Energy-Aware on-chip virtual machine placement for cloud-supported cyber-physical systems

Recent trends in the design of cyber-physical systems (CPS) are moving towards heterogeneous multi-core architectures with cloud support. In this paper, we propose an energy-aware scheme for virtual machine placement in cloud-supported CPS with Network-on-Chip (NoC) architecture. We formulate the energy-aware on-chip virtual machine placement problem as an optimization problem, and design a heuristic scheme based on ant-colony optimization. We address problems of slow convergence speed and easily falling into stagnation in ant-colony algorithm by employing pheromone diffusion model that makes the proposed scheme more efficient. Simulation results show that our scheme achieves much higher energy efficiency compared with previous schemes with different network sizes and traffic models.     

Cutting the energy bills of Internet Service Providers and telecoms through power management: An impact analysis

The energy consumption of the Information and Communication Technology (ICT) sector has been increasing recently; this sector is estimated to account for 2% of the total energy consumption. An even more aggressively increasing trend is the volume of Internet traffic and the number of connected devices. Thus, reducing the energy needs of the Internet is recognised as one of the main challenges that the ICT sector will have to face in the near future to reduce its overall energy footprint. Introducing energy-efficient techniques, both at the device level and the network level, is required.The main goal of this work is to quantitatively evaluate the potential energy savings from applying energy-efficient techniques, while examining the trade-off between network performance and the achieved energy savings.We introduce a categorisation of the energy-aware design space, focusing on the existing techniques in the device data plane, and contribute an analytical framework to represent the impact of energy-aware technologies and solutions for network devices. Our energy profile model represents the diverse energy-aware states of the network devices and is applied over two reference scenarios, one of a large-scale Telco (Telecom Italia) and one of a medium size Internet Service Provider (GRNET), to evaluate the impact of each energy-aware technology and the energy savings potential at the Home, Access, Metro/Transport and Core parts of each network.The results show the estimates of energy savings exceed 60% in many cases, while maintaining the same quality of service as in the energy-agnostic case.     

云环境下超启发式能耗感知调度算法

能耗感知调度的研究对云计算数据中心的可持续发展有着重要意义。能耗感知调度是一个NP难的多目标优化问题,目前云环境下的任务调度算法较少考虑能耗问题,且不能实现对能耗的灵活管理,随机搜索算法是一种解决该问题的有效途径,但其计算开销大,收敛速度慢。将异构云环境下的能耗感知调度问题定义为一个带约束的问题,即在一定的完成时间下优化系统能耗,以实现对能耗的灵活管理。此外,提出了基于在线学习的超启发式算法（OLHH）,该算法结合电压调节技术,在设计了简单高效的启发式策略集的基础上,引进超启发式算法,并采用在线学习的方式跟踪启发式策略的表现,实现对启发式策略的合理管理,从而达到提高算法的收敛性能的目的。模拟实验表明,该算法能够实现系统能耗的灵活管理,且比传统的随机搜索算法有着更好的收敛性能。     

Energy-efficient job stealing for CPU-intensive processing in mobile devices

Mobile devices have evolved from simple electronic agendas and mobile phones to small computers with great computational capabilities. In addition, there are more than 2 billion mobile devices around the world. Taking these facts into account, mobile devices are a potential source of computational resources for clusters and computational Grids. In this work, we present an analysis of different schedulers based on job stealing for mobile computational Grids. These job stealing techniques have been designed to consider energy consumption and battery status. As a result of this work, we present empirical evidence showing that energy-aware job stealing is more efficient than traditional random stealing in this context. In particular, our results show that mobile Grids using energy-aware job stealing might finish up to 11 % more jobs than when using random stealing, and up to 24 % more jobs than when not using any job stealing technique. This means that using energy-aware job stealing increases the energy efficiency of mobile computational Grids because it increases the number of jobs that can be executed using the same amount of energy.     

