An overview of energy efficiency techniques in cluster computing systems

Two major constraints demand more consideration for energy efficiency in cluster computing: (a) operational costs, and (b) system reliability. Increasing energy efficiency in cluster systems will reduce energy consumption, excess heat, lower operational costs, and improve system reliability. Based on the energy-power relationship, and the fact that energy consumption can be reduced with strategic power management, we focus in this survey on the characteristic of two main power management technologies: (a) static power management (SPM) systems that utilize low-power components to save the energy, and (b) dynamic power management (DPM) systems that utilize software and power-scalable components to optimize the energy consumption. We present the current state of the art in both of the SPM and DPM techniques, citing representative examples. The survey is concluded with a brief discussion and some assumptions about the possible future directions that could be explored to improve the energy efficiency in cluster computing.     

复杂生物数据集的聚类数自动确定方法

聚类数目是影响聚类效果的关键参数,通常需要人工确定,对于较难获得这一先验知识的复杂生物数据集,聚类分析会因此受到限制。针对这一问题,文章提出一种自动确定最佳聚类数目的方法,该方法利用体现"类内紧凑类间离散"思想的优化聚类算法来执行主要计算,结合目标函数二阶差分的判定准则,通过聚类算法的自学习来确定最佳聚类数。实验结果显示,该方法能在复杂数据集上自动得到合理的聚类数目。     

Energy aware DAG scheduling on heterogeneous systems

We address the problem of scheduling directed a-cyclic task graph (DAG) on a heterogeneous distributed processor system with the twin objectives of minimizing finish time and energy consumption. Previous scheduling heuristics have assigned DAGs to processors to minimize overall run-time of the application. But applications on embedded systems, such as high performance DSP in image processing, multimedia, and wireless security, need schedules which use low energy too.     

Research on heterogeneous data integration model of group enterprise based on cluster computing

Cluster, consisting of a group of computers, is to act as a whole system to provide users with computer resources. Each computer is a node of this cluster. Cluster computer refers to a system consisting of a complete set of computers connected to each other. With the rapid development of computer technology, cluster computing technique with high performance–cost ratio has been widely applied in distributed parallel computing. For the large-scale close data in group enterprise, a heterogeneous data integration model was built under cluster environment based on cluster computing, XML technology and ontology theory. Such model could provide users unified and transparent access interfaces. Based on cluster computing, the work has solved the heterogeneous data integration problems by means of Ontology and XML technology. Furthermore, good application effect has been achieved compared with traditional data integration model. Furthermore, it was proved that this model improved the computing capacity of system, with high performance–cost ratio. Thus, it is hoped to provide support for decision-making of enterprise managers.     

An efficient heterogeneous blockchain-based online/offline signcryption systems for internet of vehicles

Cluster Computing - An essential goal of the Internet of Vehicles (IoV) is to allow vehicles’ peer connections and contact a service provider through a secure communication channel. However,...     

An effective method to increase solvability in biochemical systems using S-system

In this paper we propose an effective method to estimate the intrinsic values in an immobilized enzyme system, i.e., Michaelis constant K(m) and the maximum reaction rate V(m). We combine three techniques: (1) the non-linear least square method for estimating the kinetic values, (2) orthogonal collocation with the Gauss integration method, and (3) Newton-Raphson method (NRM) or S-system method (SSM) as Newton-like method. We build a procedure to combine the first two methods to estimate the unknown kinetic values in a system. We apply this procedure to solve the intrinsic kinetic parameters determination problem in an immobilized enzyme systems following Michaelis-Menten reaction. To demonstrate the effectiveness of the current method, we test their convergence performance in detail. The results show that the basin of attraction in the current method is extremely enlarged compared with that of the S-system alone. We suggest that the current method is one of the most effective ways to solve fairly complicated biochemical reaction systems in general.     

An investigation of competitive learning for autonomous cluster identification in embedded systems.

Robust signal processing for embedded systems requires the effective identification and representation of features within raw sensory data. This task is inherently difficult due to unavoidable long-term changes in the sensory systems and/or the sensed environment. In this paper we explore four variations of competitive learning and examine their suitability as an unsupervised technique for the automated identification of data clusters within a given input space. The relative performance of the four techniques is evaluated through their ability to effectively represent the structure underlying artificial and real-world data distributions. As a result of this study it was found that frequency sensitive competitive learning provides both reliable and efficient solutions to complex data distributions. As well, frequency sensitive and soft competitive learning are shown to exhibit properties which may permit the evolution of an appropriate network structure through the use of growing or pruning procedures.     

On tradeoff between the two compromise factors in assigning tasks on a cluster computing

This paper deals with the static task-assignment problem in a cluster computing system as follows: Given a task composed of a number of interacting modules, assign the task modules to the processors in the system to minimize the communication cost while balancing the processors’ loads. Because these two optimization criteria conflict with each other, a compromise needs to be made between them according to the given task type. This paper proposes a new cost function to evaluate the static task assignments and a heuristic algorithm for solving the transformed problem explicitly describing the tradeoff between the two goals. The simulation results showed that this approach outperforms the existing representative approach for a range of tasks and processing systems.     

