3,146 research outputs found
Scientific workflow execution reproducibility using cloud-aware provenance
Scientific experiments and projects such as CMS and neuGRIDforYou (N4U) are annually producing data of the order of Peta-Bytes. They adopt scientific workflows to analyse this large amount of data in order to extract meaningful information. These workflows are executed over distributed resources, both compute and storage in nature, provided by the Grid and recently by the Cloud. The Cloud is becoming the playing field for scientists as it provides scalability and on-demand resource provisioning. Reproducing a workflow execution to verify results is vital for scientists and have proven to be a challenge. As per a study (Belhajjame et al. 2012) around 80% of workflows cannot be reproduced, and 12% of them are due to the lack of information about the execution environment. The dynamic and on-demand provisioning capability of the Cloud makes this more challenging. To overcome these challenges, this research aims to investigate how to capture the execution provenance of a scientific workflow along with the resources used to execute the workflow in a Cloud infrastructure. This information will then enable a scientist to reproduce workflow-based scientific experiments on the Cloud infrastructure by re-provisioning the similar resources on the Cloud.Provenance has been recognised as information that helps in debugging, verifying and reproducing a scientific workflow execution. Recent adoption of Cloud-based scientific workflows presents an opportunity to investigate the suitability of existing approaches or to propose new approaches to collect provenance information from the Cloud and to utilize it for workflow reproducibility on the Cloud. From literature analysis, it was found that the existing approaches for Grid or Cloud do not provide detailed resource information and also do not present an automatic provenance capturing approach for the Cloud environment. To mitigate the challenges and fulfil the knowledge gap, a provenance based approach, ReCAP, has been proposed in this thesis. In ReCAP, workflow execution reproducibility is achieved by (a) capturing the Cloud-aware provenance (CAP), b) re-provisioning similar resources on the Cloud and re-executing the workflow on them and (c) by comparing the provenance graph structure including the Cloud resource information, and outputs of workflows. ReCAP captures the Cloud resource information and links it with the workflow provenance to generate Cloud-aware provenance. The Cloud-aware provenance consists of configuration parameters relating to hardware and software describing a resource on the Cloud. This information once captured aids in re-provisioning the same execution infrastructure on the Cloud for workflow re-execution. Since resources on the Cloud can be used in static or dynamic (i.e. destroyed when a task is finished) manner, this presents a challenge for the devised provenance capturing approach. In order to deal with these scenarios, different capturing and mapping approaches have been presented in this thesis. These mapping approaches work outside the virtual machine and collect resource information from the Cloud middleware, thus they do not affect job performance. The impact of the collected Cloud resource information on the job as well as on the workflow execution has been evaluated through various experiments in this thesis. In ReCAP, the workflow reproducibility isverified by comparing the provenance graph structure, infrastructure details and the output produced by the workflows. To compare the provenance graphs, the captured provenance information including infrastructure details is translated to a graph model. These graphs of original execution and the reproduced execution are then compared in order to analyse their similarity. In this regard, two comparison approaches have been presented that can produce a qualitative analysis as well as quantitative analysis about the graph structure. The ReCAP framework and its constituent components are evaluated using different scientific workflows such as ReconAll and Montage from the domains of neuroscience (i.e. N4U) and astronomy respectively. The results have shown that ReCAP has been able to capture the Cloud-aware provenance and demonstrate the workflow execution reproducibility by re-provisioning the same resources on the Cloud. The results have also demonstrated that the provenance comparison approaches can determine the similarity between the two given provenance graphs. The results of workflow output comparison have shown that this approach is suitable to compare the outputs of scientific workflows, especially for deterministic workflows
Scientific Workflow Repeatability through Cloud-Aware Provenance
The transformations, analyses and interpretations of data in scientific
workflows are vital for the repeatability and reliability of scientific
workflows. This provenance of scientific workflows has been effectively carried
out in Grid based scientific workflow systems. However, recent adoption of
Cloud-based scientific workflows present an opportunity to investigate the
suitability of existing approaches or propose new approaches to collect
provenance information from the Cloud and to utilize it for workflow
repeatability in the Cloud infrastructure. The dynamic nature of the Cloud in
comparison to the Grid makes it difficult because resources are provisioned
on-demand unlike the Grid. This paper presents a novel approach that can assist
in mitigating this challenge. This approach can collect Cloud infrastructure
information along with workflow provenance and can establish a mapping between
them. This mapping is later used to re-provision resources on the Cloud. The
repeatability of the workflow execution is performed by: (a) capturing the
Cloud infrastructure information (virtual machine configuration) along with the
workflow provenance, and (b) re-provisioning the similar resources on the Cloud
and re-executing the workflow on them. The evaluation of an initial prototype
suggests that the proposed approach is feasible and can be investigated
further.Comment: 6 pages; 5 figures; 3 tables in Proceedings of the Recomputability
2014 workshop of the 7th IEEE/ACM International Conference on Utility and
Cloud Computing (UCC 2014). London December 201
A Framework for QoS-aware Execution of Workflows over the Cloud
The Cloud Computing paradigm is providing system architects with a new
powerful tool for building scalable applications. Clouds allow allocation of
resources on a "pay-as-you-go" model, so that additional resources can be
requested during peak loads and released after that. However, this flexibility
asks for appropriate dynamic reconfiguration strategies. In this paper we
describe SAVER (qoS-Aware workflows oVER the Cloud), a QoS-aware algorithm for
executing workflows involving Web Services hosted in a Cloud environment. SAVER
allows execution of arbitrary workflows subject to response time constraints.
SAVER uses a passive monitor to identify workload fluctuations based on the
observed system response time. The information collected by the monitor is used
by a planner component to identify the minimum number of instances of each Web
Service which should be allocated in order to satisfy the response time
constraint. SAVER uses a simple Queueing Network (QN) model to identify the
optimal resource allocation. Specifically, the QN model is used to identify
bottlenecks, and predict the system performance as Cloud resources are
allocated or released. The parameters used to evaluate the model are those
collected by the monitor, which means that SAVER does not require any
particular knowledge of the Web Services and workflows being executed. Our
approach has been validated through numerical simulations, whose results are
reported in this paper
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
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