Thermal-Aware Post Layout Voltage-Island Generation for 3D ICs

To reduce the interconnect delay and improve the chip performance, three-dimensional (3D) chip emerged with the rapid increasing of chip integration and chip power density. Therefore, thermal issue is one of the critical challenges in 3D IC design due to the high power density. Multiple Supply Voltages (MSV) technique provides an effcient way to optimize power consumption which in turn may alleviate the hotspots. But the voltage assignment is limited not only by the performance constraints of the design, but also by the physical layout of circuit modules since the modules with the same voltage should be gathered to reduce the power-network routing resource. Especially in 3D designs, the optimization using MSV technique becomes even more complicated since the high temperature also inffuences the power consumption and delay on paths. In this paper, we address the voltage-island generation problem for MSV designs in 3D ICs based on a mixed integer linear programming (MILP) model. First, we propose a general MILP formulation for voltage-island generation to optimize thermal distribution as well as power-network routing resources while maintaining the whole chip performance. With the thermal-power interdependency, an iterative optimization approach is proposed to obtain the convergence. Experimental results show that our thermal-aware voltage-island generation approach can reduce the maximal on-chip temperature by 23.64% with a reasonable runtime and save the power-network routing resources by 16.71%.     

面向海洋节能边缘计算的任务卸载研究

针对海洋通信网络能源不稳定、时延较长的问题,提出一种混合能量供应的边缘计算卸载方案。对于能量供应问题,移动边缘计算(MEC)服务器集成混合电源和混合接入点,混合电源利用可再生能源为MEC服务器供应能量,采用电力电网作为其补充能源,保证边缘计算系统的可靠运行,船舶用户通过混合接入点广播的射频(RF)信号收集能量。针对任务卸载优化问题,以能耗-时延权衡优化为目标,联合能量收集方法制定任务卸载比例、本地计算能力和发射功率的优化方案,最后利用降维优化算法,将目标函数简化为关于任务卸载比例的一维多约束问题,并利用改进的鲸鱼优化算法获得最优的执行总代价。利用边缘云模拟器EdgeCloudSim仿真的结果表明,所提方案较具有能量收集的资源分配方案和基本海上通信网络优化的方案执行成本分别降低了13.4%和9.6%。     

电网中断下基于DWOA的生鲜产品闭环供应链网络多目标模糊优化设计

针对生鲜产品供应链网络设计问题,建立了一种电网中断下的闭环生鲜供应链网络多目标模糊优化设计模型,以此解决供应链网络设计中的不确定性问题.首先,针对电网中断下生鲜产品闭环供应链网络结构设计,建立目标为成本最小、碳排放最少、中断时间最短的优化函数,采用Me测度和三角模糊数对该模型进行处理,将多目标问题转换为单目标问题;其次,在原有鲸鱼算法的基础上,引入差分算法的交叉和变异理念,增强其搜索能力,改善其局限性,得到改进差分鲸鱼优化算法(DWOA),并采用此方法对处理后的模型求解;最后,通过数值实例和敏感性分析表明,提出的算法和模型在处理生鲜产品供应链网络优化设计方面具有较强的求解能力,且计算时间较短.     

A multi-objective optimization for green supply chain network design

In this paper, we study a supply chain network design problem with environmental concerns. We are interested in the environmental investments decisions in the design phase and propose a multi-objective optimization model that captures the trade-off between the total cost and the environment influence. We conduct a comprehensive set of numerical experiments. The results show that our model can be applied as an effective tool in the strategic planning for green supply chain. Meanwhile, the sensitivity analysis provides some interesting managerial insights for firms.     

A novel 3D NoC architecture based on De Bruijn graph

Networks on Chip (NoC) and 3-Dimensional Integrated Circuits (3D IC) have been proposed as the solutions to the ever-growing communication problem in System on Chip (SoC). Most of contemporary 3D architectures are based on Mesh topology, which fails to achieve small latency and power consumption due to its inherent large network diameter. Moreover, the conventional XY routing lacks the ability of fault tolerance. In this paper, we propose a new 3D NoC architecture, which adopts De Bruijn graph as the topology in physical horizontal planes by leveraging its advantage of small latency, simple routing, low power, and great scalability. We employ an enhanced pillar structure for vertical interconnection. We design two shifting based routing algorithms to meet separate performance requirements in latency and computing complexity. Also, we use fault tolerant routing to guarantee reliable data transmission. Our simulation results show that the proposed 3D NoC architecture achieves better network performance and power efficiency than 3D Mesh and XNoTs topologies.     

