AI-POWERED INSIGHTS
Efficient workflow scheduling in fog-cloud collaboration using a hybrid IPSO-GWO algorithm
A novel hybrid optimization strategy, IPSO-GWO, integrates Improved Particle Swarm Optimization (IPSO) and Grey Wolf Optimization (GWO) to optimize workflow scheduling in heterogeneous fog-cloud environments, ensuring a balanced trade-off between exploration and exploitation. This approach aims to reduce makespan, energy consumption, and total cost in real-world scientific workflows up to 1000 tasks.
Executive Impact Snapshot
Our analysis highlights the critical performance gains achievable with hybrid IPSO-GWO scheduling.
Average Makespan Reduction
Energy Consumption Reduction
Total Cost Reduction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Problem Formulation
The paper formulates workflow scheduling as an optimization problem aiming to minimize makespan, energy consumption, and total cost in a heterogeneous Fog-Cloud-IoT environment. Workflows are modeled as Directed Acyclic Graphs (DAGs), where tasks have dependencies. The goal is to efficiently map tasks to available resources (Cloud VMs, Fog Devices, Edge Devices) while respecting these dependencies and optimizing the combined objective function.
IPSO Algorithm
The Improved Particle Swarm Optimization (IPSO) algorithm enhances standard PSO by dynamically adjusting the inertia weight. This allows for a better balance between exploration (global search) in early iterations and exploitation (local refinement) in later iterations. IPSO aims to prevent premature convergence and find more accurate solutions by refining the search around promising regions. It uses a linearly decreasing inertia weight from a 'Wbegin' to 'Wend' value.
GWO Algorithm
The Grey Wolf Optimization (GWO) algorithm mimics the hunting behavior and social hierarchy of grey wolves. It models four levels of wolves: alpha (leader), beta (second best), delta (third best), and omega (followers). The top three wolves guide the search for prey (optimal solution) through encircling and hunting mechanisms. GWO is effective in avoiding local optima and maintaining search diversity.
Hybrid IPSO-GWO
The proposed hybrid IPSO-GWO algorithm integrates the strengths of both IPSO and GWO. It begins with IPSO for half of the total iterations, focusing on refined exploitation with its dynamic inertia weight. The best solution found by IPSO is then passed as the initial alpha wolf to the GWO phase, which takes over for the remaining iterations. GWO enhances exploration and diversity by updating wolf positions around multiple leaders. This synergistic approach aims to achieve faster convergence, superior performance, and a robust balance between exploration and exploitation.
Average Makespan Reduction
Enterprise Process Flow
Initialize IPSO Population (Task-VM Mappings)
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Set IPSO Parameters (W_begin, W_end, C1, C2)
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Loop for 1st Half Iterations (IPSO Phase)
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Calculate Inertia Weight
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Evaluate Fitness Function
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Update Velocity & Position
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Update Pbest & Gbest
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Pass Gbest to GWO (as Alpha Wolf)
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Loop for 2nd Half Iterations (GWO Phase)
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Update Wolf Positions (Encircling & Hunting)
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Evaluate Fitness Function
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Update Alpha, Beta, Delta Wolves
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Return Alpha Wolf (Optimal Schedule)
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Application in Scientific Workflows
The hybrid IPSO-GWO algorithm was successfully applied to various scientific workflows like Montage, Epigenomics, CyberShake, SIPHT, and LIGO.
Results consistently demonstrate superior performance over PSO, GWO, IPSO, and GSA in optimizing key metrics, especially for larger task sizes (up to 1000 tasks).
This robust performance across diverse real-world workflows highlights the algorithm's adaptability and efficiency in complex distributed systems.
Calculate Your Potential AI ROI
Estimate the significant financial and operational benefits of optimizing your enterprise workflows with AI.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A typical phased approach to integrate and leverage these advanced AI capabilities within your enterprise.
Phase 1: Environment Setup
Configure FogWorkflowSim, define IFC architecture (IoT, Fog, Cloud layers), and set up VM and task parameters.
Phase 2: Algorithm Integration
Implement IPSO-GWO logic, including dynamic inertia weight, GWO's leadership model, and the phased transition between algorithms.
Phase 3: Workflow Mapping & Execution
Encode tasks and VMs into solution vectors, apply IPSO-GWO for scheduling, and execute workflows respecting DAG dependencies.
Phase 4: Performance Evaluation
Measure and analyze makespan, energy consumption, and total cost across various scientific workflows and task sizes (100, 500, 1000).
Phase 5: Statistical Validation & Refinement
Perform ANOVA test to confirm statistical significance of results, identify areas for further optimization, and finalize algorithm parameters.
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