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A cooperative multi‐agent offline learning algorithm to scheduling IoT workflows in the cloud computing environment

https://doi.org/10.1002/cpe.7148
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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.4044openalex-snapshot
Optimal scheduling workflows in cloud computing environment using Pareto‐based Grey Wolf Optimizer
inferredcites →https://doi.org/10.1002/cpe.4949openalex-snapshot
Multi‐objective approach of energy efficient workflow scheduling in cloud environments
inferredcites →https://doi.org/10.1002/cpe.5193openalex-snapshot
Multi‐criteria workflow scheduling on clouds under deadline and budget constraints
inferredcites →https://doi.org/10.1007/978-981-10-0356-1_31openalex-snapshot
Multiple DAGs Workflow Scheduling Algorithm Based on Reinforcement Learning in Cloud Computing
inferredcites →https://doi.org/10.1016/0377-2217(80)90117-4openalex-snapshot
Multiple objective decision making - methods and applications: A state-of-the art survey
inferredcites →https://doi.org/10.1016/j.asoc.2014.01.036openalex-snapshot
CLPS-GA: A case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling
inferredcites →https://doi.org/10.1016/j.asoc.2018.02.011openalex-snapshot
A novel multi-objective bacteria foraging optimization algorithm (MOBFOA) for multi-objective scheduling
inferredcites →https://doi.org/10.1016/j.comnet.2020.107340openalex-snapshot
A cloud resource management framework for multiple online scientific workflows using cooperative reinforcement learning agents
inferredcites →https://doi.org/10.1016/j.ejor.2020.11.016openalex-snapshot
Performance indicators in multiobjective optimization
inferredcites →https://doi.org/10.1016/j.fcij.2017.02.001openalex-snapshot
A survey of IoT cloud platforms
inferredcites →https://doi.org/10.1016/j.future.2008.12.001openalex-snapshot
Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility
inferredcites →https://doi.org/10.1016/j.future.2013.07.005openalex-snapshot
Multi-objective energy-efficient workflow scheduling using list-based heuristics
inferredcites →https://doi.org/10.1016/j.future.2015.02.001openalex-snapshot
Energy efficient scheduling of virtual machines in cloud with deadline constraint
inferredcites →https://doi.org/10.1016/j.future.2015.08.009openalex-snapshot
Static scheduling of multiple workflows with soft deadlines in non-dedicated heterogeneous environments
inferredcites →https://doi.org/10.1016/j.future.2016.06.029openalex-snapshot
Dynamic energy-aware scheduling for parallel task-based application in cloud computing
inferredcites →https://doi.org/10.1016/j.ins.2015.03.027openalex-snapshot
Re-Stream: Real-time and energy-efficient resource scheduling in big data stream computing environments
inferredcites →https://doi.org/10.1016/j.jpdc.2011.04.007openalex-snapshot
A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems
inferredcites →https://doi.org/10.1016/j.jss.2015.11.023openalex-snapshot
Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: A review, classifications, and open issues
inferredcites →https://doi.org/10.1016/j.ress.2017.12.019openalex-snapshot
Multi-objective optimization of reliability–redundancy allocation problem with cold-standby strategy using NSGA-II
inferredcites →https://doi.org/10.1023/a:1007465907571openalex-snapshot
Online Learning versus Offline Learning
inferredcites →https://doi.org/10.1109/4235.996017openalex-snapshot
A fast and elitist multiobjective genetic algorithm: NSGA-II
inferredcites →https://doi.org/10.1109/access.2019.2902846openalex-snapshot
Multi-Objective Workflow Scheduling With Deep-Q-Network-Based Multi-Agent Reinforcement Learning
inferredcites →https://doi.org/10.1109/cec.2009.4982949openalex-snapshot
The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances
inferredcites →https://doi.org/10.1109/icdcs.2017.123openalex-snapshot
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning
inferredcites →https://doi.org/10.1109/icsec.2013.6694749openalex-snapshot
Efficient energy aware task scheduling for parallel workflow tasks on hybrids cloud environment
inferredcites →https://doi.org/10.1109/icwr49608.2020.9122306openalex-snapshot
A List-based Heuristic Algorithm for Static Task Scheduling in Heterogeneous Distributed Computing Systems
inferredcites →https://doi.org/10.1109/jas.2021.1003982openalex-snapshot
An Effective Cloud Workflow Scheduling Approach Combining PSO and Idle Time Slot-Aware Rules
inferredcites →https://doi.org/10.1109/jsee.2016.00047openalex-snapshot
