News

In our recent analysis of Figure 35 from ALLEGRO , we explored the optimization landscape without 𝜀-constraints and the outcomes were remarkable. 📈 Key finding: Even in the absence of constraint tuning (indicated by "inf"), we identified a feasible solution that...

In our latest analysis within the Allegro project, we dove deeper into how objective prioritization influences optimization outcomes. 🧠 From the charts, one pattern is unmistakable: The primary objective (first in the lexicographic order) consistently achieves the...

As part of our work on the Allegro project, we investigated the effects of various sequencing strategies by framing our multi-objective optimization challenge as a lexicographic optimization problem. 📊 In the analysis, we explored all permutations of three key...

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...

As part of our work with Allegro, we evaluated performance across two hierarchical cloud-edge topologies: 📊 Topologies Analyzed: Basic: 19 nodes Extended: 53 nodesBoth consist of 3 layers: near-edge, far-edge, and cloud, with the cloud layer provisioned to handle all...

In the context of the ALLEGRO Project , we developed a powerful Multi-Agent Rollout Mechanism to further optimize microservice placement across distributed edge/cloud infrastructures. 🌍⚙️ 🧠 Why Rollout?Standard heuristics can be fast but suboptimal. Exact methods? Too...

As part of the #ALLEGRO project , we developed an intelligent Greedy Resource Allocation Mechanism for efficient microservice placement across edge/cloud infrastructure. 🌐⚙️ 🔍 What does it do?Our heuristic approach balances performance and resource efficiency by...

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...

In our latest work on the Allegro project, we applied lexicographic optimization to tackle complex combinatorial problems involving conflicting objectives like cost, latency, and availability. Rather than optimizing all objectives at once, lexicographic optimization...

As part of our ongoing work in intelligent distributed storage, we evaluated the performance of our Deep Reinforcement Learning (DRL) mechanism during the inference stage, comparing it against an optimal solver across various optimization objectives. 🧠 Figure 26...