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CREW: Cost and Reliability aware Eagle‐Whale optimiser for service placement in Fog

https://doi.org/10.1002/spe.2896
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What this work says

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

inferredcites →https://doi.org/10.1002/9781118625651openalex-snapshot
Fundamentals of Queueing Theory
inferredcites →https://doi.org/10.1002/dac.3926openalex-snapshot
Elucidating the challenges for the praxis of fog computing: An aspect‐based study
inferredcites →https://doi.org/10.1002/spe.2509openalex-snapshot
iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments
inferredcites →https://doi.org/10.1007/978-3-642-12538-6_9openalex-snapshot
Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic Optimization
inferredcites →https://doi.org/10.1007/s11761-017-0219-8openalex-snapshot
Optimized IoT service placement in the fog
inferredcites →https://doi.org/10.1007/s12243-016-0524-9openalex-snapshot
Service placement for latency reduction in the internet of things
inferredcites →https://doi.org/10.1007/s12652-018-0785-4openalex-snapshot
A methodology for deployment of IoT application in fog
inferredcites →https://doi.org/10.1007/s12652-018-0914-0openalex-snapshot
A lightweight decentralized service placement policy for performance optimization in fog computing
inferredcites →https://doi.org/10.1016/j.advengsoft.2016.01.008openalex-snapshot
The Whale Optimization Algorithm
inferredcites →https://doi.org/10.1016/j.future.2018.07.062openalex-snapshot
QoS-aware cloud service composition using eagle strategy
inferredcites →https://doi.org/10.1016/j.jnca.2016.08.010openalex-snapshot
Reliability and energy efficiency in cloud computing systems: Survey and taxonomy
inferredcites →https://doi.org/10.1108/978-1-78973-233-720191012openalex-snapshot
AI Revolution: How Data Can Identify and Shape Consumer Behavior in Ecommerce
inferredcites →https://doi.org/10.1109/4235.996017openalex-snapshot
A fast and elitist multiobjective genetic algorithm: NSGA-II
inferredcites →https://doi.org/10.1109/apnoms.2016.7737202openalex-snapshot
Dynamic module deployment in a fog computing platform
inferredcites →https://doi.org/10.1109/dsn.2014.18openalex-snapshot
Failure Analysis of Virtual and Physical Machines: Patterns, Causes and Characteristics
inferredcites →https://doi.org/10.1109/fas-w.2019.00073openalex-snapshot
TeaStore - A Micro-Service Reference Application
inferredcites →https://doi.org/10.1109/jiot.2017.2688925openalex-snapshot
Computing Resource Allocation in Three-Tier IoT Fog Networks: A Joint Optimization Approach Combining Stackelberg Game and Matching
inferredcites →https://doi.org/10.1109/jiot.2017.2701408openalex-snapshot
QoS-Aware Deployment of IoT Applications Through the Fog
inferredcites →https://doi.org/10.1109/jiot.2017.2709814openalex-snapshot
Resource Allocation Strategy in Fog Computing Based on Priced Timed Petri Nets
inferredcites →https://doi.org/10.1109/jiot.2017.2724845openalex-snapshot
Multitier Fog Computing With Large-Scale IoT Data Analytics for Smart Cities
inferredcites →https://doi.org/10.1109/jiot.2017.2780236openalex-snapshot
Multiobjective Optimization for Computation Offloading in Fog Computing
inferredcites →https://doi.org/10.1109/jiot.2018.2889511openalex-snapshot
Availability-Aware Service Placement Policy in Fog Computing Based on Graph Partitions
inferredcites →https://doi.org/10.1109/mic.2017.36openalex-snapshot
Fog Orchestration for Internet of Things Services
inferredcites →https://doi.org/10.1109/tc.2016.2536019openalex-snapshot
Joint Optimization of Task Scheduling and Image Placement in Fog Computing Supported Software-Defined Embedded System
inferredcites →https://doi.org/10.1109/tpds.2019.2901467openalex-snapshot
Performance Modeling and Workflow Scheduling of Microservice-Based Applications in Clouds
inferredcites →https://doi.org/10.1109/tsc.2018.2793266openalex-snapshot
Evolutionary Multi-Objective Optimization for Web Service Location Allocation Problem
inferredcites →https://doi.org/10.1142/s012905410700511xopenalex-snapshot
APPLICATION PLACEMENT ON A CLUSTER OF SERVERS
inferredcites →https://doi.org/10.1145/1287624.1287681openalex-snapshot
Measuring empirical computational complexity
inferredcites →https://doi.org/10.1145/2939672.2939785openalex-snapshot
XGBoost
inferredcites →https://doi.org/10.1145/3186592openalex-snapshot
Latency-Aware Application Module Management for Fog Computing Environments
inferredcites →https://doi.org/10.1145/3344341.3368800openalex-snapshot
Microservices-based IoT Application Placement within Heterogeneous and Resource Constrained Fog Computing Environments
inferredcites →https://doi.org/10.1155/2019/6215454openalex-snapshot
Task Placement on Fog Computing Made Efficient for IoT Application Provision
inferredcites →https://doi.org/10.5220/0006676100680077openalex-snapshot
Deploying Fog Applications: How Much Does It Cost, By the Way?