Design of Fast and Efficient Energy-Aware Gradient-Based Scheduling Algorithms Heterogeneous Embedded Multiprocessor Systems

In this paper, we present two heuristic energy-aware scheduling algorithms (EGMS and EGMSIV) for scheduling task precedence graphs in an embedded multiprocessor system having processing elements with dynamic voltage scaling capabilities. Unlike most energy-aware scheduling algorithms that consider task ordering and voltage scaling separately from task mapping, our algorithms consider them in an integrated way. EGMS uses the concept of energy gradient to select tasks to be mapped onto new processors and voltage levels. EGM-SIV extends EGMS by introducing intra-task voltage scaling using a Linear Programming (LP) formulation to further reduce the energy consumption. Through rigorous simulations, we compare the performance of our proposed algorithms with a few approaches presented in the literature. The results demonstrate that our algorithms are capable of obtaining energy-efficient schedules using less optimization time. On the average, our algorithms produce schedules which consume 10% less energy with more than 47% reduction in optimization time when compared to a few approaches presented in the literature. In particular, our algorithms perform better in generating energy-efficient schedules for larger task graphs. Our results show a reduction of up to 57% in energy consumption for larger task graphs compared to other approaches.     

An energy-aware virtual machines consolidation method for cloud computing: Simulation and verification

Cloud systems have become an essential part of our daily lives owing to various Internet-based services. Consequently, their energy utilization has also become a necessary concern in cloud computing systems increasingly. Live migration, including several virtual machines (VMs) packed on in minimal physical machines (PMs) as virtual machines consolidation (VMC) technique, is an approach to optimize power consumption. In this article, we have proposed an energy-aware method for the VMC problem, which is called energy-aware virtual machines consolidation (EVMC), to optimize the energy consumption regarding the quality of service guarantee, which comprises: (1) the support vector machine classification method based on the utilization rate of all resource of PMs that is used for PM detection in terms of the amount' load; (2) the modified minimization of migration approach which is used for VM selection; (3) the modified particle swarm optimization which is implemented for VM placement. Also, the evaluation of the functional requirements of the method is presented by the formal method and the non-functional requirements by simulation. Finally, in contrast to the standard greedy algorithms such as modified best fit decreasing, the EVMC decreases the active PMs and migration of VMs, respectively, 30%, 50% on average. Also, it is more efficient for the energy 30% on average, resources and the balance degree 15% on average in the cloud.     

Energy-aware scheme for the 3D-HEVC depth maps prediction

This paper presents an energy-aware scheme to reduce the energy consumption on the 3D high-efficiency video coding (3D-HEVC) depth maps prediction. Besides, a qualitative discussion is presented for intra- and inter-frame predictions that conduced to the proposition of a simple energy-aware scheme. Through our analysis, the HEVC intra-prediction is applied over homogeneous regions, whereas bipartition modes are preferred to encode edge regions. Based on this fact, the Simplified Edge Detector (SED) is proposed to employ a fast intra-mode decision. The SED anticipates the blocks that are likely to be better predicted by the HEVC intra-prediction, avoiding evaluations of bipartition modes. On inter-prediction, the TZ Search (TZS) is employed in the 3D-HEVC reference software (HTM) to encode both texture frames and depth maps. However, considering the depth maps properties, lightweight fast algorithms should be considered instead of TZS. Thus, fast algorithms such as Diamond Search, Small Diamond Search (SDSP), and One-at-a-Time Search were evaluated in this paper, aiming to reduce the complexity and energy, whereas sustaining good coding efficiency. By analyzing the depth channel, this scheme (considering intra- and inter-predictions) is able to provide an encoding time reduction of 21.2–23.1 %. As drawback, the combined solution increases the BD-rate in 0.62–0.87 %, in the synthesized views. When considering general-purpose processors, our solution is capable of providing a reduction in the energy consumption ranging between 9.85 and 10.41 %, according to our software analysis using the running average power limit. By using the SDSP combined of SED algorithm instead of HTM-10.2 baseline solution, it is possible to achieve a reduction of about 54 % in the energy consumption, and about 1.8 times in the power dissipation, when running on a dedicated hardware design. Considering that depth maps are only used for view synthesis, a subjective quality assessment was performed using synthesized views, and the results demonstrate that our solution presents minimum quality losses.     

