一种基于QoS参数归约的云计算环境能效评估方法

高性能、低功耗且具有QoS保障的高能效问题是云计算领域的一个研究难点。目前的研究主要是通过限定一个约束条件寻求另外指标的最优来实现三者之间的折衷或均衡,缺乏一种有效的能效计算方法和评估模型将三者整合,以更好地描述云环境能效的“程度”。提出一种云环境下QoS参数的归约方法和加权的能效模型,把系统性能作为一个关键因素引入QoS,并将离散的多个QoS参数度量值归约到同一个量纲区域内,获得评价权重矩阵,求得用户最终的QoS评价值,以单位能耗所提供的整体QoS水平值作为能效值,并且建立云数据中心的能效分级标识,最终将云环境下能效值刻画为一个定性的概念,实现了对云环境下能效的定性评估。此外,分别对单机环境和同构、异构的云计算环境中云数据中心的能效进行了评估分析,并进行了实验验证。实验结果表明,所提出的能效模型和评估方法在评价云系统的QoS水平和能源消耗方面是有效的。     

Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm

The real-world building can be regarded as a comprehensive energy engineering system; its actual energy consumption depends on complex affecting factors, including various weather data and time signature. Accurate energy consumption forecasting and effective energy system management play an essential part in improving building energy efficiency. The multi-source weather profile and energy consumption data could enable integrating data-driven models and evolutionary algorithms to achieve higher forecasting accuracy and robustness. The proposed building energy consumption forecasting system consists of three layers: data acquisition and storage layer, data pre-processing layer and data analytics layer. The core part of the data analytics layer is a hybrid genetic algorithm (GA) and long-short term memory (LSTM) neural network model for accurate and robust energy prediction. LSTM neural network is adopted to capture the interrelationship between energy consumption data and time. GA is adopted to select the optimal architecture for LSTM neural networks to improve its forecasting accuracy and robustness. The hyper-parameters for determining LSTM architecture include the number of LSTM layers, number of neurons in each LSTM layer, dropping rate of each LSTM layer and network learning rate. Meanwhile, the effects of historical weather profile and time horizon of past information are also investigated. Two real-life educational buildings are adopted to test the performance of the proposed building energy consumption forecasting system. Experiments reveal that the proposed adaptive LSTM neural network performs better than the existing feedforward neural network and LSTM-based prediction models in accuracy and robustness. It also outperforms those LSTM networks whose hyper-parameters are determined by grid search, Bayesian optimisation and PSO. Such accurate energy consumption prediction can play an essential role in various areas, including daily building energy management, decision making of facility managers, building information model designs, net-zero energy operation, climate change mitigation and circular economy.     

TB级多源遥感影像高效建库方法研究

随着各领域对多源遥感影像数据需求的不断增加,如何高效地管理多源海量遥感影像数据,更好地为各行各业服务是遥感数据库研究的热点。目前国内外很多学者在建设多源遥感影像数据库方面进行了相关研究,但对多源遥感影像数据组织、遥感影像数据预处理以及快速索引方面的研究尚存在不足。设计了一种多源遥感影像数据组织模型,实现了多源影像的集成化管理;实现了一种多源海量原始遥感影像(特别是航空影像)的半自动预处理机制,极大地缩短了原始影像数据的处理时间;设计了一种混合检索方法,达到了海量多源遥感影像数据快速高效检索的目的。结果表明:该方法建库效率显著提高,混合索引的查询速度比ArcSDE自动创建的空间索引检索速度有了快速提高,在数据量较小时查询速度至少可以提高4倍,而数据量较大时检索速度至少可以提高12.83倍。     

Energy consumption analysis of data stream processing: a benchmarking approach

Energy efficiency of data analysis systems has become a very important issue in recent times because of the increasing costs of data center operations. Although distributed streaming workloads have increasingly been present in modern data centers, energy‐efficient scheduling of such applications remains as a significant challenge. In this paper, we conduct an energy consumption analysis of data stream processing systems in order to identify their energy consumption patterns. We follow stream system benchmarking approach to solve this issue. Specifically, we implement Linear Road benchmark on six stream processing environments (S4, Storm, ActiveMQ, Esper, Kafka, and Spark Streaming) and characterize these systems' performance on a real‐world data center. We study the energy consumption characteristics of each system with varying number of roads as well as with different types of component layouts. We also use a microbenchmark to capture raw energy consumption characteristics. We observed that S4, Esper, and Spark Streaming environments had highest average energy consumption efficiencies compared with the other systems. Using a neural networkbased technique with the power/performance information gathered from our experiments, we developed a model for the power consumption behavior of a streaming environment. We observed that energy‐efficient execution of streaming application cannot be specifically attributed to the system CPU usage. We observed that communication between compute nodes with moderate tuple sizes and scheduling plans with balanced system overhead produces better power consumption behaviors in the context of data stream processing systems. Copyright © 2016 John Wiley & Sons, Ltd.     

