Abstract
Scheduling in the cloud-edge continuum is a challenging problem. In fact, scheduling has to cope with the peculiarities of these complex ecosystems and satisfy at the same time the desired service levels. In this paper, we investigate the benefits of the cloud-edge continuum for deploying workflows with different characteristics, e.g., computation or communication-intensive. In detail, we formulate a multi-objective optimization problem solved using a Genetic Algorithm. This problem is aimed at identifying the scheduling plans that minimize two conflicting objectives, namely, the expected workflow execution time and monetary cost associated with the cloud and edge resources to be provisioned. Our experiments have shown that the plans that exploit both cloud and edge resources represent a good tradeoff between the two objectives. In addition, the workflow characteristics strongly influence these plans. Similarly, the uncertainties that might affect the infrastructure performance are responsible of significant changes in the corresponding Pareto fronts.
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Acknowledgments
This work was partly supported by the Italian Ministry of University and Research (MUR) under the PRIN 2022 grant “Methodologies for the Parallelization, Performance Evaluation and Scheduling of Applications for the Cloud-Edge Continuum” (Master CUP: B53D23013090006, CUP: J53D23007110008, CUP: F53D23004300006) and by the European Union - Next Generation EU.
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Zanussi, L., Tessera, D., Massari, L., Calzarossa, M.C. (2024). Workflow Scheduling in the Cloud-Edge Continuum. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 203. Springer, Cham. https://doi.org/10.1007/978-3-031-57931-8_18
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DOI: https://doi.org/10.1007/978-3-031-57931-8_18
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