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Predicting CPU usage for proactive autoscaling
Proceedings of the 1st Workshop on Machine Learning and Systems, 2021Private and public clouds require users to specify requests for resources such as CPU and memory (RAM) to be provisioned for their applications. The values of these requests do not necessarily relate to the application's run-time requirements, but only help the cloud infrastructure resource manager to map requested resources to physical resources.
Thomas Wang, Simone Ferlin, Marco Chiesa
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IaaS Reactive Autoscaling Performance Challenges
2018 IEEE 11th International Conference on Cloud Computing (CLOUD), 2018The main feature of a cloud application is its scalability. Major IaaS cloud services providers (CSP) employ autoscaling on the level of virtual machines (VM). Other virtualization solutions (e.g. containers, pods) can also scale. An application scales in response to change in observed metrics, e.g. in CPU utilization.
Vladimir Podolskiy +2 more
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Autoscaling tiered cloud storage in Anna
The VLDB Journal, 2019In this paper, we describe how we extended a distributed key-value store called Anna into an autoscaling, multi-tier service for the cloud. In its extended form, Anna is designed to overcome the narrow cost-performance limitations typical of current cloud storage systems.
Chenggang Wu 0001 +2 more
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Towards Autoscaling with Guarantees on Kubernetes Clusters
2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), 2021Autoscaling is used by cloud providers, microser-vices, and edge computing applications to respond to dynamic load fluctuations. A critical direction of research has focused on providing guarantees under uncertainty that the auto scaling system will work as intended-both at design-time and more importantly, at runtime.
Stephen Burroughs +6 more
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Autoscaling Effects in Speed Scaling Systems
2016 IEEE 24th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), 2016In this paper, we study the dynamics of coupled speed scaling systems, in which service rate is a function of system occupancy. We focus on both Processor Sharing (PS) and Shortest Remaining Processing Time (SRPT) as scheduling disciplines, and study their speed scaling dynamics under heavy load.
B. Maryam Elahi, Carey L. Williamson
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ENORMOuS: An environment-based autoscaling system
2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020This paper describes an auto scale approach driven by the use of environmental metrics to scale applications. On high granularity application environments, every application impacts directly in other applications. The main goal of this paper is to predict scale demands before compromising the performance by overload.
Renato Gil-Azevedo +1 more
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A Survey of Autoscaling in Kubernetes
2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN), 2022Minh-Ngoc Tran +2 more
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Quantifying Cloud Elasticity with Container-Based Autoscaling
2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), 2017Abstract Containers have been a pervasive approach to help rapidly develop, test and update the Internet of Things applications (IoT). The autoscaling of containers can adaptively allocate computing resources for various data volumes over time. Therefore, elasticity, a critical feature of a cloud platform, is significant to measure the performance of
Fan Zhang 0003 +4 more
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Hitchhiker's Guide for Explainability in Autoscaling
Companion of the 2023 ACM/SPEC International Conference on Performance Engineering, 2023Floriment Klinaku +3 more
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Autoscaling of Containerized HPC Clusters in the Cloud
2022 IEEE/ACM International Workshop on Interoperability of Supercomputing and Cloud Technologies (SuperCompCloud), 2022Nicolas Grenèche, Christophe Cérin
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