As part of the Allegro Project, we explored how infrastructure layers are utilized under different optimization goals — with some insightful findings.
📊 Layer Utilization (Figure 32):
- Latency Optimization heavily favors the near-edge due to its proximity and responsiveness to data sources.
- Cost & Energy Optimization shift the load toward the cloud, benefiting from lower processing costs and reduced energy usage.
- When balancing latency, cost, and energy, the far-edge emerges as the dominant layer, offering the best trade-offs across all metrics.
💸 Cost Component Analysis (Figure 33):
We broke down monetary cost into operational and networking costs based on optimization focus:
- Cost Minimization: Cloud is preferred. Operational and networking costs contribute almost equally—cloud’s low processing cost offsets the higher networking cost of data transfer.
- Latency Minimization: Edge nodes dominate. Here, processing cost is the major factor, while networking costs account for only 12% of total costs.
⚙️ These insights guide intelligent workload orchestration across the edge-cloud continuum, optimizing for various performance and sustainability targets.
#EdgeComputing #CloudComputing #AIInfrastructure #CostOptimization #LatencyOptimization #AllegroProject #SustainableAI #PerformanceEngineering #DistributedSystems #MultiObjectiveOptimization
