A New Performance Evaluation Model and AHP-Based Analysis Method in Service-Oriented Workflow
In service-oriented architecture, services and workflows are closely related so that the research on service-oriented workflow attracts the attention of academia. Because of the loosely-coupled, autonomic and dynamic nature of service, the operation and performance evaluation of workflow meet some challenges, such as how to judge the quality of service (QoS) and what is the relation between QoS and workflow performance. In this paper we are going to address these challenges. First the definition of service is proposed, and the characteristics and operation mechanism of service-oriented workflow are presented. Then a service-oriented workflow performance evaluation model is described which combines the performance of the business system and IT system. The key performance indicators (KPI) are also depicted with their formal representation. Finally the improved Analytic Hierarchy Process is brought forward to analyze the correlation between different KPIs and select services.

title: An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling
abstract: This paper proposes an effective particle swarm optimization (PSO)-based memetic algorithm (MA) for the permutation flow shop scheduling problem (PFSSP) with the objective to minimize the maximum completion time, which is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed PSO-based MA (PSOMA), both PSO-based searching operators and some special local searching operators are designed to balance the exploration and exploitation abilities. In particular, the PSOMA applies the evolutionary searching mechanism of PSO, which is characterized by individual improvement, population cooperation, and competition to effectively perform exploration. On the other hand, the PSOMA utilizes several adaptive local searches to perform exploitation. First, to make PSO suitable for solving PFSSP, a ranked-order value rule based on random key representation is presented to convert the continuous position values of particles to job permutations. Second, to generate an initial swarm with certain quality and diversity, the famous Nawaz-Enscore-Ham (NEH) heuristic is incorporated into the initialization of population. Third, to balance the exploration and exploitation abilities, after the standard PSO-based searching operation, a new local search technique named NEH_1 insertion is probabilistically applied to some good particles selected by using a roulette wheel mechanism with a specified probability. Fourth, to enrich the searching behaviors and to avoid premature convergence, a simulated annealing (SA)-based local search with multiple different neighborhoods is designed and incorporated into the PSOMA. Meanwhile, an effective adaptive meta-Lamarckian learning strategy is employed to decide which neighborhood to be used in SA-based local search. Finally, to further enhance the exploitation ability, a pairwise-based local search is applied after the SA-based search. Simulation results based on benchmarks demonstrate the effectiveness of the PSOMA. Additionally, the effects of some parameters on optimization performances are also discussed

title: Designing Neural Networks Using PSO-Based Memetic Algorithm
abstract: This paper proposes an effective particle swarm optimization (PSO) based memetic algorithm (MA) for designing artificial neural network. In the proposed PSO-based MA (PSOMA), not only the evolutionary searching mechanism of PSO characterized by individual improvement plus population cooperation and competition is applied to perform the global search, but also several adaptive high-performance faster training algorithms are employed to enhance the local search, so that the exploration and exploitation abilities of PSOMA can be well balanced. Moreover, an effective adaptive Meta-Lamarckian learning strategy is employed to decide which local search method to be used so as to prevent the premature convergence and concentrate computing effort on promising neighbor solutions. Simulation results and comparisons demonstrate the effectiveness and efficiency of the proposed PSOMA.

title: Constrained Nonlinear State Estimation - A Differential Evolution Based Moving Horizon Approach
abstract: A solution is proposed to estimate the states in the nonlinear discrete time system. Moving Horizon Estimation (MHE) is used to obtain the approximated states by minimizing a criterion that is the Euclidean form of the difference between the estimated outputs and the measured ones over a finite time horizon. The differential evolution (DE) algorithm is incorporated into the implementation of MHE in order to solve the optimization problem which is presented as a nonlinear programming problem due to the constraints. The effectiveness of the approach is illustrated in simulated systems that have appeared in the moving horizon estimation literature.

title: Research on Architecture and Key Technology for Service-Oriented Workflow Performance Analysis
abstract: With the advent of SOA and Grid technology, the service has become the most important element of information systems. Because of the characteristic of service, the operation and performance management of workflow meet some new difficulties. Firstly a three-dimensional model of service is proposed. Then the characteristics of workflow in service-oriented environments are presented, based on which the workflow performance analysis architecture is described. As key technologies, workflow performance evaluation and analysis are discussed, including a multi-layer performance evaluation model and three kinds of performance analysis methods.