An integrated processing energy modeling and optimization of automated robotic polishing system

The automated robotic polishing system (ARPS) consisting of several robotic polishing cells (RPCs) is widely adopted in polishing industry to replace manual labor. Recently, energy-saving becomes a hotspot issue in manufacturing industry because of the increase in energy costs and requirement of environmental protection. Traditionally, robot motion planning and task scheduling are carried out separately and sequentially, which constrain the potential for energy-saving. In this paper, a task energy characteristic model is proposed as a polynomial function of the feedrate override to forecast the energy consumption of the polishing process of RPC, in which the designed parameters of the RPC and the polishing process parameters are encapsulated into the polynomial coefficients based on experimental data. Furthermore, an optimization model is proposed for an ARPS with mass tasks to minimize the energy consumption, in which the robot motion planning and the task scheduling are considered integratedly. An adaptive genetic algorithm with elite retention strategy is adopted to solve the optimization model. A case study is introduced to verify the proposed approach, which demonstrates the forecast error of task energy is less than 7%, and the proposed optimization approach can reduce the energy consumption of ARPS by more than 18% compared with the original processing scheme.     

A bi-level school bus routing problem with bus stops selection and possibility of demand outsourcing

This paper develops a bi-level mathematical model for the school bus routing problem aiming at designing an efficient transportation system considering the possibility of predicting the students’ response. In the real world, the demand for using private cars depends on how well public transportation systems are operating especially in metropolitan cities. An inefficient public transportation will lead to an increase in the demand for using private cars. This issue will result in problems such as increased traffics and urban pollutions. To address this issue, an efficient public transportation system is designed by developing a new bi-level mathematical model. In the proposed model, the designer of the public transportation system, as the upper-level decision-maker, will locate appropriate bus stops and identify bus navigation routes. Subsequently, the decision regarding the allocation of students to transportation systems or outsourcing them will be made at the lower level which is considered as an operational-level decision-making. To solve this problem, two hybrid metaheuristic approaches named GA-EX-TS and SA-EX-TS have been proposed based on location-allocation-routing (LAR) strategy. The performance of these proposed methods is compared with exact solutions achieved from an explicit enumeration approach followed in the small-scale instances. Finally, the proposed approaches are used to solve 50 random instance problems. Comparing the results of the two tuned hybrid algorithms and conducting the sensitivity analysis of the model provide evidence for the good performance of the proposed approach.     

Hybrid Fuzzy Modelling for Model Predictive Control

Model predictive control (MPC) has become an important area of research and is also an approach that has been successfully used in many industrial applications. In order to implement a MPC algorithm, a model of the process we are dealing with is needed. Due to the complex hybrid and nonlinear nature of many industrial processes, obtaining a suitable model is often a difficult task. In this paper a hybrid fuzzy modelling approach with a compact formulation is introduced. The hybrid system hierarchy is explained and the Takagi–Sugeno fuzzy formulation for the hybrid fuzzy modelling purposes is presented. An efficient method for identifying the hybrid fuzzy model is also proposed. A MPC algorithm suitable for systems with discrete inputs is treated. The benefits of the MPC algorithm employing the hybrid fuzzy model are verified on a batch-reactor simulation example: a comparison between the proposed modern intelligent (fuzzy) approach and a classic (linear) approach was made. It was established that the MPC algorithm employing the proposed hybrid fuzzy model clearly outperforms the approach where a hybrid linear model is used, which justifies the usability of the hybrid fuzzy model. The hybrid fuzzy formulation introduces a powerful model that can faithfully represent hybrid and nonlinear dynamics of systems met in industrial practice, therefore, this approach demonstrates a significant advantage for MPC resulting in a better control performance.     

Improving neighborhood based Collaborative Filtering via integrated folksonomy information

Personalized recommender systems which can provide people with suggestions according to individual interests usually rely on Collaborative Filtering (CF). The neighborhood based model (NBM) is a common choice when implementing such recommenders due to the intuitive nature; however, the recommendation accuracy is a major concern. Current NBM based recommenders mostly address the accuracy issue based on the rating data alone, whereas research on hybrid recommender systems suggests that users enjoy specifying feedback about items across multiple dimensions. In this work we aim to improve the accuracy of NBM via integrating the folksonomy information. To achieve this objective, we first propose the folksonomy network (FN) to analyze the item relevance described by the folksonomy data. We subsequently integrate the obtained folksonomy information into the global-optimization based NBM for making multi-source based recommendations. Experiments on the MovieLens dataset suggest positive results, which prove the efficiency of our strategy.     