What points at this work

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

inferred← citeshttps://doi.org/10.1002/spe.2986openalex-snapshot
Toward an autonomic approach for Internet of Things service placement using gray wolf optimization in the fog computing environment
inferred← citeshttps://doi.org/10.1002/spe.3003openalex-snapshot
From zero to fog: Efficient engineering of fog‐based Internet of Things applications
inferred← citeshttps://doi.org/10.1002/spe.3058openalex-snapshot
AuctionWhisk: Using an auction‐inspired approach for function placement in serverless fog platforms
inferred← citeshttps://doi.org/10.1002/spe.3111openalex-snapshot
Deploying IoT services on the fog infrastructure: A graph partitioning‐based approach
inferred← citeshttps://doi.org/10.1002/spe.3383openalex-snapshot
On Optimizing Resources for Real‐Time End‐to‐End Machine Learning in Heterogeneous Edges
inferred← citeshttps://doi.org/10.1007/978-3-031-19211-1_44openalex-snapshot
Joint Edge Server Deployment and Service Placement for Edge Computing-Enabled Maritime Internet of Things
inferred← citeshttps://doi.org/10.1007/s00607-021-01031-8openalex-snapshot
Evaluation of fog application placement algorithms: a survey
inferred← citeshttps://doi.org/10.1007/s00607-022-01142-wopenalex-snapshot
DAIP: a delay-efficient and availability-aware IoT application placement in fog environments
inferred← citeshttps://doi.org/10.1007/s10462-025-11351-2openalex-snapshot
Swarm intelligence techniques and their applications in fog/edge computing: an in-depth review
inferred← citeshttps://doi.org/10.1007/s10515-025-00521-9openalex-snapshot
Continuous integration of architectural performance models with parametric dependencies – the CIPM approach
inferred← citeshttps://doi.org/10.1007/s11227-024-06379-0openalex-snapshot
Dynamic service provisioning in heterogeneous fog computing architecture using deep reinforcement learning
inferred← citeshttps://doi.org/10.1007/s11227-024-06389-yopenalex-snapshot
MOHHO: multi-objective Harris hawks optimization algorithm for service placement in fog computing
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.128688openalex-snapshot
IoT service placement using improved ANFIS classifier and improved dung beetle optimization algorithm in Fog-Cloud computing
inferred← citeshttps://doi.org/10.1016/j.future.2023.11.022openalex-snapshot
A scalable modified deep reinforcement learning algorithm for serverless IoT microservice composition infrastructure in fog layer
inferred← citeshttps://doi.org/10.1016/j.jpdc.2022.12.001openalex-snapshot
Optimal placement of applications in the fog environment: A systematic literature review
inferred← citeshttps://doi.org/10.1016/j.jss.2023.111910openalex-snapshot
MicroFog: A framework for scalable placement of microservices-based IoT applications in federated Fog environments
inferred← citeshttps://doi.org/10.1016/j.swevo.2021.100937openalex-snapshot
Optimization techniques and applications in fog computing: An exhaustive survey
inferred← citeshttps://doi.org/10.1063/5.0210175openalex-snapshot
Survey on evaluation environment selection for fog computing application placement problem
inferred← citeshttps://doi.org/10.1109/access.2021.3085370openalex-snapshot
Service Placement for Latency Reduction in the Fog Using Application Profiles
inferred← citeshttps://doi.org/10.1109/access.2025.3583584openalex-snapshot
A Comprehensive Survey on Resource Management for IoT Applications in Edge-Fog-Cloud Environments
inferred← citeshttps://doi.org/10.1109/issre55969.2022.00043openalex-snapshot
Identifying Erroneous Software Changes through Self-Supervised Contrastive Learning on Time Series Data
inferred← citeshttps://doi.org/10.1109/tnse.2026.3689030openalex-snapshot
Navigating Spatio-Temporal Uncertainty: Adaptive Cloud Resource Management Based on Quantile Central Limit Theorem and Kalman Filtering
inferred← citeshttps://doi.org/10.1109/tnsm.2025.3562913openalex-snapshot
Network-Aware Reliability Modeling and Optimization for Microservice Placement
inferred← citeshttps://doi.org/10.1145/3592598openalex-snapshot
Placement of Microservices-based IoT Applications in Fog Computing: A Taxonomy and Future Directions
inferred← citeshttps://doi.org/10.1145/3696630.3728561openalex-snapshot
A Multimodal Intelligent Change Assessment Framework for Microservice Systems Based on Large Language Models
inferred← citeshttps://doi.org/10.1145/3729214openalex-snapshot
A Survey on Services Placement Algorithms in Integrated Cloud-Fog / Edge Computing
inferred← citeshttps://doi.org/10.3991/ijim.v17i11.37369openalex-snapshot
A Secure Recommender System Model for Service Placement in Wireless Networks
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