Auto-scaling Resources for Cloud Applications using Reinforcement learning

John, Indu; Sreekantan, Aiswarya; Bhatnagar, Shalabh

doi:10.1109/ghci47972.2019.9071835

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…There are a large variety of application specific proactive approaches, e.g. in auto-scaling for cloud computing [7], [1], [9], that employ Markov decision processes and reinforcement learning for enabling proactive system behavior.…”

Section: Related Workmentioning

confidence: 99%

Applying Reconfiguration Cost and Control Pattern Modeling to Self-Adaptive Systems

Matthé

2022

2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

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Self-adaptive systems have become a popular research topic to overcome challenges of developing highly complex, interconnected, and heterogeneous systems and networks. These systems aim to autonomously adapt to a changing environment by adapting system behavior or composition to improve performance. Many selfadaptive systems are designed in a purely reactive way and without considering costs that may be incurred by performing adaptation. This thesis therefore aims to develop an approach for proactive self-adaptive systems and evaluate the impact of reconfiguration cost and proactive adaptation in an edge computing system. Additionally the autonomous adaptation of different control patterns for centralized or decentralized control will be explored and evaluated. This thesis proposes to extend feature models, as used in dynamic software product lines, with modeling for reconfiguration cost and of uncertainty in the system's environment.

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Section: Related Workmentioning

confidence: 99%