Variable weights combined model based on multi-objective optimization for short-term wind speed forecasting

Accurate and steady wind speed prediction is essential for the efficient management of wind power factories and energy systems. However, it is difficult to obtain satisfactory forecasting performance because of the characteristics of random nonlinear fluctuations inherent in wind speed variation. Considering the drawbacks of statistical models in forecasting nonlinear time series and the problem of artificial intelligence models easily falling into a local optimum, in this study, we successfully integrate the variable weighted combination theory into a new combined forecasting model that simultaneously consists of three disparate hybrid models based on the decomposition technology. Moreover, the extreme learning machine optimized by the multi-objective grasshopper optimization algorithm is adopted to integrate all the forecasting results derived from each hybrid model to further enhance the forecasting accuracy. In this study, we consider a case study that employs several authentic wind speed data aggregates of Shandong wind farms for an evaluation of the forecasting performance of the proposed combined model. The experimental results reveal that this proposed model surpasses the contrasted benchmark models and is satisfactory for intellective grid programs.     

Power flow Petri Net modelling for building integrated multi-source power system with smart grid interaction

This paper presents an energy management modelling of a multi-source power system composed of photovoltaic (PV) array, storage and power grid connection, and taking into account messages from smart grid. The designed system can supply a tertiary building at the same time as PV may produce energy. The control strategy aims to manage the power flow through the load with respect to its power demand and public grid constraints. The proposed energy management modelling is based on interpreted Petri Nets formalism. The system is tested using a simulation Stateflow model and responds within certain limits. The results show that this approach is valid and can be a solution for the future smart grid communication between buildings and public grid and may contribute to a better balance between production and consumption and future energy management.     

Dynamic buffer management using optimal control of hybrid systems

This paper studies a dynamic buffer management problem with one buffer inserted between two interacting components. The component to be controlled is assumed to have multiple power modes corresponding to different data processing rates. The overall system is modeled as a hybrid system and the buffer management problem is formulated as an optimal control problem. Different from many previous studies, the objective function of the proposed problem depends on the switching cost and the size of the continuous state space, making its solution much more challenging. By exploiting some particular features of the problem, the best mode sequence and the optimal switching instants are characterized analytically using a variational approach. Simulation results based on real data shows that the proposed method can significantly reduce the energy consumption compared with another heuristic scheme in several typical situations.     

Coupling geometric analysis and viability theory for system exploration: Application to a living food system

This paper addresses the issue of studying a food complex system in a reverse engineering manner with the aim of identifying the set of all possible actions that makes it reach a quality target with respect to manufacturing constraints. Once the set of actions is identified, several criteria can be considered to identify interesting trajectories and control policies. A viability approach, coupling the viability theory and a geometric approach of robustness, is proposed to study complex dynamical systems. It can be implemented for several types of systems, from linear to non linear or hybrid systems. The proposed framework was adapted to a living food system: a ripening model of Camembert cheese to identify the set of states and actions (capture basin) from which it is possible to reach a predefined quality target. Within the set of viable trajectories, particular trajectories that improve the Camembert cheese ripening process are identified using the proposed approach. The results are applied at a pilot scale and are discussed in this paper.     

Context driven process selection and integration of mobile and pervasive systems

Current mobile or pervasive systems possess ever increasing computing powers. A tight integration of those systems into business processes is therefore desirable. However, this requires consideration of the changing context of those systems especially when determining adequate processes for a system.This paper introduces a novel context driven approach to select processes suitable for a system’s context and to integrate the system into the chosen processes. An infrastructure to implement the context driven approach is proposed and a prototype is given. A flexible implementation is accomplished using a hybrid context model, rule engines and OSGi.     

A hybrid multi-criteria decision-making model for personnel selection in manufacturing systems

This study described a hybrid model for supporting the personnel selection process in manufacturing systems. Personnel selection is a very important issue for an effective manufacturing system, since the improper personnel might cause many problems affecting productivity, precision, flexibility and quality of the products negatively. On the other hand, selecting the best personnel among many alternatives is a multi-criteria decision making (MCDM) problem. In this study, a hybrid model which employs analytic network process (ANP) and modified TOPSIS (Technique for Order Performance by Similarity to Idea Solution) together, is proposed for the personnel selection problem. The ANP is used to analyze the structure of the personnel selection problem and to determine weights of the criteria, and modified TOPSIS method is used to obtain final ranking. To illustrate how the approach is used for the personnel selection problem, an application of a real case in a company is conducted. The application has demonstrated the effectiveness and feasibility of the proposed model. Company management found the application and results satisfactory and implementable in their personnel selection process.     

