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Cloud computing owns merits of more efficiency and less cost in fields of information processing and service mode. Algorithms of workflow scheduling in the cloud can contribute to cutting cost and improving the quality of services, therefore, it has been a hot research topic. In this paper, the workflow technology in the cloud and the needs for cloud workflow scheduling are firstly introduced. Then, typical cloud workflow scheduling algorithms are analyzed and classified into three categories. In the end, typical cloud workflow scheduling research tools such as CloudSim, WorkflowSim and SwinFlow-Cloud are evaluated. Besides, we also analyze the existing problems of current workflow scheduling algorithm in the cloud and introduce the directions of the future research.

Cloud computing and Big Data, two disruptive trends at present, pose significant influence on current IT industry and research communities. Cloud computing provides massive computation power and storage capacity which enable users to deploy applications without infrastructure investment

Due to the uncertainty of software processes, statistic based schedule estimation and stochastic project scheduling both play significant roles in software project management. However, most current work investigates them independently without an integrated process to achieve on-time delivery for software development organisations. For such an issue, this paper proposes a two-stage probabilistic scheduling strategy which aims to decrease schedule overruns. Specifically, a probability based temporal consistency model is employed at the first pre-scheduling stage to support a negotiation between customers and project managers for setting balanced deadlines of individual software processes. At the second scheduling stage, an innovative genetic algorithm based scheduling strategy is proposed to minimise the overall completion time of multiple software processes with individual deadlines. The effectiveness of our strategy in achieving on-time delivery is verified with large scale simulation experiments.

In service oriented computing systems, a business process can be exposed as a composite service which consists of a set of logically connected sub-services. For each service in the composition, many service providers can offer the same function but may different QoS. In the general service composition, when a user submits a request, overall QoS constraints called end-to-end QoS composition's requirements, for example, time should be less than one hour, and cost should be less than 60$, can be transmitted at the same time. As such, how to effectively coordinate individual QoS constraints for single service to achieve the best overall QoS benefits without violating such end-to-end QoS constraint requirements has been a critical issue. With an increasing number of abstract services in a service composition, the possibility of execution path by selecting different service providers for each abstract service blows up exponentially. Therefore, service selection problem for service composition is a computational-hard problem, which can be regarded as a Multiple choice Multiple dimension Knapsack Problem (MMKP) that has been proved np-hard [1, 2, 3]. Recently, a lot of approaches such as graph-based techniques[4], runtime adaptation-based techniques[5], Service Level Agreement(SLA), negotiation and auction based techniques[6], Integer Linear Programming (ILP) based techniques[7] have been proposed to resolve overall QoS constraints for optimizing execution path in a service composition. No matter what the merits and the importance current existing methods have, they rely on directly judging constraint conditions to detect multiple paths for picking out a critical execution path, which easily produces a high-time complexity and even an unsatisfactory result in comparison to the best path. As such, the issue on resolving overall QoS constraints to achieve an optimal execution path has not yet been well addressed.

In scientific workflow systems, temporal consistency is critical to ensure the timely completion of workflow instances. To monitor and guarantee the correctness of temporal consistency, temporal constraints are often set and then verified. However, most current work adopts user specified temporal constraints without considering system performance, and hence may result in frequent temporal violations that deteriorate the overall workflow execution effectiveness. In this paper, with a systematic analysis of such problem, we propose a probabilistic strategy which is capable of setting coarse-grained and fine-grained temporal constraints based on the weighted joint distribution of activity durations. The strategy aims to effectively assign a set of temporal constraints which are well balanced between user requirements and system performance. The effectiveness of our work is demonstrated by an example scientific workflow in our scientific workflow system.

A WFMS(workflow management system) contains two basic elements: the workflow model and the workflow engine. It is important to verify workflow models before they are put to execution. Traditional workflow models mainly describe workflows either from the control perspective or from the data perspective. In fact, the control flow and the data flow are two important aspects for workflow modeling and they are not independent from each other. A new workflow modeling technique, named Dual Workflow Nets (DWF-nets), is proposed to explicitly model the control flow and data flow of workflow processes. Besides, the control/data flow interactions can be captured in DWF-nets. Moreover, the control/data inconsistency, which is neglected by traditional modeling techniques, can be detected by verification of DWF-nets.

