As digital infrastructure evolves, traditional monolithic applications are rapidly reaching their limits. With the rise of 5G/6G, virtualization, and optical networks, application design must adapt to meet increasing Quality of Service (QoS) demands.
Enter the cloud-native paradigm: by decomposing applications into microservices, we unlock a new era of flexibility, scalability, and resilience.
But there’s more. With emerging services generating massive data volumes at the edge, and the need for ultra-low latency, the edge-cloud continuum becomes crucial. Combining cloud and edge resources creates a dynamic infrastructure capable of adapting in real time to application needs.
💡 In the Allegro Project, we developed an intelligent resource allocation mechanism that:
- Jointly optimizes service delay and cost
- Ensures microservice dependencies meet required latency thresholds
- Adapts to heterogeneous edge-cloud infrastructures
🔢 We first modeled the problem as a Mixed Integer Linear Programming (MILP) formulation to define the optimal solution space. However, due to scalability limitations, we introduced:
- A fast heuristic algorithm to find high-quality solutions in real time
- A Rollout technique leveraging Reinforcement Learning (RL) principles to iteratively refine results and further optimize system performance
This framework empowers cloud-native applications to dynamically adapt to the available infrastructure—ensuring efficiency, cost-effectiveness, and high service quality in tomorrow’s networks.
🚀 Stay tuned as we continue pushing the boundaries of smart, distributed application management!
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