With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.

Grid technologies have progressed towards a service-oriented paradigm that enables a new way of service provisioning based on utility computing models, which are capable of supporting diverse computing services. It facilitates scientific applications to take advantage of computing resources distributed world wide to enhance the capability and performance. Many scientific applications in areas such as bioinformatics and astronomy require workflow processing in which tasks are executed based on their control or data dependencies. Scheduling such interdependent tasks on utility Grid environments need to consider users' QoS requirements. In this paper, we present a genetic algorithm approach to address scheduling optimization problems in workflow applications, based on two QoS constraints, deadline and budget.

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With cloud storage services, users can remotely store their data to the cloud and realize the data sharing with others. Remote data integrity auditing is proposed to guarantee the integrity of the data stored in the cloud. In some common cloud storage systems such as the electronic health records system, the cloud file might contain some sensitive information. The sensitive information should not be exposed to others when the cloud file is shared. Encrypting the whole shared file can realize the sensitive information hiding, but will make this shared file unable to be used by others. How to realize data sharing with sensitive information hiding in remote data integrity auditing still has not been explored up to now. In order to address this problem, we propose a remote data integrity auditing scheme that realizes data sharing with sensitive information hiding in this paper. In this scheme, a sanitizer is used to sanitize the data blocks corresponding to the sensitive information of the file and transforms these data blocks' signatures into valid ones for the sanitized file. These signatures are used to verify the integrity of the sanitized file in the phase of integrity auditing. As a result, our scheme makes the file stored in the cloud able to be shared and used by others on the condition that the sensitive information is hidden, while the remote data integrity auditing is still able to be efficiently executed. Meanwhile, the proposed scheme is based on identity-based cryptography, which simplifies Manuscript

Cloud computing is one of the hottest topics in the field of information systems. In this paper, we conduct a citation and co-citation analysis on cloud computing research published in the 11-year period from 2004 to 2014. A total of 214 papers were selected from 20 leading academic journals in IS and management and 2 prominent international IS conferences for our analysis. In the citation analysis, we rely on the degree centrality and betweenness centrality to identify 41 important papers. In addition, our main path analysis reveals three development stages of cloud computing research: the incubation stage, the exploration stage, and the burgeoning stage. In the co-citation analysis, we employ a principal component factor analysis of the co-citation matrix to identify six major research themes: foundations, SaaS model, security and risk, literature review, adoption and impacts, and modeling. This is among the first studies to examine the knowledge structure of cloud computing research in the IS discipline by using evidence-based analysis methods. Recommendations for future research directions in cloud computing are provided based on our analysis.

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Abstract-Utility Grids create an infrastructure for enabling users to consume services transparently over a global network. When optimizing workflow execution on utility Grids, we need to consider multiple Quality of Service (QoS) parameters including service prices and execution time. These optimization objectives may be in conflict. In this paper, we have proposed a workflow execution planning approach using multi-objective evolutionary algorithms (MOEAs). Our goal was to generate a set of trade-off scheduling solutions according to the users QoS requirements. The alternative trade-off solutions offer more flexibility to users when estimating their QoS requirements of workflow executions. Simulation results show that MOEAs are able to find a range of compromise solutions in a short computational time.