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oai:docta.ucm.es:20.500.14352/964852025-05-09T23:52:24Zcom_20.500.14352_14col_20.500.14352_15

00925njm 22002777a 4500 dc Costero Valero, Luis María author Igual Peña, Francisco Daniel author Olcoz Herrero, Katzalin author Tirado Fernández, José Francisco author 2020 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. 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 0920-8542 10.1007/s11227-019-03117-9 https://hdl.handle.net/20.500.14352/96485 https://doi.org/10.1007/s11227-019-03117-9 Leveraging knowledge-as-a-service (KaaS) for QoS-aware resource management in multi-user video transcoding