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A Constrained Genetic Algorithm for Rebalancing of Services in Cloud Data Centers

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-642-24958-7_30openalex-snapshot
Resource Allocation and Scheduling of Multiple Composite Web Services in Cloud Computing Using Cooperative Coevolution Genetic Algorithm
inferredcites →https://doi.org/10.1109/apscc.2011.66openalex-snapshot
Hybrid Genetic Algorithm for Cloud Computing Applications
inferredcites →https://doi.org/10.1109/csc.2011.6138518openalex-snapshot
Multi-objective optimization model of virtual resources scheduling under cloud computing and it's solution
inferredcites →https://doi.org/10.1109/hpcsim.2011.5999860openalex-snapshot
A pareto-based GA for scheduling HPC applications on distributed cloud infrastructures
inferredcites →https://doi.org/10.1109/iccrd.2011.5764170openalex-snapshot
An approach for cloud resource scheduling based on Parallel Genetic Algorithm
inferredcites →https://doi.org/10.1109/iscc.2011.5984012openalex-snapshot
Scheduling and monitoring of internally structured services in Cloud federations
inferredcites →https://doi.org/10.1109/jsyst.2013.2256731openalex-snapshot
Metaheuristic Scheduling for Cloud: A Survey
inferredcites →https://doi.org/10.1109/paap.2010.65openalex-snapshot
A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment
inferredcites →https://doi.org/10.1109/tcc.2014.2362120openalex-snapshot
Modeling and Placement of Cloud Services with Internal Structure
inferredcites →https://doi.org/10.1109/works.2006.5282347openalex-snapshot
High-level composition of QoS-aware Grid workflows: An approach that considers location affinity
inferredcites →https://doi.org/10.1145/1113361.1113374openalex-snapshot
CoMon
inferredcites →https://doi.org/10.1145/1721654.1721672openalex-snapshot
A view of cloud computing

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-319-47474-8_18openalex-snapshot
Online Workload Forecasting
inferred← citeshttps://doi.org/10.1007/978-3-319-47474-8_20openalex-snapshot
Self-awareness of Cloud Applications
inferred← citeshttps://doi.org/10.1007/s10922-024-09830-yopenalex-snapshot
Offline and Real-Time Policy-based Management for Virtualized Services: Conflict and Redundancy Detection, and Automated Resolution
inferred← citeshttps://doi.org/10.1007/s11227-022-04492-6openalex-snapshot
RAFALE: Rethinking the provisioning of virtuAl network services using a Fast and scAlable machine LEarning approach
inferred← citeshttps://doi.org/10.1007/s11227-023-05462-2openalex-snapshot
Energy optimized container placement for cloud data centers: a meta-heuristic approach
inferred← citeshttps://doi.org/10.1007/s41060-022-00347-8openalex-snapshot
From data to interpretable models: machine learning for soil moisture forecasting
inferred← citeshttps://doi.org/10.1016/j.eswa.2020.113719openalex-snapshot
A whale optimization system for energy-efficient container placement in data centers
inferred← citeshttps://doi.org/10.1109/cec.2019.8790220openalex-snapshot
A Hybrid Genetic Programming Hyper-Heuristic Approach for Online Two-level Resource Allocation in Container-based Clouds
inferred← citeshttps://doi.org/10.1109/dsaa.2018.00041openalex-snapshot
Wetting and Drying of Soil: From Data to Understandable Models for Prediction
inferred← citeshttps://doi.org/10.1109/fskd.2016.7603263openalex-snapshot
An energy-efficient virtual machine placement algorithm in cloud data center
inferred← citeshttps://doi.org/10.1109/icabcd.2018.8465422openalex-snapshot
Enhancement of Task Scheduling Technique of Big Data Cloud Computing
inferred← citeshttps://doi.org/10.17485/ijst/2015/v8i27/79175openalex-snapshot
An Adaptive Genetic Algorithm based Robust QoS Oriented Green Computing Scheme for VM Consolidation in Large Scale Cloud Infrastructures
inferred← citeshttps://doi.org/10.48550/arxiv.1611.00323openalex-snapshot
Self-Awareness of Cloud Applications
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