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AI-based Resource Allocation: Reinforcement Learning for Adaptive Auto-scaling in Serverless Environments
Serverless computing has emerged as a compelling new paradigm of cloud c...
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Cloud-scale VM Deflation for Running Interactive Applications On Transient Servers
Transient computing has become popular in public cloud environments for ...
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Scaling Scientometrics: Dimensions on Google BigQuery as an infrastructure for large-scale analysis
Cloud computing has the capacity to transform many parts of the research...
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Towards Self-Improving Hybrid Elasticity Control of Cloud-based Software Systems
Elasticity is a form of self-adaptivity in cloud-based software systems ...
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Dynamic Cloud Network Control under Reconfiguration Delay and Cost
Network virtualization and programmability allow operators to deploy a w...
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Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider
Function as a Service (FaaS) has been gaining popularity as a way to dep...
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ELASTIC: Improving CNNs with Instance Specific Scaling Policies
Scale variation has been a challenge from traditional to modern approach...
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A simple and effective predictive resource scaling heuristic for large-scale cloud applications
We propose a simple yet effective policy for the predictive auto-scaling of horizontally scalable applications running in cloud environments, where compute resources can only be added with a delay, and where the deployment throughput is limited. Our policy uses a probabilistic forecast of the workload to make scaling decisions dependent on the risk aversion of the application owner. We show in our experiments using real-world and synthetic data that this policy compares favorably to mathematically more sophisticated approaches as well as to simple benchmark policies.
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