Running Accurate, Scalable, and Reproducible Simulations of Distributed Systems with WRENCH

Scientific workflows are used routinely in numerous scientific domains, and Workflow Management Systems (WMSs) have been developed to orchestrate and optimize workflow executions on distributed platforms. WMSs are complex software systems that interact with complex software infrastructures. Most WMS research and development activities rely on empirical experiments conducted with full-fledged software stacks on actual hardware platforms. Such experiments, however, are limited to hardware and software infrastructures at hand and can be labor- and/or time-intensive. As a result, relying solely on real-world experiments impedes WMS research and development. An alternative is to conduct experiments in simulation.

In this work, we present WRENCH, a WMS simulation framework, whose objectives are (i) accurate and scalable simulations; and (ii) easy simulation software development. WRENCH achieves its first objective by building on the SimGrid framework. While SimGrid is recognized for the accuracy and scalability of its simulation models, it only provides low-level simulation abstractions and thus large software development efforts are required when implementing simulators of complex systems. WRENCH thus achieves its second objective by providing high-level and directly reusable simulation abstractions on top of SimGrid. After describing and giving rationales for WRENCH’s software architecture and APIs, we present a case study in which we apply WRENCH to simulate the Pegasus production WMS. We report on ease of implementation, simulation accuracy, and simulation scalability so as to determine to which extent WRENCH achieves its two above objectives. We also draw both qualitative and quantitative comparisons with a previously proposed workflow simulator.

Empirical cumulative distribution function of task submit times (left) and task completion times (right) for sample real-world (“pegasus”) and simulated (“wrench” and “workflowsim”) executions of Montage-2.0 on AWS-m5.xlarge.

 

Reference to the paper:

  • [PDF] [DOI] H. Casanova, S. Pandey, J. Oeth, R. Tanaka, F. Suter, and R. Ferreira da Silva, “WRENCH: A Framework for Simulating Workflow Management Systems,” in 13th Workshop on Workflows in Support of Large-Scale Science (WORKS’18), 2018, p. 74–85.
    [Bibtex]
    @inproceedings{casanova-works-2018,
    title = {WRENCH: A Framework for Simulating Workflow Management Systems},
    author = {Casanova, Henri and Pandey, Suraj and Oeth, James and Tanaka, Ryan and Suter, Frederic and Ferreira da Silva, Rafael},
    booktitle = {13th Workshop on Workflows in Support of Large-Scale Science (WORKS'18)},
    year = {2018},
    pages = {74--85},
    doi = {10.1109/WORKS.2018.00013}
    }

 


 

69 views

Continue Reading

A Characterization of Workflow Management Systems for Extreme-Scale Applications

 

Automation of the execution of computational tasks is at the heart of improving scientific productivity. Scientific workflows have supported breakthroughs across several domains such as astronomy, physics, climate science, earthquake science, biology, and others. Scientific workflow management systems (WMS) are critical automation components that enable efficient and robust workflow execution across heterogeneous infrastructures.

In this paper, we seek to understand the requirements and characteristics of state-of-the-art WMSs for extreme-scale applications. We evaluate and classify 15 popular workflow systems and the applications they support designed specifically for extreme-scale workflows. We surveyed and classified workflow properties and management systems in terms of workflow execution models, heterogeneous computing environments, and data access methods. This paper has identified a number of properties that future WMSs need to support in order to meet extreme-scale requirements, as well as the re-search gaps in the state-of-the-art.

This paper has been published in the Future Generation Computer Systems, available online here.

 

Characterization of state-of-the-art WMSs. The classification highlights relevant characteristics to attain extreme-scale.

Abstract – Automation of the execution of computational tasks is at the heart of improving scientific productivity. Over the last years, scientific workflows have been established as an important abstraction that captures data processing and computation of large and complex scientific applications. By allowing scientists to model and express entire data processing steps and their dependencies,workflow management systems relieve scientists from the details of an application and manage its execution on a computational infrastructure. As the resource requirements of today’s computational and data science applications that process vast amounts of data keep increasing, there is a compelling case for a new generation of advances in high-performance computing, commonly termed as extreme-scale computing, which will bring forth multiple challenges for the design of workflow applications and management systems. This paper presents a novel characterization of workflow management systems using features commonly associated with extreme-scale computing applications. We classify 15 popular workflow management systems in terms of workflow execution models, heterogeneous computing environments, and data access methods. The paper also surveys workflow applications and identifies gaps for future research on the road to extreme-scale workflows and management systems.

 

Reference to the paper:

  • [PDF] [DOI] R. Ferreira da Silva, R. Filgueira, I. Pietri, M. Jiang, R. Sakellariou, and E. Deelman, “A Characterization of Workflow Management Systems for Extreme-Scale Applications,” Future Generation Computer Systems, vol. 75, p. 228–238, 2017.
    [Bibtex]
    @article{ferreiradasilva-fgcs-2017,
    title = {A Characterization of Workflow Management Systems for Extreme-Scale Applications},
    author = {Ferreira da Silva, Rafael and Filgueira, Rosa and Pietri, Ilia and Jiang, Ming and Sakellariou, Rizos and Deelman, Ewa},
    journal = {Future Generation Computer Systems},
    volume = {75},
    number = {},
    pages = {228--238},
    year = {2017},
    doi = {10.1016/j.future.2017.02.026}
    }

 

List of publications

 

1,323 views

Continue Reading