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Leveraging knowledge-as-a-service (KaaS) for QoS-aware resource management in multi-user video transcoding

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Abstract

The coexistence of parallel applications in shared computing nodes, each one featuring different Quality of Service (QoS) requirements, carries out new challenges to improve resource occupation while keeping acceptable rates in terms of QoS. As more application-specific and system-wide metrics are included as QoS dimensions, or under situations in which resource-usage limits are strict, building and serving the most appropriate set of actions (application control knobs and system resource assignment) to concurrent applications in an automatic and optimal fashion become mandatory. In this paper, we propose strategies to build and serve this type of knowledge to concurrent applications by leveraging Reinforcement Learning techniques. Taking multi-user video transcoding as a driving example, our experimental results reveal an excellent adaptation of resource and knob management to heterogeneous QoS requests, and increases in the amount of concurrently served users up to 1.24 × compared with alternative approaches considering homogeneous QoS requests.

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Funding

This work has been partially supported by the EU (FEDER), the Spanish MINECO and the CM under Grants S2018/TCS-4423, TIN 2015-65277-R and RTI2018-093684-B-I00 and the Spanish MECD under Grant FPU15/02050.

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Correspondence to Luis Costero.

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Costero, L., Igual, F.D., Olcoz, K. et al. Leveraging knowledge-as-a-service (KaaS) for QoS-aware resource management in multi-user video transcoding. J Supercomput 76, 9388–9403 (2020). https://doi.org/10.1007/s11227-019-03117-9

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  • DOI: https://doi.org/10.1007/s11227-019-03117-9

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