Bi-LCQ: A low-weight clustering-based Q-learning approach for NoCs

Network congestion has a negative impact on the performance of on-chip networks due to the increased packet latency. Many congestion-aware routing algorithms have been developed to alleviate traffic congestion over the network. In this paper, we propose a congestion-aware routing algorithm based on the Q-learning approach for avoiding congested areas in the network. By using the learning method, local and global congestion information of the network is provided for each switch. This information can be dynamically updated, when a switch receives a packet. However, Q-learning approach suffers from high area overhead in NoCs due to the need for a large routing table in each switch. In order to reduce the area overhead, we also present a clustering approach that decreases the number of routing tables by the factor of 4. Results show that the proposed approach achieves a significant performance improvement over the traditional Q-learning, C-routing, DBAR and Dynamic XY algorithms.     

基于遗传算法的移动自组网路由优化

对移动自组网的拓扑结构进行分析,建立其路由网络模型．将遗传算法的基本原理和移动自组 网的路由模型结合起来,提出了一种求解无线网络最优路径的算法．该方法采用可变长度染色体编码,利用 遗传算法优化路由,可以在满足系统开销最小的约束条件下寻找到最优路径．     

基于图论的煤矿井下高压供电网络优化

矿井高压供电网络大部分负荷集中在配电线路的末端,目前的配电网优化方案不能完全适用于矿井配电网。针对该问题,提出了一种基于图论的煤矿井下高压供电网络优化方案,分析了矿井供电网络的特征,详细介绍了应用图论中的最短路径法优化井下高压供电网络的过程,并给出了某矿井下高压供电网络的优化结果。优化前后的矿井高压供电网络潮流计算结果表明,该优化方案可有效提高矿井供电网络的电压质量。     

三维片上网络研究综述

三维片上网络以其更短的全局互连、更高的封装密度、更小的体积等诸多优势,已引起国内外学术界和产业界的高度重视.对三维片上网络的研究,将直接影响一个国家未来三维集成电路和三维芯片产业的发展,也关系到国家安全.近年来,三维片上网络逐渐成为片上网络研究领域的一个重要方向,已取得了许多研究进展,但仍然存在许多挑战性的课题.对三维片上网络的基本问题作了简介;分析了三维片上网络在国内外的研究现状;讨论了三维片上网络研究中的关键问题,归纳出网络拓扑结构、路由机制、性能评估、通信容错、功耗、映射、测试、交换技术、服务质量、流量控制、资源网络接口等12类研究课题;分类综述了关键问题的研究进展;分析了三维片上网络存在的问题;指出,在三维片上网络拓扑结构方面:个性化拓扑结构设计、仿真平台研究开发、基于新型拓扑结构的三维芯片样片试制以及无线技术的引入等,在路由算法方面:适合3D Torus的路由算法、结合无关路由与自适应路由算法优点的新路由算法、适合各种新型拓扑结构的高效路由算法等,在性能评估方面:永久故障的容错、改进仿真程序增加对物理链路的建模、充分考虑通信的局部性等,在功耗方面:对拓扑结构/映射算法/路由算法和布局进行综合优化、动态和静态控制相结合、更为精确的3D NoC功耗模型等,在映射方面:发热均匀性、动态路由策略下映射评估模型的优化、低功耗映射算法、基于优化算法的组合映射等,都将是三维片上网络未来的重要研究课题.     

Reliable Communication Networks for Smart Grid Transmission Systems

In this paper, we discuss the communications reliability requirements posed by the smart power grid with a focus on communications in support of wide area situational awareness. Implementation of wide area situational awareness relies on both transmission substation networks and wide area optical networks. We study the reliability of a sample communications network of the California Power Grid and find that its reliability falls short of proposed requirements. To overcome this issue, we consider the problem of designing the substation network and the wide area network to meet the reliability requirements while minimizing the network cost. For the wide area network design problem, we propose two alternate design approaches, namely: (1) following the power lines and (2) a mesh based design interconnecting the nodes. For the first approach we develop two greedy iterative heuristics and a heuristic integer linear programming (H-ILP) model using minimum cut-sets for network reliability optimization. The greedy iterative algorithms outperform the H-ILP approach in terms of cost, but require a larger amount of computing resources. Both proposed models are in fact complementary and thus provide a framework to optimize the reliability of smart grid communications networks restricted to following the power lines. In the second approach a greenfield mesh network method is proposed based on starting with a minimum spanning tree which is then augmented through a greedy heuristic into a mesh. Comparative numerical results show that the reliable mesh design has advantages in terms of the number of links and total link distance needed.     

