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Scheduling Data-Intensive Workloads in Large-Scale Distributed Systems: Trends and Challenges

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Modeling and Simulation in HPC and Cloud Systems

Part of the book series: Studies in Big Data ((SBD,volume 36))

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Abstract

With the explosive growth of big data, workloads tend to get more complex and computationally demanding. Such applications are processed on distributed interconnected resources that are becoming larger in scale and computational capacity. Data-intensive applications may have different degrees of parallelism and must effectively exploit data locality. Furthermore, they may impose several Quality of Service requirements, such as time constraints and resilience against failures, as well as other objectives, like energy efficiency. These features of the workloads, as well as the inherent characteristics of the computing resources required to process them, present major challenges that require the employment of effective scheduling techniques. In this chapter, a classification of data-intensive workloads is proposed and an overview of the most commonly used approaches for their scheduling in large-scale distributed systems is given. We present novel strategies that have been proposed in the literature and shed light on open challenges and future directions.

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Acknowledgements

The second author of this chapter, Helen D. Karatza, has been invited as a trainer to the cHiPSet Training School 2016 “New Trends in Modeling and Simulation in HPC Systems”, held in Bucharest, Romania, 21–23 September 2016, and has been supported by the IC1406 Horizon 2020 grant.

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  1. Department of Informatics, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    Georgios L. Stavrinides & Helen D. Karatza

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  1. Georgios L. Stavrinides
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Correspondence to Georgios L. Stavrinides .

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  1. Institute of Computer Science, Cracow University of Technology, Cracow, Poland

    Joanna Kołodziej

  2. Department of Computer Science, University Politehnica of Bucharest, Bucharest, Romania

    Florin Pop

  3. Department of Computer Science, University Politehnica of Bucharest, Bucharest, Romania

    Ciprian Dobre

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Stavrinides, G.L., Karatza, H.D. (2018). Scheduling Data-Intensive Workloads in Large-Scale Distributed Systems: Trends and Challenges. In: Kołodziej, J., Pop, F., Dobre, C. (eds) Modeling and Simulation in HPC and Cloud Systems. Studies in Big Data, vol 36. Springer, Cham. https://doi.org/10.1007/978-3-319-73767-6_2

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