Trading off logging overhead and coordinating overhead to achieve efficient rollback recovery

In the rollback recovery of large‐scale long‐running applications in a distributed environment, pessimistic message logging protocols enable failed processes to recover independently, though at the expense of logging every message synchronously during fault‐free execution. In contrast, coordinated checkpointing protocols avoid message logging, but they are poor in scalability with a sharply increased coordinating overhead as the system grows. With the aim of achieving efficient rollback recovery by trading off logging overhead and coordinating overhead, this paper suggests a partitioning of the system into clusters, and then presents a scheme to implement the conversion between these overheads. Using the proposed conversion, coordination can be introduced to reduce the unbearable logging overhead found in some systems, whereas proper logging can be employed to alleviate the unacceptable coordinating overhead in others. Furthermore, heuristics are introduced to address the issue of how to partition the system into clusters in order to speed up the recovery process and to improve recovery efficiency. Performance evaluation results indicate that our scheme can lower the overall system overhead effectively. Copyright © 2008 John Wiley & Sons, Ltd.     

一种大型软件自动构建方案的设计与实现

为了解决大型软件在人工构建过程中需要消耗大量时间、占用大量人力资源和计算机资源的问题,提出一种运用Java、Ajax、适配器设计等技术的辅助自动构建工具Mantis构建方案,提高了软件自动构建的灵活性和稳定性。     

The HM-Nucleus: Distributed Kernel Design for the Homogeneous Multiprocessor

This modular, hierarchical operating system will capitalize on hardware features deemed important for image and digital signal processing: a fast LAN and shared memory.     

ADAPTIVE HYPER-FUZZY PARTITION PARTICLE SWARM OPTIMIZATION CLUSTERING ALGORITHM

This article presents an adaptive hyper-fuzzy partition particle swarm optimization clustering algorithm to optimally classify different geometrical structure data sets into correct groups. In this architecture, we use a novel hyper-fuzzy partition metric to improve the traditional common-used Euclidean norm metric clustering method. Since one fuzzy rule describes one pattern feature and implies the detection of one cluster center, it is encouraged to decrease the number of fuzzy rules with the hyper-fuzzy partition metric. According to the adaptive particle swarm optimization, it is very suitable to manage the clustering task for a complex, irregular, and high dimensional data set. To demonstrate the robustness of the proposed adaptive hyper-fuzzy partition particle swarm optimization clustering algorithms, various clustering simulations are experimentally compared with K-means and fuzzy c-means learning methods.     

HEURISTIC PARTICLE SWARM OPTIMIZATION LEARNING-BASED IMAGE COMPRESSION SYSTEM

Traditional LBG algorithm is a pure iterative optimization procedure to achieve the vector quantization (VQ) codebook, where an initial codebook is continually refined at every iteration to reduce the distortion between code-vectors and a given training data set. However, such interactive type learning algorithms will easily direct final results converging toward the local optimization while the high quality of the initial codebook is not available. In this article, an efficient heuristic-based learning method, called novel particle swarm optimization (NPSO), is proposed to design the proper codebook of VQ scheme that can develop the image compression system. To improve the performance of the basic PSO, the centroid updating machine applies the one step-size gradient descent learning step in the heuristic learning procedure. Additionally, the presented NPSO with advantages of the centroid updating machine is proposed to quickly achieve the near-optimal reconstructive image. For demonstrating the proposed NPSO learning scheme, the image with several horizontal grey bars is first applied to present the efficiency of the NPSO learning mechanism. LBG and NPSO learning methods are also applied to test the reconstructing performance in several type images “Lena,” “Airplane,” “Cameraman”, and “peppers.” In our experiments, the NPSO learning algorithm provides the higher performance than conventional LBG methods in the application of building image compression system.     