Energy-aware resource sharing with mobile devices

Ad hoc sharing of resources by offering remote services through an appropriate infrastructure enables new applications for mobile devices. However, the willingness of device owners to contribute resources to such applications remains low as long as they cannot control the amount of energy spent in sharing. In this paper, we present a framework for energy-aware resource sharing among mobile devices of various kinds that comprises (1) energy-aware strategies for selecting remote service providers and (2) a generic energy estimator for forecasting and accounting the energy consumption of a remote service call. To illustrate the benefit of (1), we show by simulation that the battery lifetime of devices running the framework can be extended up to 40% by service selection strategies that take into account the energy cost of a requested service compared to energy-unaware (random) service selection. For providing the energy-related input for service selection, we present (2) a generic estimator that can be customized easily for different hardware-platforms by solving a linear equation system with coefficients derived from benchmark measurements. We present a prototype-based case study for three different platforms, the Nokia N810, the HTC Touch Cruise and the Samsung Galaxy S showing that for all of them the estimation error is below 10% for 90% of the service calls. Furthermore, measurements conducted with a prototype implementation of the resource sharing framework show that battery lifetime can in fact be extended by energy-aware service selection strategies.     

Energy-aware disk scheduling for soft real-time I/O requests

In this work, we develop energy-aware disk scheduling algorithm for soft real-time I/O. Energy consumption is one of the major factors which bar the adoption of hard disk in mobile environment. Heat dissipation of large scale storage system also calls for an energy-aware scheduling technique to further increase the storage density. The basic idea in this work is to properly determine the I/O burst size so that device can be in standby mode between consecutive I/O bursts and that it can satisfy the soft real-time requirement. We develop an elaborate model which incorporates the energy consumption characteristics, overhead of mode transition in determining the appropriate I/O burst size and the respective disk operating schedule. Efficacy of energy-aware disk scheduling algorithm greatly relies on not only disk scheduling algorithm itself but also various operating system and device firmware related concerns. It is crucial that the various operating system level and device level features need to be properly addressed within disk scheduling framework. Our energy-aware disk scheduling algorithm successfully addresses a number of outstanding issues. First, we examine the effect of OS and hard disk firmware level prefetch policy and incorporate its effect in our disk scheduling framework. Second, our energy aware scheduling framework can allocate a certain fraction of disk bandwidth to handle sporadically arriving non real-time I/O’s. Third, we examine the relationship between lock granularity of the buffer management and energy consumption. We develop a prototype software with energy-aware scheduling algorithm. In our experiment, proposed algorithm can reduce the energy consumption to one fourth if we use energy-aware disk scheduling algorithm. However, energy-aware disk scheduling algorithm increases buffer requirement significantly, e.g., from 4 to 140 KByte. We carefully argue that the buffer overhead is still justifiable given the cost of DRAM chip and importance of energy management in modern mobile devices. The result of our work not only provides the energy efficient scheduling algorithm but also provides an important guideline in capacity planning of future energy efficient mobile devices. This paper is funded by KOSEF through Statistical Research Paper for Complex System at Seoul National University.     

