Free-scaling your data center

The increasing popularity of both small and large private clouds and expanding public clouds poses new requirements to data center (DC) architectures. First, DC architectures should be incrementally scalable allowing the creation of DCs of arbitrary size with consistent performance characteristics. Second, initial DC deployments should be incrementally expandable supporting small-scale upgrades without decreasing operation efficiency. A DC architecture possessing both properties satisfies the requirement of free-scaling.Recent work in DC design focuses on traditional performance and scalability characteristics, therefore resulting in symmetric topologies whose upgradability is coarse-grained at best. In our earlier work we proposed Scafida, an asymmetric, scale-free network inspired DC topology which scales incrementally and has favorable structural characteristics. In this paper, we build on Scafida and propose a full-fledged DC architecture achieving free-scaling called FScafida. Our main contribution is threefold. First, we propose an organic expansion algorithm for FScafida; this combined with Scafida’s flexible original design results in a freely scalable architecture. Second, we introduce the Effective Source Routing mechanism that provides near-shortest paths, multi-path and multicast capability, and low signaling overhead by exploiting the benefits of the FScafida topology. Third, we show based on extensive simulations and a prototype implementation that FScafida is capable of handling the traffic patterns characteristic of both enterprise and cloud data centers, tolerates network equipment failures to a high degree, and allows for high bisection bandwidth.     

Detecting stable locality-aware predicates

In a large-scale locality-driven network such as in modular robotics and wireless sensor networks, knowing the state of a local area is sometimes necessary due to either interactions being local and driven by neighborhood proximity or the users being interested in the state of a certain region. We define locality-aware predicates (LAP) that aim at detecting a predicate within a specified area. We model the area of interest as the set of processes that are within a breadth-first search tree (BFST) of height k$k$ rooted at the initiator process. Although a locality-aware predicate specifies a predicate only within a local area, observing the area consistently requires considering the entire system in a consistent manner. This raises the challenge of making the complexities of the corresponding predicate detection algorithms scale-free, i.e., independent of the size of the system. Since all existing algorithms for getting a consistent view of the system require either a global snapshot of the entire system or vector clocks of the size of the system, a new solution is needed. We focus on stable LAP, which are those LAP that remain true once they become true. We propose a scale-free algorithm to detect stable LAP within a k$k$-height BFST. Our algorithm can detect both stable conjunctive LAP and stable relational LAP. In the process of designing our algorithm, we also propose the first distributed algorithm for building a BFST within an area of interest in a graph, and the first distributed algorithm for recording a consistent sub-cut within the area of interest. This paper demonstrates that LAPs are a natural fit for detecting distributed properties in large-scale distributed systems, and stable LAPs can be practically detected at low cost.     

一种无尺度网络上垃圾邮件蠕虫的传播模型

用有向图描述了电子邮件网络的结构，并分析了电子邮件网络的无尺度特性。在此基础上，通过用户检查邮件的频率和打开邮件附件的概率建立了一种电子邮件蠕虫的传播模型。分别仿真了电子邮件蠕虫在无尺度网络和随机网络中的传播，结果表明，邮件蠕虫在无尺度网络中的传播速度比在随机网络中更快，与理论分析相一致。     

网络结点度相关性测度及其稳定性分析

判别网络同配/异配性的Newman相关系数r(g)和网络无标度程度的S(g)是研究网络结点度相关性的两个重要测度,其相关论文被科研人员广泛地引用,两个测度分别被应用到实际网络的同配/异配性和无标度程度的分析.为检验两个测度的稳定性,以BA模型为例,通过大量的计算机模拟和数值统计分析,结果显示:BA-3模型的r(g)与网络规模N的经验公式:r(g)∝-N-0.32,并指出r(g)和S(g)关于网络规模N、网络稠密度ρ都具有波动性.因此,用它们来分析不同规模或不同稠密度的有限的、增长的实际网络,其研究会产生一些误导性结果.     

K(Knowledge)-net: building up and its dynamics

The essence of intelligence is to use certain abilities to obtain knowledge, to use that knowledge, and to operate with that knowledge. New knowledge learned by a human is often related to old existing knowledge, and sometimes we could have more conceptual knowledge based on old knowledge. So, the knowledge in the brain exists in a related structural form, and this structure is dynamic, and therefore is evolvable. Based on the understanding of the real process of learning by a human being, we discuss how to make a model to describe the dynamic structure of knowledge. This model is also a principle of artificial brain design. Most of the knowledge a child learns is from natural language and perception information, and we define this as semantic knowledge. The model to describe the process and structure of knowledge growing in a network form is called a K-net. It is a dynamic network with two main dynamics: one is new knowledge added, and the other is aggregating knowledge existing in the network with some probability. Under these very natural conditions, we found that the network is originally a simple random net, and then some characteristics of a complex network gradually appear when more new knowledge is added and aggregated. A more interesting phenomenon is the appearance of a random hierarchical structure. Does this mean emergence?     

