Multi-Area, Multi-Service and Multi-Tier Edge-Cloud Continuum Planning
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View abstract on PubMed
Summary
This summary is machine-generated.Optimal planning for edge-cloud environments involves deploying compute continuum resources efficiently. Batch-based task processing offers a faster, scalable solution for complex computing needs.
Area Of Science
- Computer Science
- Distributed Systems
- Edge Computing
Background
- Edge-cloud environments are increasingly complex, requiring efficient resource management.
- Optimal planning is crucial for meeting diverse processing, rate, and latency demands.
- Existing offline planning methods may struggle with real-world problem sizes.
Purpose Of The Study
- To investigate optimal planning strategies for multi-area, multi-service, multi-tier edge-cloud systems.
- To evaluate different approaches for deploying the compute continuum (processing devices and their allocation).
- To analyze the trade-offs between performance and execution time for various planning schemes.
Main Methods
- Comparison of three offline compute continuum planning schemes: one processing all tasks at once, and two using iterative task batching.
- Analysis of direct complex versus simpler, faster planning methods.
- Investigation of task selection strategies, including random selection and clustering methods, for group-based approaches.
Main Results
- Processing all tasks simultaneously offers better performance but incurs longer execution times.
- Iterative, batch-oriented schemes provide a faster approach and are more scalable for larger problems.
- Random task selection in group-based schemes generally yields better performance compared to other strategies.
Conclusions
- Batch-oriented compute continuum planning is effective for large-scale edge-cloud environments.
- The choice of task selection strategy significantly impacts the performance of group-based planning.
- Random task selection emerges as a robust strategy for optimizing performance in these systems.
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