Load balancing and optimization of distributed proximity effect correction on a temporally heterogeneous cluster

Grayscale electron-beam lithography is a technique widely used in transferring three-dimensional structures onto the resist layer or substrate. The proximity effect caused by electron scattering in the resist imposes a severe limitation on the ultimate spatial resolution attainable by e-beam lithography. Therefore, correction of the proximity effect is essential particularly for the fine-feature, high-density circuit patterns. However, the proximity effect correction is very time-consuming due to the intensive computation required in the correction procedure and a large size of circuit data to be processed. Hence, it is an ideal candidate for distributed computing where the otherwise-unused CPU cycles of a number of computers on a network (cluster) can be efficiently utilized. One of the characteristics of such a cluster is its heterogeneity, i.e., the available computing power varies with computer and/or time. This variation may degrade the performance of distributed computing significantly. In this paper, efficient distributed implementations of grayscale proximity effect correction on a temporally heterogeneous cluster are described with the main emphasis on static and dynamic load balancing schemes and their optimization through effective task partitioning methods. The experimental results obtained on a cluster of Sun workstations shared by multiple users are presented with detailed discussion.     

Sedimentation velocity analysis of highly heterogeneous systems

This article discusses several improvements to the van Holde-Weischet (vHW) method [Biopolymers 17 (1978) 1387] that address its capability to deal with sedimentation coefficient distributions spanning a large range of s values. The method presented here allows the inclusion of scans early and late in the experiment that ordinarily would need to be excluded from the analysis due to ultracentrifuge cell end effects. Scans late in the experiment are compromised by the loss of a defined plateau region and by back-diffusion from the bottom of the cell. Early scans involve partial boundaries that have not fully cleared the meniscus. In addition, a major refinement of the algorithm for determining the boundary fractions is introduced, taking into account different degrees of radial dilution for different species in the system. The method retains its desirable model-independent properties (the analysis of sedimentation data does not require prior knowledge of a user-imposed model or range of sedimentation coefficients) and reports diffusion-corrected s value distributions, which can be presented either in a histogram format or the traditional integral distribution format. Data analyzed with the traditional vHW method are compared with those of the improved method to demonstrate the benefit from the added information in the analysis.     

An effective technique for the processing of saliva for the analysis of leptin and adiponectin

The recovery of protein from saliva has been extensively investigated as a method to monitor health. The aim of this study was to compare filtration and centrifugation as two methods of saliva processing necessary for determining the levels of salivary leptin and adiponectin. Thirty-seven healthy patients (median age of 45 years; range 35–73) participated in the study. Unstimulated whole saliva was collected by a drooling technique. An aliquot was filtered using a Millex-Millipore^® (0.45 μm PVDF Dura Pore membrane) syringe and a second aliquot was centrifuged at 15 000 × g for 15 min at 4 °C. Leptin and adiponectin levels were analyzed using an ELISA kit for serum (RayBio^®, GA, USA) with minor modifications. Leptin and adiponectin levels following the filtration technique yielded comparable results with those after centrifugation. Correlation was observed between filtered and centrifuged salivary leptin levels ((r = 0.9155; 95% CI 0.8362–0.9573; p < 0.0001) with concordance correlation coefficient k 0.9114 (95% CI 0.8332–0.9539)). Less correlation was observed for adiponectin ((r = 0.5718; 95% CI 0.3041–0.7558; p = 0.0002) with concordance correlation coefficient k 0.5586 (95% CI 0.2977–0.7419)). Using a Bland–Altman plot, similar measurements for both adipocytokines were observed with mean difference within a 95% CI, and interpreted as no systematic differences between the two processing techniques. This study showed that filtration is an alternative saliva processing technique to retrieve supernatant for protein analysis. Filtered saliva yielded leptin and adiponectin concentrations comparable with those obtained from centrifuged saliva.     

An effective technique for enrichment and isolation of Candida cloacae mutants defective in alkane catabolism

Techniques are described which allow mutated populations of Candida cloacae to be enriched efficiently (up to 167-fold in one round of enrichment) for mutants deficient in the alkane degradation pathway (Alk-). Such mutants, as well as being of scientific importance in studies of the degradation pathway, are also of commercial interest because several of the degradative intermediates are of value to the chemical industry. The Alk- mutants were readily isolated by their inability to grow on agar plates supplied with hexadecane as sole carbon source. A total of 288 Alk- mutants were isolated from, effectively, 4 x 10(6) mutagen-treated cells. They were further characterized by replica-plating using palmitic acid (PA) or acetate (Ac) as sole carbon source. Preliminary screening studies showed that of the 84 Alk- PA- Ac+ mutants, most could accumulate dicarboxylic acids from hexadecane and palmitic acid and at least one mutant also produced 3-hydroxyhexadecanedioic acid. Of the 80 mutants characterized as Alk- PA+, 16 produced small amounts of hexadecanol.     