基于复杂网络面向对象集成测试的研究

软件测试是保证软件质量的重要手段.面向对象的方法给软件系统带来好处的同时,也为测试带来了挑战,传统的测试方法无法应用于许多面向对象的特性.研究表明,大型软件系统内部结构具有小世界效应(Small-World,SW)和无标度特性(Scale-Free,SF).基于软件的复杂网络特性对面向对象的集成测试进行了研究,提出了一种通过分析类之间的交互复杂性和聚集复杂性来确定软件测试顺序的方法.利用该方法进行面向对象集成测试可以减少桩模块的数量,提高测试效率,且不降低原有测试覆盖度.     

基于复杂网络模型的基因调控网络的计算模拟

随着大通量基因芯片数据的产生,基因调控机制的网络化研究需求日趋迫切。提出了基于复杂网络理论的基因调控网络的模拟方法,构建了基因调控网络模拟器GN-Simulator。通过分析真实基因调控网络的拓扑特性,提出了对应的矩阵模型,并充分考虑了网络的生物学鲁棒性和动力学稳定性,给出了人工基因网络的生成过程和计算模拟方法。计算实验表明,GN-Simulator能高效地模拟出与真实基因调控网络高度相似的大规模人工网络,并可为不同算法提供无偏验证的多样化人工模拟数据。     

Innovative reuse of drinking water sludge for the treatment of petroleum produced water to enhance oil recovery

Using treated petroleum produced water (PPW) as injected water, for enhancing oil recovery, is important in petroleum industry. The process of water reuse is a cost-effective method. On the contrary, injection of inadequately treated water could induce scale formation. In the present study oil-free petroleum produced water (PPW) was treated to obtain water without scale tendency by using the water treatment sludge (WTS) produced from the Drinking Water Treatment Plants (DWTP). The WTS is usually accumulated in the DWTP as solid useless solid wastes. The characterization of the washed and dried WTS was investigated extensively by scanning electron microscope (SEM), Energy Dispersive X-Ray (EDAX), Dynamic light scattering (DLS), and Surface area analyzer. It was found that the average particle size of WTS is 47.34 nm. The main constituents of the WTS are MgO, Al₂O₃, SiO₂, CaO, and Fe₂O₃. This WTS was reused for the treatment of oil-free PPW, where the chemical and physical characteristics of PPW before and after treatment were determined. The present study showed that the optimum WTS dose was 3 g/l at which the salinity, alkalinity, TDS, conductivity, hardness, cations and anions of the PPW were significantly decreased. The overall results revealed that by decreasing the cations and anions of PPW, the later has no any tendency to form scale. Therefore, the treated PPW could be used again to enhance oil recovery without any concern of scale formation, water-saving, as well as protecting the environment from the discharge of such polluted water.     

The diffusion of technological knowledge through interlaced networks

In the last decade, many papers discussed the basic features of the networks formed by knowledge diffusion. In this paper, we show that a network formed by the spreading of technological information, represented by the patents citation network, does not obey the expected power law behavior and, therefore, is not a scale-free network. We mapped a network formed by almost 600 000 patents, covering a 40 years period. Although the complete network is not scale-free, small portions within the whole network can be described by power laws. The network combines several fields of knowledge, but those power law portions belong to specific fields of technological knowledge. A mathematical model is introduced, which can explain the basic dynamics of the formation of this network.     

Software architecture graphs as complex networks: A novel partitioning scheme to measure stability and evolution

The stability and evolution of the structure of consecutive versions of a series of software architecture graphs are analysed using the theory of complex networks. Brief comparisons are drawn between the scale-free behaviour and second order phase transitions. On this basis a software design metric I_cc is proposed. This software metric is used to quantify the evolution of the stability vs. maintainability of the software through various releases. It is demonstrated that the classes in the software graph are acquiring more out-going calls than incoming calls as the software ages. Three examples of software applications where maintainability and continuous refactoring are an inherent part of their development process are presented, in addition to a Sun Java2 framework where growth and backward compatibility are the more important factors for the development. Further to this a projected future evolution of the software structure and maintainability is calculated. Suggestions for future applications to software engineering and the natural sciences are briefly presented.