面向物联网的能耗感知虚拟网络映射算法

物联网中传感器节点间规模庞大的数据交互使得能耗过大问题日趋严重,传统能耗感知算法无法适用于节点能耗不均的物联网环境。针对该问题,重新构建基于无线传感器网络的能耗模型,在考虑节点异构性和链路时效性的同时保证能耗最小。在此基础上,提出一种改进的能耗感知虚拟网络映射算法,在节点映射阶段,基于最接近剩余容量原则将虚拟节点映射至同类型且能耗最小的物理节点上,并为不同时延下的链路分配合适的资源。仿真结果表明,相比EA-VNE、EA-VNEH算法,该算法通过资源整合的方式,可以提高底层资源利用率,降低虚拟网络映射能耗,且随着引入参数的增加,能够实现更细粒度的资源分配。     

基于能量感知的无线传感器网络层次型路由协议

为了有效平衡负载,满足大规模网络的需要,提出了一种基于能量感知的无线传感器网络层次型路由协议。该协议基于能量消耗模型,根据节点与基站的距离将网络中的节点划分为不同的层次。每层次内的节点轮流当选负责收集该层内所有节点数据的汇聚节点。汇聚节点的数据逐层转发和聚合,最后传送到剩余能量较大的一个汇聚节点——超节点,由其将数据发送到基站。汇聚节点变迁、超节点变迁机制用于均衡节点能量消耗,延长网络生命周期。NS2仿真结果表明,该协议可以有效节省能量,延长网络生存时间。     

基于网络编码的异构无线传感器网络数据广播协议

本文提出一种适应异构无线传感器网络的能量感知的网络编码数据广播协议(ENCBP)。传感器节点根据邻居能量信息列表实时地计算节点剩余能量的线性映射值。在此基础上,采用能量感知的网络编码转发概率机制,使具有较高剩余能量的节点比低能量节点拥有更高的数据转发优先级,以实现能量均衡的数据传输。另外,采用伪广播机制对协议进行优化,以提高数据包投递率。仿真结果表明,ENCBP不仅可以有效实现网络的能耗均衡,以延长网络的生命周期,还能在一定程度上降低数据包传输时延,从而提高数据通信性能。     

Energy-aware robust model predictive control based on noisy wireless sensors

Wireless sensor networks (WSNs) are becoming fundamental components of modern control systems due to their flexibility, ease of deployment and low cost. However, the energy-constrained nature of WSNs poses new issues in control design; in particular the discharge of batteries of sensor nodes, which is mainly due to radio communications, must be taken into account. In this paper we present a novel transmission strategy for communication between controller and sensors which is intended to minimize the data exchange over the wireless channel. Moreover, we propose an energy-aware control technique for constrained linear systems based on explicit model predictive control (MPC), providing closed-loop stability in the presence of disturbances. The presented control schemes are compared to traditional MPC techniques. The results show the effectiveness of the proposed energy-aware approach, which achieves a profitable trade-off between energy savings and closed-loop performance.     

无线Ad Hoc网络能量感知地理路由协议研究进展

无线Ad Hoc网络（以下简称为Ad Hoc网络）能量感知地理路由协议深度影响网络性能,具有降低网络能量消耗、延长网络寿命等功效,受到越来越多的关注.系统阐述了Ad Hoc网络能量感知地理路由协议的研究进展.首先介绍了Ad Hoc网络地理路由,进而详细概述了能量感知地理路由协议形成的背景、度量指标、节点选择规则、研究意义及分类;然后,详细介绍了典型能量感知地理路由协议,并从多角度对其进行了归纳总结与比较;最后,阐述了能量感知地理路由协议研究存在的问题,指出了未来需要研究的内容,并在此基础上进行总结.     

Robust energy-aware routing with redundancy elimination

Many studies in literature have shown that energy-aware routing (EAR) can significantly reduce energy consumption for backbone networks. Also, as an arising concern in networking research area, the protocol-independent traffic redundancy elimination (RE) technique helps to reduce (a.k.a compress) traffic load on backbone network. Motivation from a formulation perspective, we first present an extended model of the classical multi-commodity flow problem with compressible flows. Moreover, our model is robust with fluctuation of traffic demand and compression rate. In details, we allow any set of a predefined size of traffic flows to deviate simultaneously from their nominal volumes or compression rates. As an applicable example, we use this model to combine redundancy elimination and energy-aware routing to increase energy efficiency for a backbone network. Using this extra knowledge on the dynamics of the traffic pattern, we are able to significantly increase energy efficiency for the network. We formally define the problem and model it as a Mixed Integer Linear Program (MILP). We then propose an efficient heuristic algorithm that is suitable for large networks. Simulation results with real traffic traces on Abilene, Geant and Germany50 networks show that our approach allows for 16–28% extra energy savings with respect to the classical EAR model.     

