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Open AccessArticle

On Implementing Autonomic Systems with a Serverless Computing Approach: The Case of Self-Partitioning Cloud Caches

1
Escuela Superior Politecnica del Litoral, Guayaquil EC090112, Ecuador
2
College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA 01003, USA
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in the Workshop on Self-Aware Computing (SeAC), held in conjunction with Foundations and Applications of Self* Systems (FAS* 2019).
Computers 2020, 9(1), 14; https://doi.org/10.3390/computers9010014
Received: 12 January 2020 / Revised: 19 February 2020 / Accepted: 23 February 2020 / Published: 26 February 2020
(This article belongs to the Special Issue Applications in Self-Aware Computing Systems and their Evaluation)
The research community has made significant advances towards realizing self-tuning cloud caches; notwithstanding, existing products still require manual expert tuning to maximize performance. Cloud (software) caches are built to swiftly serve requests; thus, avoiding costly functionality additions not directly related to the request-serving control path is critical. We show that serverless computing cloud services can be leveraged to solve the complex optimization problems that arise during self-tuning loops and can be used to optimize cloud caches for free. To illustrate that our approach is feasible and useful, we implement SPREDS (Self-Partitioning REDiS), a modified version of Redis that optimizes memory management in the multi-instance Redis scenario. A cost analysis shows that the serverless computing approach can lead to significant cost savings: The cost of running the controller as a serverless microservice is 0.85% of the cost of the always-on alternative. Through this case study, we make a strong case for implementing the controller of autonomic systems using a serverless computing approach. View Full-Text
Keywords: self-tuning; cloud computing; serverless computing; autonomic controller self-tuning; cloud computing; serverless computing; autonomic controller
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Boza, E.F.; Andrade, X.; Cedeno, J.; Murillo, J.; Aragon, H.; Abad, C.L.; Abad, A.G. On Implementing Autonomic Systems with a Serverless Computing Approach: The Case of Self-Partitioning Cloud Caches. Computers 2020, 9, 14.

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