94 research outputs found
A Taxonomy of Workflow Management Systems for Grid Computing
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.Comment: 29 pages, 15 figure
Dynamic workflow management for large scale scientific applications
The increasing computational and data requirements of scientific applications have made the usage of large clustered systems as well as distributed resources inevitable. Although executing large applications in these environments brings increased performance, the automation of the process becomes more and more challenging. The use of complex workflow management systems has been a viable solution for this automation process. In this thesis, we study a broad range of workflow management tools and compare their capabilities especially in terms of dynamic and conditional structures they support, which are crucial for the automation of complex applications. We then apply some of these tools to two real-life scientific applications: i) simulation of DNA folding, and ii) reservoir uncertainty analysis. Our implementation is based on Pegasus workflow planning tool, DAGMan workflow execution system, Condor-G computational scheduler, and Stork data scheduler. The designed abstract workflows are converted to concrete workflows using Pegasus where jobs are matched to resources; DAGMan makes sure these jobs execute reliably and in the correct order on the remote resources; Condor-G performs the scheduling for the computational tasks and Stork optimizes the data movement between different components. Integrated solution with these tools allows automation of large scale applications, as well as providing complete reliability and efficiency in executing complex workflows. We have also developed a new site selection mechanism on top of these systems, which can choose the most available computing resources for the submission of the tasks. The details of our design and implementation, as well as experimental results are presented
LOGOS: Enabling Local Resource Managers for the Efficient Support of Data-Intensive Workflows within Grid Sites
In this study we discuss how to enable grid sites for the support of data-intensive workflows. Usually, within grid sites, tasks and resources are administrated by local resource managers (LRMs). Many of LRMs have been designed for managing compute-intensive applications. Therefore, data-intensive workflow applications might not perform well on such environments due to the number and size of data transfers between tasks. To improve the performance of such kind of applications it is necessary to redefine the scheduling policies integrated on LRMs. This paper proposes a novel scheme for efficiently supporting data-intensive workflows in LRMs within grid sites. Such scheme is partially implemented in our grid middleware LOGOS and used to improve the performance of a well known LRM: HTCondor. The core of LOGOS is a novel communication-aware scheduling algorithm (PPSA) capable of finding near-optimal solutions. Experiments conducted in this study showed that our approach leads to performance improvements up to 52 % in the management of data-intensive workflow applications
How Workflow Engines Should Talk to Resource Managers: A Proposal for a Common Workflow Scheduling Interface
Scientific workflow management systems (SWMSs) and resource managers together
ensure that tasks are scheduled on provisioned resources so that all
dependencies are obeyed, and some optimization goal, such as makespan
minimization, is fulfilled. In practice, however, there is no clear separation
of scheduling responsibilities between an SWMS and a resource manager because
there exists no agreed-upon separation of concerns between their different
components. This has two consequences. First, the lack of a standardized API to
exchange scheduling information between SWMSs and resource managers hinders
portability. It incurs costly adaptations when a component should be replaced
by another one (e.g., an SWMS with another SWMS on the same resource manager).
Second, due to overlapping functionalities, current installations often
actually have two schedulers, both making partial scheduling decisions under
incomplete information, leading to suboptimal workflow scheduling.
In this paper, we propose a simple REST interface between SWMSs and resource
managers, which allows any SWMS to pass dynamic workflow information to a
resource manager, enabling maximally informed scheduling decisions. We provide
an exemplary implementation of this API for Nextflow as an SWMS and Kubernetes
as a resource manager. Our experiments with nine real-world workflows show that
this strategy reduces makespan by up to 25.1% and 10.8% on average compared to
the standard Nextflow/Kubernetes configuration. Furthermore, a more widespread
implementation of this API would enable leaner code bases, a simpler exchange
of components of workflow systems, and a unified place to implement new
scheduling algorithms.Comment: Paper accepted in: 2023 23rd IEEE International Symposium on Cluster,
Cloud and Internet Computing (CCGrid
Optimization and Management of Large-scale Scientific Workflows in Heterogeneous Network Environments: From Theory to Practice
Next-generation computation-intensive scientific applications feature large-scale computing workflows of various structures, which can be modeled as simple as linear pipelines or as complex as Directed Acyclic Graphs (DAGs). Supporting such computing workflows and optimizing their end-to-end network performance are crucial to the success of scientific collaborations that require fast system response, smooth data flow, and reliable distributed operation.We construct analytical cost models and formulate a class of workflow mapping problems with different mapping objectives and network constraints. The difficulty of these mapping problems essentially arises from the topological matching nature in the spatial domain, which is further compounded by the resource sharing complicacy in the temporal dimension. We provide detailed computational complexity analysis and design optimal or heuristic algorithms with rigorous correctness proof or performance analysis. We decentralize the proposed mapping algorithms and also investigate these optimization problems in unreliable network environments for fault tolerance.To examine and evaluate the performance of the workflow mapping algorithms before actual deployment and implementation, we implement a simulation program that simulates the execution dynamics of distributed computing workflows. We also develop a scientific workflow automation and management platform based on an existing workflow engine for experimentations in real environments. The performance superiority of the proposed mapping solutions are illustrated by extensive simulation-based comparisons with existing algorithms and further verified by large-scale experiments on real-life scientific workflow applications through effective system implementation and deployment in real networks
Workflow task clustering for best effort systems with
ABSTRACT Many scientific workflows are composed of fine computational granularity tasks, yet they are composed of thousands of them and are data intensive in nature, thus requiring resources such as the TeraGrid to execute efficiently. In order to improve the performance of such applications, we often employ task clustering techniques to increase the computational granularity of workflow tasks. The goal is to minimize the completion time of the workflow by reducing the impact of queue wait times. In this paper, we examine the performance impact of the clustering techniques using the Pegasus workflow management system. Experiments performed using an astronomy workflow on the NCSA TeraGrid cluster show that clustering can achieve a significant reduction in the workflow completion time (upto 97%)
High-throughput Scientific Workflow Scheduling under Deadline Constraint in Clouds
Cloud computing is a paradigm shift in service delivery that promises a leap in efficiency and flexibility in using computing resources. As cloud infrastructures are widely deployed around the globe, many data- and computeintensive scientific workflows have been moved from traditional high-performance computing platforms and grids to clouds. With the rapidly increasing number of cloud users in various science domains, it has become a critical task for the cloud service provider to perform efficient job scheduling while still guaranteeing the workflow completion time as specified in the Service Level Agreement (SLA). Based on practical models for cloud utilization, we formulate a delay-constrained workflow optimization problem to maximize resource utilization for high system throughput and propose a two-step scheduling algorithm to minimize the cloud overhead under a user-specified execution time bound. Extensive simulation results illustrate that the proposed algorithm achieves lower computing overhead or higher resource utilization than existing methods under the execution time bound, and also significantly reduces the total workflow execution time by strategically selecting appropriate mapping nodes for prioritized modules
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