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Lightweight Automatic Resource Scaling for Multi-tier Web Applications
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/cpe.2864openalex-snapshot
Applying reinforcement learning towards automating resource allocation and application scalability in the cloud
inferredcites →https://doi.org/10.1007/s10586-007-0035-6openalex-snapshot
On the use of hybrid reinforcement learning for autonomic resource allocation
inferredcites →https://doi.org/10.1109/cloud.2012.12openalex-snapshot
SmartScale: Automatic Application Scaling in Enterprise Clouds
inferredcites →https://doi.org/10.1109/cloud.2013.142openalex-snapshot
VScaler: Autonomic Virtual Machine Scaling
inferredcites →https://doi.org/10.1109/inm.2009.5188871openalex-snapshot
Memory overbooking and dynamic control of Xen virtual machines in consolidated environments
inferredcites →https://doi.org/10.1109/iwqos.2011.5931341openalex-snapshot
DynaQoS: Model-free self-tuning fuzzy control of virtualized resources for QoS provisioning
inferredcites →https://doi.org/10.1109/noms.2012.6212070openalex-snapshot
Integrated and autonomic cloud resource scaling
inferredcites →https://doi.org/10.1145/1272996.1273026openalex-snapshot
Adaptive control of virtualized resources in utility computing environments
inferredcites →https://doi.org/10.1145/1342171.1342172openalex-snapshot
Agile dynamic provisioning of multi-tier Internet applications
inferredcites →https://doi.org/10.1145/1519065.1519068openalex-snapshot
Automated control of multiple virtualized resources
inferredcites →https://doi.org/10.5555/1996039.1996074openalex-snapshot
DynaQoS: model-free self-tuning fuzzy control of virtualized resources for QoS provisioning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/nem.2176openalex-snapshot
Deep Q‐network‐based auto scaling for service in a multi‐access edge computing environment
inferred← citeshttps://doi.org/10.1007/s10586-020-03148-5openalex-snapshot
RHAS: robust hybrid auto-scaling for web applications in cloud computing
inferred← citeshttps://doi.org/10.1016/j.future.2020.02.043openalex-snapshot
DYVERSE: DYnamic VERtical Scaling in multi-tenant Edge environments
inferred← citeshttps://doi.org/10.1016/j.jss.2019.02.031openalex-snapshot
Unsupervised learning approach for web application auto-decomposition into microservices
inferred← citeshttps://doi.org/10.1109/cloud49709.2020.00076openalex-snapshot
Cloud Resource Provisioning and Bottleneck Eliminating for Meshed Web Systems
inferred← citeshttps://doi.org/10.1109/icaci.2018.8377525openalex-snapshot
Auto-scaling microservices on IaaS under SLA with cost-effective framework
inferred← citeshttps://doi.org/10.1109/iccmc51019.2021.9418275openalex-snapshot
Retracted: Performance Evaluation of Multi-Tier Application by using the Comprehensive Workload Modelling in the Cloud
inferred← citeshttps://doi.org/10.1109/ispdc.2016.26openalex-snapshot
HybridScaler: Handling Bursting Workload for Multi-tier Web Applications in Cloud
inferred← citeshttps://doi.org/10.1109/jsyst.2015.2424998openalex-snapshot
Unsupervised Learning of Dynamic Resource Provisioning Policies for Cloud-Hosted Multitier Web Applications
inferred← citeshttps://doi.org/10.1109/netsoft51509.2021.9492728openalex-snapshot
Machine Learning-Based Auto-Scaler for Video Conferencing Systems
inferred← citeshttps://doi.org/10.1109/smartcity.2015.212openalex-snapshot
Efficient Hybriding Auto-scaling for OpenStack Platforms
inferred← citeshttps://doi.org/10.1109/tcc.2019.2944364openalex-snapshot
Predictive Auto-Scaling of Multi-Tier Applications Using Performance Varying Cloud Resources
inferred← citeshttps://doi.org/10.1109/tpds.2022.3170834openalex-snapshot
State Space Model and Queuing Network Based Cloud Resource Provisioning for Meshed Web Systems
inferred← citeshttps://doi.org/10.1109/tsc.2025.3596887openalex-snapshot
Deep Learning and Feedback Control Based Container Auto-Scaling for Cloud Native Micro-Services
inferred← citeshttps://doi.org/10.23919/cnsm50824.2020.9269107openalex-snapshot
Deep Q-Networks based Auto-scaling for Service Function Chaining
inferred← citeshttps://doi.org/10.48550/arxiv.1709.06686openalex-snapshot
Sieve: Actionable Insights from Monitored Metrics in Microservices
inferred← citeshttps://doi.org/10.48550/arxiv.2010.05187openalex-snapshot
On the Road from Edge Computing to the Edge Mesh
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