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

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 i...

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Bibliographic Details
Published in:The Journal of supercomputing Vol. 76; no. 12; pp. 9388 - 9403
Main Authors: Costero, Luis, Igual, Francisco D., Olcoz, Katzalin, Tirado, Francisco
Format: Journal Article
Language:English
Published: New York Springer US 01.12.2020
Springer Nature B.V
Subjects:
Automatic control
Compilers
Computer Science
High Performance Computing in Science and Engineering - CMMSE-2019
Interpreters
Knobs
Processor Architectures
Programming Languages
Quality of service
Resource management
Reinforcement Learning
HEVC video transcoding
Multi-core architectures
Heterogeneous Quality of Service
Resource management
ISSN:0920-8542, 1573-0484
Online Access:Get full text
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Summary: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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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-019-03117-9