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A novel adaptive checkpointing method based on information obtained from workflow structure

Kail E., Kacsuk P., Kozlovszky M.

Computer Science

2016

Vol. 17 (3)

387--406

Scientific workflows are data- and compute-intensive; thus, they may run for days or even weeks on parallel and distributed infrastructures such as grids, supercomputers, and clouds. In these high-performance computing infrastruc- tures, the number of failures that can arise during scientific-workflow enact- ment can be high, so the use of fault-tolerance techniques is unavoidable. The most-frequently used fault-tolerance technique is taking checkpoints from time to time; when failure is detected, the last consistent state is restored. One of the most-critical factors that has great impact on the effectiveness of the checkpointing method is the checkpointing interval. In this work, we propose a Static (Wsb) and an Adaptive (AWsb) Workflow Structure Based checkpoint- ing algorithm. Our results showed that, compared to the optimal checkpointing strategy, the static algorithm may decrease the checkpointing overhead by as much as 33% without affecting the total processing time of workflow execution. The adaptive algorithm may further decrease this overhead while keeping the overall processing time at its necessary minimum.

Data analysis and management engine for signal processingData analysis and management engine for signal processing

Mąsior M., Kacprzak S.

Studia Informatica

2014

Vol. 35, nr 2

19--28

This paper presents framework for managing analysis of scientific data. The framework was build on sole purpose of research on signal processing and speech technology but can be successfully adapted to other scientific problems.

Artykuł przedstawia środowisko zarządzania analizami danych naukowych. System został stworzony na potrzeby badań nad przetwarzaniem sygnałów i technologią mowy, ale może być z powodzeniem zastosowany w innych problemach naukowych.

Approaches to Distributed Execution of Scientific Workflows in Kepler

Płóciennik M., Żok T., Altintas I., Wang J., Crawl D., Abramson D., Imbeaux F., Guillerminet B., Lopez-Caniego M., Campos Plasencia I., Pych W., Ciecieląg P., Palak B., Owsiak M., Frauel Y.

Fundamenta Informaticae

2013

Vol. 128, nr 3

281--302

The Kepler scientific workflow system enables creation, execution and sharing of workflows across a broad range of scientific and engineering disciplines while also facilitating remote and distributed execution of workflows. In this paper, we present and compare different approaches to distributed execution of workflows using the Kepler environment, including a distributed data-parallel framework using Hadoop and Stratosphere, and Cloud and Grid execution using Serpens, Nimrod/K and Globus actors. We also present real-life applications in computational chemistry, bioinformatics and computational physics to demonstrate the usage of different distributed computing capabilities of Kepler in executable workflows. We further analyze the differences of each approach and provide a guidance for their applications.