation is tested on a real test case data center and significant energy savings are reported.

Towards efficient energy management in smart grids considering microgrids with day-ahead energy forecasting

Energy efficiency is a major issue in Cloud computing infrastructure. The large power consumption is mainly attributed to the large number of modern data centers operating within. Developing these data centers includes dynamically expanding their infrastructures to meet the ever-increasing demand for huge computation, large storage, and massive communication. Energy conservation through optimization of resources and management policies in the Cloud are a viable solution. Using virtualization to save power and employing such practices as using Virtual Machines (VMs), Server Consolidation, and VM Live Migration. This paper investigates the opportunities for Green Cloud Computing (GCC) to obtain a more comprehensive prospect towards achieving energy efficient Cloud Computing, and presents an energy efficient network resources management approach in an Infrastructure as a Service (IaaS) Cloud model. We focus on developing an energy efficient algorithm by proposing a practical multi-level Cloud Resource-Network Management (CRNM) algorithm, which is implemented in a virtual Cloud environment using Snooze framework as the Cloud energy efficiency manager. The optimization focuses on the utilization of network bandwidth as main resource under test, while also taking into account the other resources, such as CPU and memory, to get the desired performance. We choose a fat tree topology as a common three tier architecture for Cloud data canters. We conclude that our proposed algorithm will save up to 75% of power consumption in Cloud data centers, with an observed increase in efficiency compared to Non-Power Aware (NPA), Power aware(PA), and Greedy algorithms, where network elements consume about 30% of the total power of Cloud data centers.

With the development of smart grid technology, power energy efficiency evaluation will be driven to become more intelligent and professional. In order to achieve energy conservation and low-carbon economy, energy efficiency technologies need to be improved. The application of energy efficient technologies has to be enhanced from the supply side, power grid side and demand-side in order to raise energy efficiency effects and update energy conservation technologies. The key technologies, system architecture and advanced applications of demand-side power efficiency evaluation and management system were described in detail in this paper. The monitoring model, efficiency analysis, and optimized control of energy efficiency management system were also elaborated with case studies. The impact and prospect of this technology were finally analyzed.

International audience

In this paper, an efficient and robust power and energy management for large clusters of plug‐in electric vehicles (PEVs) and distribution networks is proposed. The method aims to minimise the charging cost of large clusters of PEVs in real time while ensuring distribution network normal operation and satisfying a large number of constraints from PEV level up to distribution network. The design of the method ensures very low dependence on forecast errors of critical quantities such as electricity price while it can be easily integrated with conventional optimal power flow algorithms. To this end, innovative virtual differential operation costs are assigned to clusters of PEVs. Moreover, an innovative definition of the flexibility of a cluster of PEVs to change its power is introduced while a simple idea based on the principle of the selection of the fittest is used to achieve efficient power dispatch to the PEVs with minimal computational requirements. The efficiency and the robustness of the proposed method are proved by detailed simulations of several operation scenarios of a realistic distribution network with large penetration of PEVs and renewable energy sources.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Hard Disk Drive (HDD) based storage systems in high performance computing (HPC), such as Lustre, are being confronted with great challenges from massive parallel data-intensive applications, and becomes one of the most critical factors that influence applications performance. In this paper, we analyze the performance of Lustre, and statistically examine the distribution of file sizes, I/O patterns for typical data-intensive applications, as well as the utilization of CPU and memory resources in TH-1A supercomputer from the last five years. We present masFS, a novel file system for HPC that exploits available memory and SSD resources on compute nodes with little interference to applications running on the nodes. The masFS supports POSIX interface with an unique namespace and provides comprehensive file service for applications. We design and implement masFS with memory and SSD, and have deployed and evaluated it on TH-1A. Experimental results show that masFS works as general storage system and can meet the requirement of variety of I/O loads. It can achieve a speedup of 14.5x for read and 8.8x for write respectively in running benchmarks, and an average 7.6x faster I/O time in a real-world data-intensive application compared to Lustre.

Energy efficiency management system (EEMS)provides effective energy saving measures and high quality energy conservation services; it will achieve the goal of energy conservation through system integration, concordance and optimization to the existing energy, which is particularly important in today's situation.Firstly, this paper analyzes the present situation of EEMS in China and other countries, and summarizes the characteristics of the existing methods.Secondly, the combination of CBR (Case-Based Reasoning, CBR) and MAS (Multi-agent System, MAS) method is proposed to solve the existing problems in energy efficiency management.This paper introduces AI (Artificial Intelligence, AI) to the design which will provide various solutions to intelligent energy consumptions now and in the future.Finally, the user feedback link is added into system, user's comfort becomes the vital factor in simulation.Users will back-feed their suggestions to the system, thus providing personalized services in energy saving.

Large-scale data centers leverage virtualization technology to achieve excellent resource utilization, scalability, and high availability. Ideally, the performance of an application running inside a virtual machine (VM) shall be independent of co-located applications and VMs that share the physical machine. However, adverse interference effects exist and are especially severe for data-intensive applications in such vir-tualized environments. In this work, we present TRACON, a novel Task and Resource Allocation CONtrol framework that mitigates the interference effects from concurrent data-intensive applications and greatly improves the overall system performance. TRACON utilizes modeling and control techniques from statistical machine learning and consists of three major components: the interference prediction model that infers application performance from resource consumption observed from different VMs, the interference-aware scheduler that is designed to utilize the model for effective resource management, and the task and resource monitor that collects application characteristics at the runtime for model adaption. We simulate TRACON with a wide variety of data-intensive applications including bioinformatics, data mining, video processing, email and web servers, etc. The evaluation results show that TRACON can achieve up to 50% improvement on application runtime, and up to 80% on I/O throughput for data-intensive applications in virtu-alized data centers.