Disentanglement of heterogeneous dynamics in mixed lipid systems

Static phosphorous NMR has been a powerful technique for the study of model supramolecular phospholipid structures. Application to natural lipid bilayers with complex compositions, however, has been severely limited by the difficulty in deconvoluting overlapping broad lineshapes. We demonstrate a solution to this problem, using a global fit to a few slow magic-angle spinning spectra, in combination with an adaptation of Boltzmann statistics maximum entropy. The method provides a model-free means to characterize a heterogeneous mix of lipid dynamics via a distribution of ³¹P chemical shift anisotropies. It is used here to identify clear changes in membrane dynamics of a phosphatidylethanolamine and phosphatidylglycerol mixture, mimicking an Escherichia coli membrane upon addition of just 2% of the antimicrobial peptide maculatin 1.1. This illustration opens the prospect for investigation of arbitrarily complex natural lipid systems, important in many areas of biophysical chemistry and biomedicine.     

An effective data mining technique for reconstructing gene regulatory networks from time series expression data

Recent development in DNA microarray technologies has made the reconstruction of gene regulatory networks (GRNs) feasible. To infer the overall structure of a GRN, there is a need to find out how the expression of each gene can be affected by the others. Many existing approaches to reconstructing GRNs are developed to generate hypotheses about the presence or absence of interactions between genes so that laboratory experiments can be performed afterwards for verification. Since, they are not intended to be used to predict if a gene in an unseen sample has any interactions with other genes, statistical verification of the reliability of the discovered interactions can be difficult. Furthermore, since the temporal ordering of the data is not taken into consideration, the directionality of regulation cannot be established using these existing techniques. To tackle these problems, we propose a data mining technique here. This technique makes use of a probabilistic inference approach to uncover interesting dependency relationships in noisy, high-dimensional time series expression data. It is not only able to determine if a gene is dependent on another but also whether or not it is activated or inhibited. In addition, it can predict how a gene would be affected by other genes even in unseen samples. For performance evaluation, the proposed technique has been tested with real expression data. Experimental results show that it can be very effective. The discovered dependency relationships can reveal gene regulatory relationships that could be used to infer the structures of GRNs.     

Link contention-constrained scheduling and mapping of tasks and messages to a network of heterogeneous processors

In this paper, we consider the problem of scheduling and mapping precedence-constrained tasks to a network of heterogeneous processors. In such systems, processors are usually physically distributed, implying that the communication cost is considerably higher than in tightly coupled multiprocessors. Therefore, scheduling and mapping algorithms for such systems must schedule the tasks as well as the communication traffic by treating both the processors and communication links as equally important resources. We propose an algorithm that achieves these objectives and adapts its task scheduling and mapping decisions according to the given network topology. Just like tasks, messages are also scheduled and mapped to suitable links during the minimization of the finish times of tasks. Heterogeneity of processors is exploited by scheduling critical tasks to the fastest processors. Our experimental study has demonstrated that the proposed algorithm is efficient and robust, and yields consistent performance over a wide range of scheduling parameters. This revised version was published online in July 2006 with corrections to the Cover Date.     

Simultaneous cluster structure learning and estimation of heterogeneous graphs for matrix-variate fMRI data

Graphical models play an important role in neuroscience studies, particularly in brain connectivity analysis. Typically, observations/samples are from several heterogenous groups and the group membership of each observation/sample is unavailable, which poses a great challenge for graph structure learning. In this paper, we propose a method which can achieve Simultaneous Clustering and Estimation of Heterogeneous Graphs (briefly denoted as SCEHG) for matrix-variate functional magnetic resonance imaging (fMRI) data. Unlike the conventional clustering methods which rely on the mean differences of various groups, the proposed SCEHG method fully exploits the group differences of conditional dependence relationships among brain regions for learning cluster structure. In essence, by constructing individual-level between-region network measures, we formulate clustering as penalized regression with grouping and sparsity pursuit, which transforms the unsupervised learning into supervised learning. A modified difference of convex programming with the alternating direction method of multipliers (DC-ADMM) algorithm is proposed to solve the corresponding optimization problem. We also propose a generalized criterion to specify the number of clusters. Extensive simulation studies illustrate the superiority of the SCEHG method over some state-of-the-art methods in terms of both clustering and graph recovery accuracy. We also apply the SCEHG procedure to analyze fMRI data associated with attention-deficit hyperactivity disorder (ADHD), which illustrates its empirical usefulness.     

病毒--基因治疗中有效的载体系统

基因治疗面临的首要问题是如何选择适当的基因载体将具有治疗价值的基因导入靶细胞并使其有效表达,以达到治疗疾病的目的。目前基因治疗临床试验中采用的载体大多数为病毒载体。本文主要介绍基因治疗中常用的4种病毒载体的生物学特性,以及各个载体在基因治疗中的优缺点。