Lizhen Cui

A Multiple QoS Constrained Scheduling Strategy of Multiple Workflows for Cloud Computing

Cloud computing has gained popularity in recent times. As a cloud must provide services to many users at the same time and different users have different QoS requirements, the scheduling strategy should be developed for multiple workflows with different QoS requirements. In this paper, we introduce a Multiple QoS Constrained Scheduling Strategy of Multi-Workflows (MQMW) to address this problem. The strategy can schedule multiple workflows which are started at any time and the QoS requirements are taken into account. Experimentation shows that our strategy is able to increase the scheduling success rate significantly.

Linear unsupervised hashing for ANN search in Euclidean space

Approximate nearest neighbors (ANN) search for large scale data has attracted considerable attention due to the fact that large amounts of data are easily available. Recently, hashing has been widely adopted for similarity search because of its good potential for low storage cost and fast query speed. Among of them, when semantic similarity information is available, supervised hashing methods show better performance than unsupervised ones. However, supervised hashing methods need explicit similarity information which is not available in some scenarios. In addition, they have the problems of difficult optimization and time consuming for training, which make them unpracticable to large scale data. In this paper, we propose an unsupervised hashing method - Unsupervised Euclidean Hashing (USEH), which learns and generates hashing codes to preserve the Euclidean distance relationship between data. Specifically, USEH first utilizes Locality-Sensitive Hashing (LSH) to generate pseudo labels; then, it adopts a sequential learning strategy to learn the hash functions, one bit at a time, which can generate very discriminative codes. Moreover, USEH avoids explicitly computing the similarity matrix by decomposing it into the product of a label matrix and its transposition, which makes the training complexity of USEH linear to the size of training samples when the number of training samples is much greater than the dimension of feature. Thus, it can efficiently work on large scale data. We test USEH on two large scale datasets - SIFT1M and GIST1M. Experimental results show that USEH is comparable to state-of-the-art unsupervised hashing methods.

A New QoS Prediction Approach Based on User Clustering and Regression Algorithms

QoS has become an important measure for web service selection. In this paper, we present an approach which can provide the approximate QoS value for users, and support finding the optimal web service. Firstly, it clusters the users based on location and network condition, then according to the QoS historical statistics of users in the same cluster, uses the linear regression algorithm to predict the QoS value based on invocation time and workload.

A Dynamic Web Service Composition Method Based on Viterbi Algorithm

In cloud computing, it is an urgent problem to provide stable composition service which can satisfy the personalized requirements for large scale users. This paper takes several aspects of web service into consideration, including Quality of Service (QoS), user preference and the service relationships and proposes a method based viterbi algorithm to reason out the global optimal solution of web composition service. Result shows our method holds executive efficiency, stability as well as outstanding selecting result.

An Extended Matching Method for Semantic Web Service in Collaboration Environment

The emerging semantic web service provides a promising way to address the challenge of building collaborative design system over heterogeneous resources. Efficient services discovery method is a significant challenge for semantic web service. This paper proposes a semantic web service matching algorithm based not only on functions of services but also the world state related to services which is most important for web service in collaboration environment. Functions matching make sure that the selected services can fulfill the tasks users required, while the state matching can make sure that the selected services are executable and applying the selected services achieves the desired effects.

Multi-tenant Service Composition Based on Granularity Computing

As a common delivery model in cloud computing, SaaS applications are becoming increasingly popular. With the increasing of users individual and diverse requirements, multi-tenancy has been the main delivery model for SaaS applications in future. Meanwhile, in order to adapt to the complex application development, application components had become modularization and fine-grained. Thus, new applications can be built through assembling those components quickly and agilely. So service composition for multi-tenant is the key to build application flexibly. This paper proposes a service granularity space for multi-tenant service composition based on granularity computing, which provides a semantic basis for multi-tenant service composition. The service granularity space supports the characteristic of hierarchy, inheritance, evolved correlation and versioning, effectively responding to the challenge of service composition for cloud computing SaaS applications. On the one hand, the service granularity space makes it possible to change disorder services into the hierarchy and ordered clustering services, and on the other hand, it makes it easy to develop multi-tenant SaaS applications by combining customization and evolution according to individual and diverse requirements. Our final experiments further demonstrate the feasibility and the efficiency of our proposed approaches.

A Collaborative Framework for Exception Handling in Business Process Execution

Web services can be managed and coordinated by business processes while computer application systems are more and more based on Internet. Services binding in these business processes evolve autonomously and their coupling is highly loose. Accordingly, exception handling of business processes execution under the Internet environment is one key to ensure the reliability of application systems. In this paper, we analyze the types of exceptions in business process execution and propose several corresponding handling strategies. We also analyze the role of chorographic which business process plays during execution and present a collaborative approach for exception handling in business process execution by cooperative works. In our opinion, cooperative works are based on strategies ontology which is related to different types of exception. We describe the approach and illustrate it with examples to show its functions. The major concern addressed by our approach is the support to anticipated and unexpected exceptions handling with cooperative works.

Cloud Data Management for Scientific Workflows: Research Issues, Methodologies, and State-of-the-Art

Data-intensive scientific applications are posing many challenges in distributed computing systems. In the scientific field, the application data are expected to double every year over the next decade and further. With this continuing data explosion, high performance computing systems are needed to store and process data efficiently, and workflow technologies are facilitated to automate these scientific applications. Scientific workflows are typically very complex. They usually have a large number of tasks and need a long time for execution. Running scientific workflow applications usually need not only high performance computing resources but also massive storage. The emergence of cloud computing technologies offers a new way to develop scientific workflow systems. Scientists can upload their data and launch their applications on the scientific cloud workflow systems from everywhere in the world via the Internet, and they only need to pay for the resources that they use for their applications. As all the data are managed in the cloud, it is easy to share data among scientists. This kind of model is very convenient for users, but remains a big challenge to the system. This paper proposes several research topics of data management in scientific cloud workflow systems, and discusses their research methodologies and state-of-the-art solutions.

A dataset of human decision-making in teamwork management

Today, most endeavours require teamwork by people with diverse skills and characteristics. In managing teamwork, decisions are often made under uncertainty and resource constraints. The strategies and the effectiveness of the strategies different people adopt to manage teamwork under different situations have not yet been fully explored, partially due to a lack of detailed large-scale data. In this paper, we describe a multi-faceted large-scale dataset to bridge this gap. It is derived from a game simulating complex project management processes. It presents the participants with different conditions in terms of team members’ capabilities and task characteristics for them to exhibit their decision-making strategies. The dataset contains detailed data reflecting the decision situations, decision strategies, decision outcomes, and the emotional responses of 1,144 participants from diverse backgrounds. To our knowledge, this is the first dataset simultaneously covering these four facets of decision-making. With repeated measurements, the dataset may help establish baseline variability of decision-making in teamwork management, leading to more realistic decision theoretic models and more effective decision support approaches.

An Evolutionary Framework for Multi-agent Organizations

The organizational design of a multi-agent system (MAS) is important for its efficiency, adaptability and robustness. However, finding suitable organizational structures for different MASs is a challenging problem. In this paper, we propose a Framework of Evolutionary Optimization for Agent Organizations (FEVOR) based on Genetic Programming for optimizing tree-structured MASs. FEVOR employs a flexible representation of organizations and may be applied to a wide range of organizational forms such as pure hierarchies, holarchies, and federations. Compared to existing work, FEVOR is capable of efficient quantitative search and less vulnerable to stalling at local optima due to its non-greedy nature. Extensive experiments for optimizing an information retrieval system have been conducted to demonstrate the advantages of FEVOR in generating suitable MAS organizations for adaptive environments.