Resource pre-allocation algorithms for low-energy task scheduling of cloud computing
inferredcites →https://doi.org/10.1109/tase.2017.2693688openalex-snapshot
Dynamic Cloud Task Scheduling Based on a Two-Stage Strategy
inferredcites →https://doi.org/10.1109/tc.2010.216openalex-snapshot
EAD and PEBD: Two Energy-Aware Duplication Scheduling Algorithms for Parallel Tasks on Homogeneous Clusters
inferredcites →https://doi.org/10.1109/tcad.2019.2930575openalex-snapshot
H₂O-Cloud: A Resource and Quality of Service-Aware Task Scheduling Framework for Warehouse-Scale Data Centers
inferredcites →https://doi.org/10.1109/tcc.2014.2303077openalex-snapshot
Multi-Objective Game Theoretic Schedulingof Bag-of-Tasks Workflows on Hybrid Clouds
inferredcites →https://doi.org/10.1109/tcyb.2014.2363878openalex-snapshot
An Efficient Chemical Reaction Optimization Algorithm for Multiobjective Optimization
inferredcites →https://doi.org/10.1109/tevc.2007.892759openalex-snapshot
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
inferredcites →https://doi.org/10.1109/tnn.2004.842673openalex-snapshot
Reinforcement Learning: An Introduction
inferredcites →https://doi.org/10.1109/tpds.2013.57openalex-snapshot
List Scheduling Algorithm for Heterogeneous Systems by an Optimistic Cost Table
inferredcites →https://doi.org/10.1134/s0005117919020085openalex-snapshot
Heuristic Algorithms to Maximize Revenue and the Number of Jobs Processed on Parallel Machines
inferredcites →https://doi.org/10.14257/ijgdc.2017.10.8.03openalex-snapshot
Dynamic Task Scheduling in Cloud Computing Based on the Availability Level of Resources
inferredcites →https://doi.org/10.22034/ijiems.2020.118105openalex-snapshot
A memetic algorithm for multistage hybrid flow shop scheduling problem with multiprocessor tasks to minimize makespan
inferredcites →https://doi.org/10.22070/jqepo.2021.14209.1182openalex-snapshot
A novel algorithm for solving the parallel machine scheduling problem to maximize benefit and the number of jobs processed
inferredcites →https://doi.org/10.5220/0006358607450751openalex-snapshot
Workflow for the Internet of Things

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-3-031-95133-6_22openalex-snapshot
SARS: A Resource Selection Algorithm for Autonomous Driving Tasks in Heterogeneous Mobile Edge Computing
inferred← citeshttps://doi.org/10.1016/j.engappai.2023.106454openalex-snapshot
Problem-specific knowledge MOEA/D for energy-efficient scheduling of distributed permutation flow shop in heterogeneous factories
inferred← citeshttps://doi.org/10.1016/j.future.2024.03.043openalex-snapshot
Efficient decentralized optimization for edge-enabled smart manufacturing: A federated learning-based framework
inferred← citeshttps://doi.org/10.1016/j.future.2024.07.054openalex-snapshot
A cross-modal high-resolution image generation approach based on cloud-terminal collaboration for low-altitude intelligent network
inferred← citeshttps://doi.org/10.1016/j.future.2024.107588openalex-snapshot
Task Offloading Optimization for Multi-objective Based on Cloud-Edge-End Collaboration in Maritime Networks
inferred← citeshttps://doi.org/10.1016/j.jpdc.2025.105069openalex-snapshot
A knowledge-driven approach to multi-objective IoT task graph scheduling in fog-cloud computing
inferred← citeshttps://doi.org/10.1016/j.knosys.2023.110309openalex-snapshot
Toward automated algorithm configuration for distributed hybrid flow shop scheduling with multiprocessor tasks
inferred← citeshttps://doi.org/10.1109/access.2025.3577044openalex-snapshot
Multi-Objective Scheduling for Green Flexible Assembly Job-Shop System via Multi-Agent Deep Reinforcement Learning With Game Theory
inferred← citeshttps://doi.org/10.1109/tpds.2023.3288702openalex-snapshot
Energy-Minimized Scheduling of Intermittent Real-Time Tasks in a CPU-GPU Cloud Computing Platform
inferred← citeshttps://doi.org/10.1109/tsc.2026.3707320openalex-snapshot
Integrating Multi-Agent Reinforcement Learning and XGBoost for Efficient Large-Scale Dynamic Workflow Scheduling in Cloud Environments
inferred← citeshttps://doi.org/10.4067/s0718-33052024000100214openalex-snapshot
Learning in multi-agent systems to solve scheduling problems: a systematic literature review
inferred← citeshttps://doi.org/10.52547/ijmt.10.18.25openalex-snapshot
A new multi-objective model for berth allocation and quay crane assignment problem with speed optimization and air emission considerations (A case study of Rajaee Port in Iran)
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