title: An Effective PSO-Based Hybrid Algorithm for Multiobjective Permutation Flow Shop Scheduling
abstract: This paper proposes a hybrid algorithm based on particle swarm optimization (PSO) for a multiobjective permutation flow shop scheduling problem, which is a typical NP-hard combinatorial optimization problem with strong engineering backgrounds. Not only does the proposed multiobjective algorithm (named MOPSO) apply the parallel evolution mechanism of PSO characterized by individual improvement, population cooperation, and competition to effectively perform exploration but it also utilizes several adaptive local search methods to perform exploitation. First, to make PSO suitable for solving scheduling problems, a ranked-order value (ROV) rule based on a random key technique to convert the continuous position values of particles to job permutations is presented. Second, a multiobjective local search based on the Nawaz-Enscore-Ham heuristic is applied to good solutions with a specified probability to enhance the exploitation ability. Third, to enrich the searching behavior and to avoid premature convergence, a multiobjective local search based on simulated annealing with multiple different neighborhoods is designed, and an adaptive meta-Lamarckian learning strategy is employed to decide which neighborhood will be used. Due to the fusion of multiple different searching operations, good solutions approximating the real Pareto front can be obtained. In addition, MOPSO adopts a random weighted linear sum function to aggregate multiple objectives to a single one for solution evaluation and for guiding the evolution process in the multiobjective sense. Due to the randomness of weights, searching direction can be enriched, and solutions with good diversity can be obtained. Simulation results and comparisons based on a variety of instances demonstrate the effectiveness, efficiency, and robustness of the proposed hybrid algorithm.

title: Numerical Experiments with Monte Carlo Methods and SPAI Preconditioner for Solving System of Linear Equations
abstract: In this paper we present the results of experiments comparing the performance of the mixed Monte Carlo algorithms and SPAI preconditener with BICGSTAB. The experiments are carried out on a Silicon Graphics ONYX2 machine. Based on our experiments, we conclude that these techniques are comparable from the point of view of robustness and rates of convergence, with the Monte Carlo approach performing better for some general cases and SPAI approach performing better in case of very sparse matrices.

title: BitTube: Case Study of a Web-Based Peer-Assisted Video-on-Demand System
abstract: Recent theoretical and simulation-based studies have confirmed the tremendous benefit of peer-to-peer (P2P)communication at reducing the cost of running a VoD service.To date, very limited effort has been paid to validate the concept of peer-assisted VoD service, especially in terms of system implementation and service deployment. In this paper, we present the case study of a peer-assisted video-on-demand (VoD) system. We designed and developed Bit-Tube, a BitTorrent-compliant VoD system. By combining client/server and P2P downloading, it supports seamless transition across the spectrum from pure client-server mode to BitTorrent mode. Within this framework, we experiment with a series of piece picking policies to enhance BitTubepsilas support to video streaming and promote locality-aware P2P downloading. We evaluate our system over PlanetLab,which hosts the user-side component of the BitTube system and emulates the global-scale user requests to the VoD service.

title: Scheduling strategies for mapping application workflows onto the grid
abstract: In this work, we describe new strategies for scheduling and executing workflow applications on grid resources using the GrADS [Ken Kennedy et al., 2002] infrastructure. Workflow scheduling is based on heuristic scheduling strategies that use application component performance models. The workflow is executed using a novel strategy to bind and launch the application onto heterogeneous resources. We apply these strategies in the context of executing EMAN, a bio-imaging workflow application, on the grid. The results of our experiments show that our strategy of performance model based, in-advance heuristic workflow scheduling results in 1.5 to 2.2 times better makespan than other existing scheduling strategies. This strategy also achieves optimal load balance across the different grid sites for this application.

title: MAC Performance and Improvement in Mobile Wireless Sensor Networks
abstract: Former Media Access Control (MAC) protocols for Wireless Sensor Networks (WSNs) are mainly for static networks. In the networks where mobile nodes exist, the performance will degrade greatly. In this paper the performance of MAC in mobile WSNs (mWSNs) is analyzed and improved algorithms are presented. Simulations validate the effects. A new MAC protocol also is presented for the scenario with more mobile nodes.

title: A holistic lexicon-based approach to opinion mining
abstract: One of the important types of information on the Web is the opinions expressed in the user generated content, e.g., customer reviews of products, forum posts, and blogs. In this paper, we focus on customer reviews of products. In particular, we study the problem of determining the semantic orientations (positive, negative or neutral) of opinions expressed on product features in reviews. This problem has many applications, e.g., opinion mining, summarization and search. Most existing techniques utilize a list of opinion (bearing) words (also called opinion lexicon) for the purpose. Opinion words are words that express desirable (e.g., great, amazing, etc.) or undesirable (e.g., bad, poor, etc) states. These approaches, however, all have some major shortcomings. In this paper, we propose a holistic lexicon-based approach to solving the problem by exploiting external evidences and linguistic conventions of natural language expressions. This approach allows the system to handle opinion words that are context dependent, which cause major difficulties for existing algorithms. It also deals with many special words, phrases and language constructs which have impacts on opinions based on their linguistic patterns. It also has an effective function for aggregating multiple conflicting opinion words in a sentence. A system, called Opinion Observer, based on the proposed technique has been implemented. Experimental results using a benchmark product review data set and some additional reviews show that the proposed technique is highly effective. It outperforms existing methods significantly