A two-time-scale load balancing framework for minimizing electricity bills of Internet Data Centers

Internet Data Center (IDC) is one of important emerging cyber-physical systems. To guarantee the quality of service for their worldwide users, large Internet service providers usually operate multiple geographically distributed IDCs. The enormous power consumption of these data centers may lead to both huge electricity bills and considerable carbon emissions. To mitigate these problems, on-site renewable energy plants are emerging in recent years. Since the renewable energy is intermittent, greening geographical load balancing (GGLB for short) has been proposed to reduce both the electricity bills and carbon emissions by following the renewables. However, GGLB is not able to well deal with the wildly fluctuating wind power when applied into IDCs with on-site wind energy plants. It may either fail to minimize the total electricity bills or incur the costly frequent on–off switching of servers. In order to minimize the total electricity bills of geographically distributed IDCs with on-site wind energy plants, we formulate the total electricity bills minimization problem and propose a novel two-time-scale load balancing framework TLB to solve it. First, TLB models the runtime cooling efficiency for each IDC. Then it predicts the future fine-grained (e.g., 10-min) on-site wind power output at the beginning of each scheduling period (e.g., an hour). After that, TLB transforms the primal optimization problem into a typical mixed-integer linear programming problem and solves it to finally obtain the optimal scheduling policy including the open server number as well as the request routing policy. It is worth noting that the open server number of each IDC will remain the same during each scheduling period. As an application instance of TLB, we also design a two-time-scale load balancing algorithm TLB-ARMA for our experimental scenario. Evaluation results based on real-life traces show that TLB-ARMA is able to reduce the total electricity bills by as much as 12.58 % compared with the hourly executed GGLB without incurring the costly repeated on–off switching of servers.     

A scalable and automatic mechanism for resource allocation in self-organizing cloud

Taking advantage of the huge potential of consumers’ untapped computing power, self-organizing cloud is a novel computing paradigm where the consumers are able to contribute/sell their computing resources. Meanwhile, host machines held by the consumers are connected by a peer-to-peer (P2P) overlay network on the Internet. In this new architecture, due to large and varying multitudes of resources and prices, it is inefficient and tedious for consumers to select the proper resource manually. Thus, there is a high demand for a scalable and automatic mechanism to accomplish resource allocation. In view of this challenge, this paper proposes two novel economic strategies based on mechanism design. Concretely, we apply the Modified Vickrey Auction (MVA) mechanism to the case where the resource is sufficient; and the Continuous Double Auction (CDA) mechanism is employed when the resource is insufficient. We also prove that aforementioned mechanisms have dominant strategy incentive compatibility. Finally, extensive experiment results are conducted to verify the performance of the proposed strategies in terms of procurement cost and execution efficiency.     

A metamodeling approach for pattern specification and management

The formal specification of design patterns is central to pattern research and is the foundation of solving various pattern-related problems.In this paper,we propose a metamodeling approach for pattern specification,in which a pattern is modeled as a meta-level class and its participants are meta-level references.Instead of defining a new metamodel,we reuse the Unified Modeling Language(UML)metamodel and incorporate the concepts of Variable and Set into our approach,which are unavailable in the UML but essential for pattern specification.Our approach provides straightforward solutions for pattern-related problems,such as pattern instantiation,evolution,and implementation.By integrating the solutions into a single framework,we can construct a pattern management system,in which patterns can be instantiated,evolved,and implemented in a correct and manageable way.     

浅析P2P技术的安全问题与防御体系

P2P作为一种崭新的传输模式,不仅能提供隐私保护与匿名通信,还能提高网络的健壮性和抗毁性。然而,P2P技术也由于其自身存在的一些缺陷。文章在对P2P技术简要论述的基础上,重点分析目前P2P系统安全的各种威胁以及防御体系的建设。     

移动天气雷达系统的卫星通信

为了把移动雷达实时数据传输到北京市气象局,通过对比多种无线通信方式以及卫星通信的优缺点等方法建立了一套卫星通信系统。结果表明：本系统在应急、网络速率、安全等方面都能满足移动雷达系统对数据通信的要求。这套系统的设计将对应急系统的建立具有一定的指导意义。     

Dynamic Hotlinks

Consider a directed rooted tree T=(V,E) representing a collection V of n web pages connected via a set E of links all reachable from a source home page, represented by the root of T. Each web page i carries a weight w _i representative of the frequency with which it is visited. By adding hotlinks, shortcuts from a node to one of its descendants, we are interested in minimizing the expected number of steps needed to visit pages from the home page. We give the first linear time algorithm for assigning hotlinks so that the number of steps to access a page i from the root of the tree reaches the entropy bound, i.e. is at most O(log (W/w _i )) where W=∑ _i∈T w _i . The best previously known algorithm for this task runs in time O(n ²). We also give the first efficient data structure for maintaining hotlinks when nodes are added, deleted or their weights modified, in amortized time O(log (W/w _i )) per update. The data structure can be made adaptive, i.e. reaches the entropy bound in the amortized sense without knowing the weights w _i in advance.     

晶振频率偏差补偿的无线传感器网络时间同步算法

节点间存在的晶振频率偏差是引起时间漂移的最主要原因,针对这个问题,本文提出一种基于晶振频率偏差补偿的时间同步算法,该算利用相邻两次同步过程中记录的时间估算出了与时间基准节点间的晶振频率偏差,并根据晶振频率偏差和双向报文交换模型对时间偏移进行了补偿.试验结果表明,该算法大大提高了同步精度.     

基于时域双谱特征的雷达目标识别

雷达目标高分辨一雏距离像(High range resolution profile,HRRP)是目标散射回波在雷达径向上的投影,具有非高斯、非线性特点.文中从双谱概念出发引入时域双谱概念.分析了HRRP的时域双谱特征.为提高识别性能,定义了Fisher类可分离度函数,以类间可分离度最大化作为特征提取准则,提取一些特征双谱作为HRRP的特征向量.采用基于子空间变换的独立分量分析(Principle component analysis,PCA)和线性判别分析(Linear discriminant analysis,LDA)的特征提取方法进行了对比实验.结果表明,本文方法用于雷达目标高分辨一维距离像识别具有良好的抗喙性能和较高识别率.     

阿尔茨海默氏症研究中的磁共振成像数据分析

首先综述了当前结构磁共振成像、功能磁共振成像和扩散张量磁共振成像3种技术在阿尔茨海默氏症研究中的现状;其次介绍和分析了上述3种磁共振成像数据的主要处理方法;最后介绍了基于阿尔茨海默氏症的神经影像数据库及其诊断平台的建设状况.另外,也提到了此课题在该领域的一些研究进展.     

Digital‐to‐analog converter with gamma correction on glass substrate for TFT‐panel applications

Abstract— Low‐temperature polysilicon (LTPS) technology has a tendency towards integrating all circuits on glass substrate. However, the poly‐Si TFTs suffered poor uniformity with large variations in the device characteristics due to a narrow laser process window for producing large‐grained poly‐Si TFTs. The device variation is a serious problem for circuit realization on the LCD panel, so how to design reliable on‐panel circuits is a challenge for system‐on‐panel (SOP) applications. In this work, a 6‐bit R‐string digital‐to‐analog converter (DAC) with gamma correction on glass substrate for TFT‐panel applications is proposed. The proposed circuit, which is composed of a folded R‐string circuit, a segmented digital decoder, and reordering of the decoding circuit, has been designed and fabricated in a 3‐μm LTPS technology. The area of the new proposed DAC circuit is effectively reduced to about one‐sixth compared to that of the conventional circuit for the same LTPS process.