41,628 research outputs found
Enabling Adaptive Grid Scheduling and Resource Management
Wider adoption of the Grid concept has led to an increasing amount of federated
computational, storage and visualisation resources being available to scientists and
researchers. Distributed and heterogeneous nature of these resources renders most of the
legacy cluster monitoring and management approaches inappropriate, and poses new
challenges in workflow scheduling on such systems. Effective resource utilisation monitoring
and highly granular yet adaptive measurements are prerequisites for a more efficient Grid
scheduler. We present a suite of measurement applications able to monitor per-process
resource utilisation, and a customisable tool for emulating observed utilisation models. We
also outline our future work on a predictive and probabilistic Grid scheduler. The research is
undertaken as part of UK e-Science EPSRC sponsored project SO-GRM (Self-Organising
Grid Resource Management) in cooperation with BT
Event-Oriented Dynamic Adaptation of Workflows: Model, Architecture and Implementation
Workflow management is widely accepted as a core technology to support long-term business processes in heterogeneous and distributed environments. However, conventional workflow management systems do not provide sufficient flexibility support to cope with the broad range of failure situations that may occur during workflow execution. In particular, most systems do not allow to dynamically adapt a workflow due to a failure situation, e.g., to dynamically drop or insert execution steps. As a contribution to overcome these limitations, this dissertation introduces the agent-based workflow management system AgentWork. AgentWork supports the definition, the execution and, as its main contribution, the event-oriented and semi-automated dynamic adaptation of workflows. Two strategies for automatic workflow adaptation are provided. Predictive adaptation adapts workflow parts affected by a failure in advance (predictively), typically as soon as the failure is detected. This is advantageous in many situations and gives enough time to meet organizational constraints for adapted workflow parts. Reactive adaptation is typically performed when predictive adaptation is not possible. In this case, adaptation is performed when the affected workflow part is to be executed, e.g., before an activity is executed it is checked whether it is subject to a workflow adaptation such as dropping, postponement or replacement. In particular, the following contributions are provided by AgentWork: A Formal Model for Workflow Definition, Execution, and Estimation: In this context, AgentWork first provides an object-oriented workflow definition language. This language allows for the definition of a workflow\u92s control and data flow. Furthermore, a workflow\u92s cooperation with other workflows or workflow systems can be specified. Second, AgentWork provides a precise workflow execution model. This is necessary, as a running workflow usually is a complex collection of concurrent activities and data flow processes, and as failure situations and dynamic adaptations affect running workflows. Furthermore, mechanisms for the estimation of a workflow\u92s future execution behavior are provided. These mechanisms are of particular importance for predictive adaptation. Mechanisms for Determining and Processing Failure Events and Failure Actions: AgentWork provides mechanisms to decide whether an event constitutes a failure situation and what has to be done to cope with this failure. This is formally achieved by evaluating event-condition-action rules where the event-condition part describes under which condition an event has to be viewed as a failure event. The action part represents the necessary actions needed to cope with the failure. To support the temporal dimension of events and actions, this dissertation provides a novel event-condition-action model based on a temporal object-oriented logic. Mechanisms for the Adaptation of Affected Workflows: In case of failure situations it has to be decided how an affected workflow has to be dynamically adapted on the node and edge level. AgentWork provides a novel approach that combines the two principal strategies reactive adaptation and predictive adaptation. Depending on the context of the failure, the appropriate strategy is selected. Furthermore, control flow adaptation operators are provided which translate failure actions into structural control flow adaptations. Data flow operators adapt the data flow after a control flow adaptation, if necessary. Mechanisms for the Handling of Inter-Workflow Implications of Failure Situations: AgentWork provides novel mechanisms to decide whether a failure situation occurring to a workflow affects other workflows that communicate and cooperate with this workflow. In particular, AgentWork derives the temporal implications of a dynamic adaptation by estimating the duration that will be needed to process the changed workflow definition (in comparison with the original definition). Furthermore, qualitative implications of the dynamic change are determined. For this purpose, so-called quality measuring objects are introduced. All mechanisms provided by AgentWork include that users may interact during the failure handling process. In particular, the user has the possibility to reject or modify suggested workflow adaptations. A Prototypical Implementation: Finally, a prototypical Corba-based implementation of AgentWork is described. This implementation supports the integration of AgentWork into the distributed and heterogeneous environments of real-world organizations such as hospitals or insurance business enterprises
BioWorkbench: A High-Performance Framework for Managing and Analyzing Bioinformatics Experiments
Advances in sequencing techniques have led to exponential growth in
biological data, demanding the development of large-scale bioinformatics
experiments. Because these experiments are computation- and data-intensive,
they require high-performance computing (HPC) techniques and can benefit from
specialized technologies such as Scientific Workflow Management Systems (SWfMS)
and databases. In this work, we present BioWorkbench, a framework for managing
and analyzing bioinformatics experiments. This framework automatically collects
provenance data, including both performance data from workflow execution and
data from the scientific domain of the workflow application. Provenance data
can be analyzed through a web application that abstracts a set of queries to
the provenance database, simplifying access to provenance information. We
evaluate BioWorkbench using three case studies: SwiftPhylo, a phylogenetic tree
assembly workflow; SwiftGECKO, a comparative genomics workflow; and RASflow, a
RASopathy analysis workflow. We analyze each workflow from both computational
and scientific domain perspectives, by using queries to a provenance and
annotation database. Some of these queries are available as a pre-built feature
of the BioWorkbench web application. Through the provenance data, we show that
the framework is scalable and achieves high-performance, reducing up to 98% of
the case studies execution time. We also show how the application of machine
learning techniques can enrich the analysis process
Resist, comply or workaround? An examination of different facets of user engagement with information systems
This paper provides a summary of studies of user resistance to Information Technology (IT) and identifies workaround activity as an understudied and distinct, but related, phenomenon. Previous categorizations of resistance have largely failed to address the relationships between the motivations for divergences from procedure and the associated workaround activity. This paper develops a composite model of resistance/workaround derived from two case study sites. We find four key antecedent conditions derived from both positive and negative resistance rationales and identify associations and links to various resultant workaround behaviours and provide supporting Chains of Evidence from two case studies
Predictive Monitoring of Business Processes
Modern information systems that support complex business processes generally
maintain significant amounts of process execution data, particularly records of
events corresponding to the execution of activities (event logs). In this
paper, we present an approach to analyze such event logs in order to
predictively monitor business goals during business process execution. At any
point during an execution of a process, the user can define business goals in
the form of linear temporal logic rules. When an activity is being executed,
the framework identifies input data values that are more (or less) likely to
lead to the achievement of each business goal. Unlike reactive compliance
monitoring approaches that detect violations only after they have occurred, our
predictive monitoring approach provides early advice so that users can steer
ongoing process executions towards the achievement of business goals. In other
words, violations are predicted (and potentially prevented) rather than merely
detected. The approach has been implemented in the ProM process mining toolset
and validated on a real-life log pertaining to the treatment of cancer patients
in a large hospital
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