Abstract
Fog computing uses geographically distributed fog nodes that can supply nearby end devices with low-latency access to cloud-like compute resources. If the load of a fog node exceeds its capacity, some non-latency-critical application components may be offloaded to the cloud. Using commercial cloud offerings for such offloading incurs financial costs. Optimally deciding which application components to keep in the fog node and which ones to offload to the cloud is a difficult combinatorial problem. We introduce an optimization algorithm that (i) guarantees that the deployment always satisfies capacity constraints, (ii) achieves near-optimal cloud usage costs, and (iii) is fast enough to be run online. Experimental results show that our algorithm can optimize the deployment of hundreds of components in a fraction of a second on a commodity computer, while leading to only slightly higher costs than the optimum.
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Notes
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Abbreviations: AM = Additive Manufacturing, iWh = inbound Warehouse, VR/AR = virtual reality/augmented reality, ERP = Enterprise Resource Planning.
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Acknowledgments
Research leading to these results received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements no. 731678 (RestAssured) and 731932 (TransformingTransport).
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Mann, Z.Á., Metzger, A., Prade, J., Seidl, R. (2019). Optimized Application Deployment in the Fog. In: Yangui, S., Bouassida Rodriguez, I., Drira, K., Tari, Z. (eds) Service-Oriented Computing. ICSOC 2019. Lecture Notes in Computer Science(), vol 11895. Springer, Cham. https://doi.org/10.1007/978-3-030-33702-5_22
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DOI: https://doi.org/10.1007/978-3-030-33702-5_22
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