网络能耗系统模型及能效算法

网络能耗问题是当前网络研究和发展的重要问题,影响着网络的设计、应用和发展.当前网络能效算法的研究大多从网络局部角度关注于网络某一(些)设施的能耗问题,缺乏从网络全局的角度研究网络整体能耗的算法和策略.文中从网络全局角度研究网络的能耗模型和算法问题.文中基于3种基本网络数据传递模式和3种基本网络能耗机制,构建了网络能耗系统优化模型,对其中5种关键系统模型给出了详细的描述.针对几个关键系统模型,文中给出了形式化描述并且提出了相应的优化的节能路由算法.文中给出的网络系统能耗优化模型有助于对于网络能耗的理解和分析,所提出的能耗优化的网络数据包路由算法可以从网络路由的角度有效降低网络能耗.     

基于AHSPSO算法的DG并网位置优化研究

为寻求不同接线模式下分布式电源接入位置对配网电压优化的规律性,以系统电压影响参数最小化为目标,以功率平衡、节点电压、支路容量为约束条件,采用自适应和声搜索与粒子群优化混合(AHSPSO)算法对不同接线模式下的DG并网位置进行优化,得出不同接线模式下的最优接入位置.利用IEEE-33节点配网模型进行仿真对比,分析不同接线模式下DG的并网位置对配网电压影响的规律.结果表明:只有正确合理地配置不同接线模式下DG并网位置,才能够更为有效地发挥电压的支撑作用.     

Coordinating workload balancing and power switching in renewable energy powered data center

There has been growing concern about energy consumption and environmental impact of datacenters. Some pioneers begin to power datacenters with renewable energy to offset carbon footprint. However, it is challenging to integrate intermittent renewable energy into datacenter power system. Grid-tied system is widely deployed in renewable energy powered datacenters. But the drawbacks (e.g. Harmonic disturbance and costliness) of grid tie inverter harass this design. Besides, the mixture of green load and brown load makes powermanagement heavily depend on software measurement and monitoring, which often suffers inaccuracy. We propose DualPower, a novel power provisioning architecture that enables green datacenters to integrate renewable power supply without grid tie inverters. To optimize DualPower operation, we propose a specially designed power management framework to coordinate workload balancing with power supply switching. We evaluate three optimization schemes (LM, PS and JO) under different datacenter operation scenarios on our trace-driven simulation platform. The experimental results show that DualPower can be as efficient as grid-tied system and has good scalability. In contrast to previous works, DualPower integrates renewable power at lower cost and maintains full availability of datacenter servers.     

无线传感器网络中一种拥塞感知的多路径流量分配算法

拥塞控制是无线传感器网络中的一个关键问题,拥塞不但增加丢包率,影响传输可靠性,而且会浪费宝贵的能量资源。本文从保证数据传输可靠性的多路径路由出发,提出了 一个拥塞感知的多路径流量分配算法COTA。COTA从全网出发,基于路径的启发式信息分配流量,避免给潜在的热点区域分配过多负载。此外,繁忙节点使用可靠性相关的报文文调度策略有效保证高可靠报文优先使用带宽。详细的模拟实验表明了COTA在可靠性、吞吐率等方面的优势。     

基于BP神经网络模型实现配电网络损耗优化的研究

配电网络损耗是关系到电网正常运行的关键,如何对配电网络损耗进行优化是目前亟待解决的技术问题.论文针对无功补偿设备辐射状配电网存在的网络损耗问题,提出了一种优化方案.根据辐射状配电网体系结构的特征,提出基于BP网络模型理论进行计算配电网络损耗的方法,该方法能够映射、处理任意复杂的非线性关系,数据处理精确、及时,并根据计算...     

