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A Quantitative Approach to Coordinated Scaling of Resources in Complex Cloud Computing Workflows

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Computer Performance Engineering and Stochastic Modelling (EPEW 2023, ASMTA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14231))

Abstract

Resource scaling is widely employed in cloud computing to adapt system operation to internal (i.e., application) and external (i.e., environment) changes. We present a quantitative approach for coordinated vertical scaling of resources in cloud computing workflows, aimed at satisfying an agreed Service Level Objective (SLO) by improving the workflow end-to-end (e2e) response time distribution. Workflows consist of IaaS services running on dedicated clusters, statically reserved before execution. Services are composed through sequence, choice/merge, and balanced split/join blocks, and have generally distributed (i.e., non-Markovian) durations possibly over bounded supports, facilitating fitting of analytical distributions from observed data. Resource allocation is performed through an efficient heuristics guided by the mean makespans of sub-workflows. The heuristics performs a top-down visit of the hierarchy of services, and it exploits an efficient compositional method to derive the response time distribution and the mean makespan of each sub-workflow. Experimental results on a workflow with high concurrency degree appear promising for feasibility and effectiveness of the approach.

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Acknowledgments

This work was partially supported by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on “Telecommunications of the Future” (PE00000001 - program “RESTART”).

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Authors and Affiliations

  1. Department of Information Engineering, University of Florence, Florence, Italy

    Laura Carnevali, Benedetta Picano, Riccardo Reali, Leonardo Scommegna & Enrico Vicario

  2. Department of Computer Science, University of Southern California, Los Angeles, USA

    Marco Paolieri

Authors
  1. Laura Carnevali
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  2. Marco Paolieri
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  3. Benedetta Picano
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  4. Riccardo Reali
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  5. Leonardo Scommegna
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  6. Enrico Vicario
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Corresponding author

Correspondence to Riccardo Reali .

Editor information

Editors and Affiliations

  1. Università degli Studi della Campania, Caserta, Italy

    Mauro Iacono

  2. Università degli Studi di Messina, Messina, Italy

    Marco Scarpa

  3. Università Cattolica del Sacro Cuore, Brescia, Italy

    Enrico Barbierato

  4. Università degli Studi di Messina, Messina, Italy

    Salvatore Serrano

  5. Università del Piemonte Orientale, Alessandria, Italy

    Davide Cerotti

  6. Universita degli Studi di Messina, Messina, Italy

    Francesco Longo

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Carnevali, L., Paolieri, M., Picano, B., Reali, R., Scommegna, L., Vicario, E. (2023). A Quantitative Approach to Coordinated Scaling of Resources in Complex Cloud Computing Workflows. In: Iacono, M., Scarpa, M., Barbierato, E., Serrano, S., Cerotti, D., Longo, F. (eds) Computer Performance Engineering and Stochastic Modelling. EPEW ASMTA 2023 2023. Lecture Notes in Computer Science, vol 14231. Springer, Cham. https://doi.org/10.1007/978-3-031-43185-2_21

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  • DOI: https://doi.org/10.1007/978-3-031-43185-2_21

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