Time management in workflow systems is crucial in determining and controlling the life cycle of a workflow process. Research on time modeling and analysis is important to guarantee workflow plans to be efficiently implemented and to make enterprises more competitive. Time WF-nets derived from time Petri nets and WF-nets are an effective model for workflow time management. In this paper, considering multiple instances of one workflow model run concurrently, we concern how to determine the workflow instance arrival cycle in order to make the waiting time of each instance as short as possible. Meanwhile the safety property of Time WF-nets is preserved. The key contribution of our work is twofold. First, instance arrival cycle is calculated in order to satisfy the safety property of Time WF-nets. Second, performance evaluation for Time WF-nets is proposed, which is based on instance arrival cycle and instance throughput cycle derived from safety analysis.

Email overload is a recent problem that there is increasingly difficulty people have faced to process the large number of emails received daily. Currently this problem becomes more and more serious and it has already affected the normal usage of email as a knowledge management tool. It has been recognized that categorizing emails into meaningful groups can greatly save cognitive load to process emails and thus this is an effective way to manage email overload problem. However, most current approaches still require significant human input when categorizing emails. In this paper we develop an automatic email clustering system, underpinned by a new nonparametric text clustering algorithm. This system does not require any predefined input parameters and can automatically generate meaningful email clusters. Experiments show our new algorithm outperforms existing text clustering algorithms with higher efficiency in terms of computational time and clustering quality measured by different gauges.

In grid workflow systems, existing representative checkpoint selection strategies, which are used to select checkpoints for verifying fixed-time constraints at run-time execution stage, often select some unnecessary checkpoints and ignore some necessary ones. Consequently, overall temporal verification efficiency and effectiveness can be severely impacted. In this paper, we propose a new strategy that selects only necessary and sufficient checkpoints dynamically along grid workflow execution. Specifically, we introduce a new concept of minimum time redundancy as a key reference value for checkpoint selection. We also investigate its relationships with fixed-time constraint consistency. Based on these relationships, we present our strategy which can improve overall temporal verification efficiency and effectiveness significantly.

Grid workflow systems aim to support large-scale complex e-science and e-business processes. Due to the complexity of these kinds of processes, to control the execution of them in terms of time in the grid workflow execution environments, multiple fixed-date temporal constraints often need to be set simultaneously. Hence the dependency problem between them must be considered and emphasised, which affects the verification effectiveness and efficiency of the fixed-date temporal constraints. However, current relevant workflow verification works do not take into consideration this dependency. Therefore, in this paper, we explore it in depth and analyse its impact on the verification of the fixed-date temporal constraints. Furthermore, based on the temporal dependency, we develop some new temporal verification methods and algorithms. The comparison and evaluation show that these new methods and algorithms make the verification more effective and efficient. All these analyses, new methods and algorithms further strengthen the grid workflow time management.

To verify fixed-time constraints in grid workflow systems, the consistency and inconsistency conditions have been defined in the conventional work. However, in this paper, we argue that although the conventional consistency condition is reasonable, the conventional inconsistency condition is too rigorous and covers several different situations. These situations which are handled conventionally by the same exception handling should be handled differently for cost saving. Therefore, we divide the conventional inconsistency into weak consistency,weak inconsistency and strong inconsistency and treat the conventional consistency as strong consistency. Correspondingly, we discuss how to verify them. Especially, for the weak consistency, we present a method on how to adjust it to the strong consistency. For the weak inconsistency, we analyse why it can be handled by simpler and more economical exception handling. The final comparison and discussion further demonstrate that our research can achieve better cost effectiveness than the conventional work.

The current workflow management systems do not take into consideration the mutual dependency between temporal constraints, which is affecting the effectiveness and efficiency of the temporal verification. In this paper, we investigate this dependency and its effects on the verification of temporal constraints. Furthermore, based on these analyses on the temporal dependency, we develop some new methods for more effective and efficient temporal verification. These analyses and new methods further reinforce the time management of the current workflow management systems.