Using Simple PID Controllers to Prevent and Mitigate Faults in Scientific Workflows


Presentation held at the 11th Workflows in Support of Large-Scale Science, 2016
Salt Lake City, UT, USA – SuperComputing’16

Abstract – Scientific workflows have become mainstream for conducting large-scale scientific research. As a result, many workflow applications and Workflow Management Systems (WMSs) have been developed as part of the cyberinfrastructure to allow scientists to execute their applications seamlessly on a range of distributed platforms. In spite of many success stories, a key challenge for running workflows in distributed systems is failure prediction, detection, and recovery. In this paper, we propose an approach to use control theory developed as part of autonomic computing to predict failures before they happen, and mitigated them when possible. The proposed approach applying the proportional-integral-derivative controller (PID controller) control loop mechanism, which is widely used in industrial control systems, to mitigate faults by adjusting the inputs of the controller. The PID controller aims at detecting the possibility of a fault far enough in advance so that an action can be performed to prevent it from happening. To demonstrate the feasibility of the approach, we tackle two common execution faults of the Big Data era—data storage overload and memory overflow. We define, implement, and evaluate simple PID controllers to autonomously manage data and memory usage of a bioinformatics workflow that consumes/produces over 4.4TB of data, and requires over 24TB of memory to run all tasks concurrently. Experimental results indicate that workflow executions may significantly benefit from PID controllers, in particular under online and unknown conditions. Simulation results show that nearly-optimal executions (slowdown of 1.01) can be attained when using our proposed method, and faults are detected and mitigated far in advance of their occurrence.

 

Related Publication

  • [PDF] R. Ferreira da Silva, R. Filgueira, E. Deelman, E. Pairo-Castineira, I. M. Overton, and M. Atkinson, “Using Simple PID Controllers to Prevent and Mitigate Faults in Scientific Workflows,” in 11th Workflows in Support of Large-Scale Science, 2016, pp. 15-24.
    [Bibtex]
    @inproceedings{ferreiradasilva-works-2016,
    author = {Ferreira da Silva, Rafael and Filgueira, Rosa and Deelman, Ewa and Pairo-Castineira, Erola and Overton, Ian Michael and Atkinson, Malcolm},
    title = {Using Simple PID Controllers to Prevent and Mitigate Faults in Scientific Workflows},
    year = {2016},
    booktitle = {11th Workflows in Support of Large-Scale Science},
    series = {WORKS'16},
    pages = {15--24}
    }

 

687 views

Continue Reading

Task Resource Consumption Prediction for Scientific Applications and Workflows


Presentation held at the Algorithms and Scheduling Techniques to Manage Resilience and Power Consumption in Distributed Systems 2015
Dagstuhl, Germany

Abstract – Estimates of task runtime, disk space usage, and memory consumption, are commonly used by scheduling and resource provisioning algorithms to support efficient and reliable scientific application executions. Such algorithms often assume that accurate estimates are available, but such estimates are difficult to generate in practice. In this work, we first profile real scientific applications and workflows, collecting fine-grained information such as process I/O, runtime, memory usage, and CPU utilization. We then propose a method to automatically characterize task requirements based on these profiles. Our method estimates task runtime, disk space, and peak memory consumption. It looks for correlations between the parameters of a dataset, and if no correlation is found, the dataset is divided into smaller subsets using the statistical recursive partitioning method and conditional inference trees to identify patterns that characterize particular behaviors of the workload. We then propose an estimation process to predict task characteristics of scientific applications based on the collected data. For scientific workflows, we propose an online estimation process based on the MAPE-K loop, where task executions are monitored and estimates are updated as more information becomes available. Experimental results show that our online estimation process results in much more accurate predictions than an offline approach, where all task requirements are estimated prior to workflow execution.

 

Related Publications

  • [PDF] [DOI] R. Ferreira da Silva, G. Juve, M. Rynge, E. Deelman, and M. Livny, “Online Task Resource Consumption Prediction for Scientific Workflows,” Parallel Processing Letters, vol. 25, iss. 3, 2015.
    [Bibtex]
    @article{ferreiradasilva-ppl-2015,
    title = {Online Task Resource Consumption Prediction for Scientific Workflows},
    author = {Ferreira da Silva, Rafael and Juve, Gideon and Rynge, Mats and Deelman, Ewa and Livny, Miron},
    journal = {Parallel Processing Letters},
    volume = {25},
    number = {3},
    pages = {},
    year = {2015},
    doi = {10.1142/S0129626415410030}
    }
  • [PDF] [DOI] R. Ferreira da Silva, M. Rynge, G. Juve, I. Sfiligoi, E. Deelman, J. Letts, F. Würthwein, and M. Livny, “Characterizing a High Throughput Computing Workload: The Compact Muon Solenoid (CMS) Experiment at LHC,” Procedia Computer Science, vol. 51, pp. 39-48, 2015.
    [Bibtex]
    @article{ferreiradasilva-iccs-2015,
    title = {Characterizing a High Throughput Computing Workload: The Compact Muon Solenoid ({CMS}) Experiment at {LHC}},
    author = {Ferreira da Silva, Rafael and Rynge, Mats and Juve, Gideon and Sfiligoi, Igor and Deelman, Ewa and Letts, James and W\"urthwein, Frank and Livny, Miron},
    journal = {Procedia Computer Science},
    year = {2015},
    volume = {51},
    pages = {39--48},
    note = {International Conference On Computational Science, \{ICCS\} 2015 Computational Science at the Gates of Nature},
    doi = {10.1016/j.procs.2015.05.190}
    }
  • [PDF] [DOI] R. Ferreira da Silva, G. Juve, E. Deelman, T. Glatard, F. Desprez, D. Thain, B. Tovar, and M. Livny, “Toward fine-grained online task characteristics estimation in scientific workflows,” in 8th Workshop on Workflows in Support of Large-Scale Science, 2013, pp. 58-67.
    [Bibtex]
    @inproceedings{ferreiradasilva-works-2013,
    author = {Ferreira da Silva, Rafael and Juve, Gideon and Deelman, Ewa and Glatard, Tristan and Desprez, Fr{\'e}d{\'e}ric and Thain, Douglas and Tovar, Benjamin and Livny, Miron},
    title = {Toward fine-grained online task characteristics estimation in scientific workflows},
    booktitle = {8th Workshop on Workflows in Support of Large-Scale Science},
    series = {WORKS '13},
    year = {2013},
    pages = {58--67},
    doi = {10.1145/2534248.2534254},
    }

 

542 views

Continue Reading