Abstract
When solving compute-intensive tasks, CPU/GPU hardware resources and specialized Grid, Custer, Cloud infrastructure are commonly used to achieve high performance. However, this requires a high initial capital expense and ongoing maintenance costs. In contrast, ARM-based mobile devices regularly see improvement in their capacity, stability, and processing power daily while becoming ever more ubiquitous and requiring no massive capital or operating expenditures thanks to their reduced size and energy efficiency. Given this shifting computer paradigm, it is conceivable that a cost- and power-efficient solution for our world’s HPC processing tasks would include ARM-based mobile devices, while they are idle during recharging periods. We proposed, developed, deployed and evaluated a distributed, collaborative, elastic and low-cost platform to solve HPC tasks recycling ARM mobile resources based on Cloud, microservices and containers, efficiently orchestrated via Kubernetes. To validate the system scalability, flexibility, and performance a lot of concurrent video transcoding scenarios were run. The results showed the system allows for improvements in terms of scalability, flexibility, stability, efficiency, and cost for HPC workloads.
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Petrocelli, D., De Giusti, A., Naiouf, M. (2022). Collaborative, Distributed, Scalable and Low-Cost Platform Based on Microservices, Containers, Mobile Devices and Cloud Services to Solve Compute-Intensive Tasks. In: Chaves, R., et al. Euro-Par 2021: Parallel Processing Workshops. Euro-Par 2021. Lecture Notes in Computer Science, vol 13098. Springer, Cham. https://doi.org/10.1007/978-3-031-06156-1_47
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DOI: https://doi.org/10.1007/978-3-031-06156-1_47
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