RT Journal Article
T1 Leveraging knowledge-as-a-service (KaaS) for QoS-aware resource management in multi-user video transcoding
A1 Costero Valero, Luis María
A1 Igual Peña, Francisco Daniel
A1 Olcoz Herrero, Katzalin
A1 Tirado Fernández, José Francisco
AB 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.
PB Springer
SN 0920-8542
YR 2020
FD 2020-02-25
LK https://hdl.handle.net/20.500.14352/96485
UL https://hdl.handle.net/20.500.14352/96485
LA eng
DS Docta Complutense
RD 8 abr 2025