607 research outputs found
Usability of Scientific Workflow in Dynamically Changing Environment
Scientific workflow management systems are mainly data-flow oriented, which face several challenges due to the huge amount of data and the required computational capacity which cannot be predicted before enactment. Other problems may arise due to the dynamic access of the data storages or other data sources and the distributed nature of the scientific workflow computational infrastructures (cloud, cluster, grid, HPC), which status may change even during running of a single workflow instance. Many of these failures could be avoided with workflow management systems that provide provenance based dynamism and adaptivity to the unforeseen scenarios arising during enactment. In our work we summarize and categorize the failures that can arise in cloud environment during enactment and show the possibility of prediction and avoidance of failures with dynamic and provenance support
Technical Report: A Trace-Based Performance Study of Autoscaling Workloads of Workflows in Datacenters
To improve customer experience, datacenter operators offer support for
simplifying application and resource management. For example, running workloads
of workflows on behalf of customers is desirable, but requires increasingly
more sophisticated autoscaling policies, that is, policies that dynamically
provision resources for the customer. Although selecting and tuning autoscaling
policies is a challenging task for datacenter operators, so far relatively few
studies investigate the performance of autoscaling for workloads of workflows.
Complementing previous knowledge, in this work we propose the first
comprehensive performance study in the field. Using trace-based simulation, we
compare state-of-the-art autoscaling policies across multiple application
domains, workload arrival patterns (e.g., burstiness), and system utilization
levels. We further investigate the interplay between autoscaling and regular
allocation policies, and the complexity cost of autoscaling. Our quantitative
study focuses not only on traditional performance metrics and on
state-of-the-art elasticity metrics, but also on time- and memory-related
autoscaling-complexity metrics. Our main results give strong and quantitative
evidence about previously unreported operational behavior, for example, that
autoscaling policies perform differently across application domains and by how
much they differ.Comment: Technical Report for the CCGrid 2018 submission "A Trace-Based
Performance Study of Autoscaling Workloads of Workflows in Datacenters
Cloud computing resource scheduling and a survey of its evolutionary approaches
A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon
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