Recommendation based on rational inferences in collaborative filtering

In collaborative filtering, the existing memory-based methods make recommendations based on the overall consistency between two users or two items. The major concerns with these methods are: (1) they are sometimes being overly confident; (2) they are prone to disregard some useful information in the user profiles; (3) they often imply some untrustworthy inferences in making a prediction. This work investigates the drawbacks of these methods, and then proposes a collaborative filtering approach based on heuristic formulated inferences. The proposed approach is based on the fact that any two users may have some common interest genres as well as different ones. Different from most existing methods, this approach introduces a more reasonable similarity measure metric, considers users’ preferences and rating patterns, and promotes rational individual prediction, thus more comprehensively measures the relevance between user and item. Experimental results from two popular public datasets show that the proposed approach improves the prediction quality significantly over several other popular methods.     

Formation of first gold(III) complex of an N(4)-disubstituted thiosemicarbazone derived from 2-benzoylpyridine: structural and spectral studies

The reaction between gold(III) chloride(AuCl₃) and 2-benzoylpyridine N(4), N(4)-(butane-1,4-diyl) thiosemicarbazone (HBpypTsc) leads to an unexpected formation of a first gold(III) complex from an N(4)-disubstituted thiosemicarbazone derived from 2-benzoylpyridine. The crystal structure, spectroscopic characterization, and preliminary biological activity of [Au(III)(Cl)(BpypTsc)][Au(I)Cl₂] complex are discussed herein.     

A strategy for the optimization and economic evaluation of an ORC system for energy recovery

Abstract

A strategy for the performance prediction and economic evaluation of an ORC (organic Rankine cycle) system for power conversion is studied. Different assumptions and system boundaries are used to understand the constraints of the boundaries as well as their effects on the evaluation results. A series of methods, including cost estimation, operation’ research, sensitivity analysis and optimal design of heat exchangers, is employed in evaluating this thermal to power conversion system. It is found that the resultant variations in the economic evaluation, no matter what economic index is used, are mostly depending on the input parameters and the unpredictable variables at the preliminary design stage. The analysis strategy, the detailed procedures as well as the computer programs, is presented with a 400°F, 2.5 ×10⁷ Btu/hr flue gas recovery case as an illustration. This study shows a large variation of payback period ranging from 5 to 15 years, depending on different assumptions, operating conditions, and system boundaries. This analysis recommends a more conservative approach to evaluate an energy recovery project to avoid an inappropriate judgement of an engineering design project.     

Size Selective Synthesis of Superparamagnetic Nanoparticles in Thin Fluids under Continuous Flow Conditions

Continuous flow spinning disc processing (SDP), which has extremely rapid mixing under plug flow conditions, effective heat and mass transfer, allowing high throughput with low wastage solvent efficiency, is effective in gaining access to superparamagnetic Fe₃O₄ nanoparticles at room temperature. These are formed by passing ammonia gas over a thin aqueous film of Fe^2+/3+ which is introduced through a jet feed close to the centre of a rapidly rotating disc (500 to 2500 rpm), the particle size being controlled with a narrow size distribution over the range 5 nm to 10 nm, and the material having very high saturation magnetizations, in the range 68–78 emu g^?1.     

A pre‐emption framework for UMTS compatible satellite system supporting multimedia traffic

The pre‐emption procedure is an important part of the radio resource management when dealing with the emergency traffic. It allows resources to be allocated to higher priority connections by pre‐empting lower priority connections. The provision of the pre‐emption mechanism becomes much more important in the case of satellite systems such as the Inmarsat Broadband Global Area Network system, which aids in providing the communication during a catastrophe. This paper focuses on the pre‐emption framework for a Universal Mobile Telecommunications System‐based satellite systems. Three algorithms have been proposed, Greedy, SubSetSum and Fuzzy pre‐emption algorithm. Extensive simulations are carried out for the three algorithms, and their performances are compared against each other. Simulation results show that the Fuzzy pre‐emption algorithm performs better than the other two algorithms. Copyright © 2014 John Wiley & Sons, Ltd.