数据仓库在城市公共建筑能耗管理中的应用

当前众多城市公共建筑能耗监测系统中已收集了大量的建筑能耗数据。针对这些数据源存在的各自独立而且分散,不能够提供全局的数据分析环境,不能够有效支持建筑能耗的评估与建筑节能的研究等问题,提出将数据仓库技术应用于城市公共建筑能耗监管系统的解决方法。通过对建筑能耗监测系统的研究,以及对建筑能耗管理的应用需求的调研,建立城市级公共建筑能耗数据仓库的多维数据模型,对主题设计、指标设计和维度模型设计进行了探讨,并在实验阶段已成功构建了某高校公共建筑能耗数据仓库的实例。实验结果表明,该方法能够有效地为建筑能耗的管理与决策提供良好的数据分析环境。     

A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems

In this paper, we investigate the problem of scheduling precedence-constrained parallel applications on heterogeneous computing systems (HCSs) like cloud computing infrastructures. This kind of application was studied and used in many research works. Most of these works propose algorithms to minimize the completion time (makespan) without paying much attention to energy consumption.We propose a new parallel bi-objective hybrid genetic algorithm that takes into account, not only makespan, but also energy consumption. We particularly focus on the island parallel model and the multi-start parallel model. Our new method is based on dynamic voltage scaling (DVS) to minimize energy consumption.In terms of energy consumption, the obtained results show that our approach outperforms previous scheduling methods by a significant margin. In terms of completion time, the obtained schedules are also shorter than those of other algorithms. Furthermore, our study demonstrates the potential of DVS.     

A novel hybrid DEPS optimized fuzzy PI/PID controller for load frequency control of multi-area interconnected power systems

In this paper, a novel hybrid Differential Evolution (DE) and Pattern Search (PS) optimized fuzzy PI/PID controller is proposed for Load Frequency Control (LFC) of multi-area power system. Initially a two-area non-reheat thermal system is considered and the optimum gains of the fuzzy PI/PID controller are optimized employing a hybrid DE and PS (hDEPS) optimization technique. The superiority of the proposed controller is demonstrated by comparing the results with some recently published modern heuristic optimization techniques such as DE, Bacteria Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA) and conventional Ziegler Nichols (ZN) based PI controllers for the same interconnected power system. Furthermore, robustness analysis is performed by varying the system parameters and operating load conditions from their nominal values. It is observed that the optimum gains of the proposed controller need not be reset even if the system is subjected to wide variation in loading condition and system parameters. Additionally, the proposed approach is further extended to multi-area multi-source power system with/without HVDC link and the gains of fuzzy PID controllers are optimized using hDEPS algorithm. The superiority of the proposed approach is shown by comparing the results with recently published DE optimized PID controller and conventional optimal output feedback controller for the same power systems. Finally, Reheat turbine, Generation Rate Constraint (GRC) and time delay are included in the system model to demonstrate the ability of the proposed approach to handle nonlinearity and physical constraints in the system model.     

Energy optimization with authentication and cost effective storage in the wireless sensor IoTs using blockchain

In this paper, a hybrid blockchain-based authentication scheme is proposed that provides the mechanism to authenticate the randomly distributed sensor IoTs. These nodes are divided into three types: ordinary nodes, cluster heads and sink nodes. For authentication of these nodes in a Wireless Sensor IoTs (WSIoTs), a hybrid blockchain model is introduced. It consists of both private and public blockchains, which are used to authenticate ordinary nodes and cluster heads, respectively. Moreover, to handle the issue of cluster head failure due to inefficient energy consumption, Improved Heterogeneous Gateway-based Energy-Aware Multi-hop Routing (I-HMGEAR) protocol is proposed in combination with blockchain. It provides a mechanism to efficiently use the overall energy of the network. Besides, the processed data of subnetworks is stored on blockchain that causes the issue of increased monetary cost. To solve this issue, an external platform known as InterPlanetary File System (IPFS) is used, which distributively stores the data on different devices. The simulation results show that our proposed model outperforms existing clustering scheme in terms of network lifetime and data storage cost of the WSIoTs. Our proposed scheme increases the lifetime of the network as compared to existing trust management model, intrusion prevention and multi WSN authentication schemes by 17.5%, 24.2% and 19.6%, respectively.