基于量子进化算法的路由选择

网络中存在许多设计和优化问题,其中相当一部分属于NP类型。传统的解法由于计算复杂度过大而失效。为了降低计算机网络的时延和运营费用以改进网络性能,采用量子进化算法优化计算机网络中路由选择问题,深入研究了量子进化算法及其在路由选择优化问题中的应用,并对量子进化算法进行了改进,使之更适合这类问题的求解。仿真实验结果表明,同传统优化算法相比该方法对求解网络的路由选择具有很大优越性。研究结果不仅对各类网络的优化问题有一定的应用价值,而且也扩展了量子进化算法的应用范围。     

基于节点密度加权的T-LEACH三维动态路由协议研究

随着无线传感网络在三维动态环境应用需求的俱增,如何在动态拓扑的三维网络环境下,设计能量高效和数据传输高可靠性的路由协议是当前学术界的研究热点。现有的三维路由协议未充分考虑节点移动和环境因素的影响,多为二维静态协议的补充和支持。将拓扑结构和地理结构的路由协议相结合,提出了基于节点密度加权的T-LEACH三维动态路由协议。使用T-LEACH算法获得全局节点密度信息,DDRS算法预知路由空洞和使用DDRS-R的恢复算法逃逸空洞和修正路由,有效地实现规避局部最优问题和迅速逃逸路由空洞的目标,提高了网络整体的健壮性和生存时间。通过在课题组设计的无线传感器网络三维环境路由协议仿真平台上的实验和对比,证明本文提出的路由协议在能量消耗,网络生存期及数据交付率等方面优于现有协议,具有良好的应用前景。     

服务覆盖网络中一种动态流量工程模型与算法

针对服务覆盖网络中的自私路由造成的网络流量失衡将严重影响网络效率和稳定性的问题, 研究如何在覆 盖层应用动态流量工程的方法进行流量优化分配. 基于随机动态优化配流理论, 提出一种服务覆盖网络的动态流量 工程模型, 并设计了分布式的流量工程算法. 该算法可以折衷控制路由的自私与负载均衡的程度. 模拟实验显示, 所 提出的方法较其他方法具有更好的性能, 尤其对于实时动态流量有着较好的适应性.

     

Joint topology control and routing for multi-radio multi-channel WMNs under SINR model using bio-inspired techniques

Multi-channel communication in a wireless mesh network (WMN) equipped with multi-radio routers can significantly enhance the network capacity. Channel allocation, power control and routing are three main issues involved in the performance of multi-channel multi-radio WMNs. In this paper, the joint optimization of channel allocation, power control and routing under signal-to-interference-and-noise ratio (SINR) model for multi-channel multi-radio WMNs is investigated. It is proven to be NP hard. As we know, no optimal polynomial time solutions have been proposed in the previous literatures. In order to tackle this problem, we apply bio-inspired optimization techniques for channel allocation and power control, and use linear programming for routing optimization. To reflect the cross-layer interaction property among these three issues, the routing optimization is further defined as the fitness value of a chromosome in bio-inspired optimization. Further, we propose an effective joint optimization framework, in which two representative bio-inspired optimization methods (genetic algorithm and particle swarm optimization algorithm) are hybridized to enhance the searching ability. The detailed evolution processes for both genetic algorithm and particle swarm optimization algorithm are demonstrated. Extensive simulation results show that the proposed algorithm converges fast and approaches the sub-optimal solution effectively.     

A novel optimization-based bandwidth-aware minimum power multicast routing algorithm in green wireless networks

Multicast routing in wireless networks that possess the wireless multicast advantage could significantly reduce the power and energy consumption. However, this kind of multicast routing that only addresses the transmission radius coverage might not be able to meet the bandwidth requirement of the users. As a result, additional transmissions are required to incur more energy consumption and carbon dioxide emissions that make existing algorithms not applicable to bandwidth constrained applications. In this paper, for the first time, we address the bandwidth aware minimum power multicast routing problem in wireless networks where the objective function is to minimize the total power consumption subject to the users?? bandwidth requirements. This problem is a challenging cross-layer design problem that requires seamless and sophisticated integrated design in the network layer (multicast routing) and physical layer (bandwidth-aware wireless transmission and power control). We first formulate this problem as a mixed integer linear programming problem and then propose a Lagrangian relaxation based algorithm to solve this problem. Numerical results demonstrate that the proposed approach is a sound green networking algorithm that outperforms the existing power efficient multicast routing approaches under all tested cases, especially in large bandwidth request, fine radius granularity, large group size and sparse network.