Improving chip multiprocessor reliability through code replication

Chip multiprocessors (CMPs) are promising candidates for the next generation computing platforms to utilize large numbers of gates and reduce the effects of high interconnect delays. One of the key challenges in CMP design is to balance out the often-conflicting demands. Specifically, for today’s image/video applications and systems, power consumption, memory space occupancy, area cost, and reliability are as important as performance. Therefore, a compilation framework for CMPs should consider multiple factors during the optimization process. Motivated by this observation, this paper addresses the energy-aware reliability support for the CMP architectures, targeting in particular at array-intensive image/video applications. There are two main goals behind our compiler approach. First, we want to minimize the energy wasted in executing replicas when there is no error during execution (which should be the most frequent case in practice). Second, we want to minimize the time to recover (through the replicas) from an error when it occurs. This approach has been implemented and tested using four parallel array-based applications from the image/video processing domain. Our experimental evaluation indicates that the proposed approach saves significant energy over the case when all the replicas are run under the highest voltage/frequency level, without sacrificing any reliability over the latter.     

异构移动终端能量感知的P2P数据共享机制

针对现有P2P数据共享网络中存在的移动终端异构性这一问题,提出一种异构移动终端能量感知的P2P数据共享机制。该机制在判断移动终端类型的同时引入能量感知模块,用于预测终端的剩余能量,在此基础上,根据网络环境的变化动态地调整数据的共享策略。仿真实验表明,该机制能够有效提高移动终端的能量利用率,平衡终端的负载,延长数据共享时间,从而提高数据分发成功率。在保持文件高可用性的前提下,平均减少15%的终端能耗。     

Stochastic programming and scenario generation within a simulation framework: An information systems perspective

Stochastic programming brings together models of optimum resource allocation and models of randomness to create a robust decision-making framework. The models of randomness with their finite, discrete realisations are called scenario generators. In this paper, we investigate the role of such a tool within the context of a combined information and decision support system. We explain how two well-developed modelling paradigms, decision models and simulation models can be combined to create “business analytics” which is based on ex-ante decision and ex-post evaluation. We also examine how these models can be integrated with data marts of analytic organisational data and decision data. Recent developments in on-line analytical processing (OLAP) tools and multidimensional data viewing are taken into consideration. We finally introduce illustrative examples of optimisation, simulation models and results analysis to explain our multifaceted view of modelling. In this paper, our main objective is to explain to the information systems (IS) community how advanced models and their software realisations can be integrated with advanced IS and DSS tools.     

面向能耗优化的容错实时系统任务调度模型研究

为了支持面向能耗优化的容错实时任务调度算法研究,提出一种频率相关的时间Petri网—FRTPN.FRTPN引入用于动态电压调整的变迁频率设置空间以及和频率相关的静态引发时域,以支持调度算法的能耗评估及优化;同时它增加一类抑制弧刻画容错故障恢复过程.通过对基于检查点的容错实时能耗优化任务调度进行建模证明了FRTPN的有效性.     

无线传感器网络中基于网络层的能源有效性研究

如何有效地使用有限的能源是无线传感器网络的一个核心问题。以环境监测为背景,基于网络层建立了无线传感器网络生命期的模型,并时其进行分析,指出能源有效路由算法和数据融合是网络层节省能源的重要因素。最后给出了一种基于网络层的能源有效性解决方案,达到延长网络生命期的目的。