Gianluca Reali

Faraz Shaikh, Gianluca Reali, Mauro Femminella

In the emerging landscape of edge computing, the stochastic and bursty nature of serverless workloads presents a critical challenge for autonomous resource orchestration. Traditional reactive controllers, such as the Kubernetes Horizontal Pod Autoscaler (HPA), suffer from inherent reaction latency, leading to Service Level Objective (SLO) violations during traffic spikes and resource flapping during ramp-downs. While Deep Reinforcement Learning (DRL) offers a pathway toward proactive management, standard agents suffer from temporal blindness, an inability to effectively capture long-term dependencies in non-Markovian edge environments. To bridge this gap, we propose a novel stability-aware autoscaling framework unifying workload forecasting and control via an Attention-Enhanced Double-Stacked LSTM architecture integrated within a Proximal Policy Optimization (PPO) agent. Unlike shallow recurrent models, our approach employs a deep temporal attention mechanism to selectively weight historical states, effectively filtering high-frequency noise while retaining critical precursors of demand shifts. We validate the framework on a heterogeneous cluster using real-world Azure Functions traces. Comparative analysis against industry-standard HPA, stateless Double DQN, and a single-layer LSTM ablation demonstrates that our approach reduces 90th percentile latency by approximately 29% while simultaneously decreasing replica churn by 39%, relative to the single-layer LSTM baseline. These results confirm that mitigating temporal blindness through deep attentive memory is a prerequisite for reliable, low-jitter autoscaling in production edge environments.

Gianluca Reali, Mauro Femminella

The information flows in serverless platforms are complex and non-conservative. This is a direct result of how independently deployed functions interact under the platform coarse-grained control mechanisms. To manage this complexity, we introduce a topological model for serverless services. Using Hodge decomposition, we can separate observed operational flows into two distinct categories. They include components that can be corrected locally and harmonic modes that persist at any scale. Our analysis reveals that these harmonic flows emerge naturally from different types of inter-function interactions. They should be understood as structural properties of serverless systems, not as configuration errors. Building on this insight, we present an iterative method for analyzing inter-function flows. This method helps deriving practical remediation strategies. One such strategy is the introduction of "dumping effects" to contain harmonic inefficiencies, offering an alternative to completely restructuring the service's topological model. Our experimental results confirm that this approach can uncover latent architectural structures.

Gianluca Reali, Mauro Femminella

In this paper we propose a method for analyzing services deployed in serverless platforms. These services typically consists of orchestrated functions that can exhibit complex and non-conservative information flows due to the interaction of independently deployed functions under coarse-grained control mechanisms. We introduce a topological model of serverless services and make use of the Hodge decomposition to partition observed operational flows into locally correctable components and globally persistent harmonic modes. Our analysis shows that harmonic flows naturally arise from different kind of interactions among functions and should be interpreted as structural properties of serverless systems rather than configuration errors. We present a systematic methodology for analyzing inter-function flows and deriving actionable remediation strategies, including dumping effects to contain the effects of harmonic inefficiencies as an alternative to completely restructure the topological model of the service. Experimental results confirm that the proposed approach can uncover latent architectural structures leading to inefficiencies.

Priscilla Benedetti, Damiano Perri, Marco Simonetti et al.

Medical data classification is typically a challenging task due to imbalance between classes. In this paper, we propose an approach to classify dermatoscopic images from HAM10000 (Human Against Machine with 10000 training images) dataset, consisting of seven imbalanced types of skin lesions, with good precision and low resources requirements. Classification is done by using a pretrained convolutional neural network. We evaluate the accuracy and performance of the proposal and illustrate possible extensions.