7 papers
Multi-Dimensional Autoscaling of Stream Processing Services on Edge Devices
Boris Sedlak, Philipp Raith, Andrea Morichetta +2
Edge devices have limited resources, which inevitably leads to situations where stream processing services cannot satisfy their needs. While existing autoscaling mechanisms focus e…
BIPPO: Budget-Aware Independent PPO for Energy-Efficient Federated Learning Services
Anna Lackinger, Andrea Morichetta, Pantelis A. Frangoudis +1
Federated Learning (FL) is a promising machine learning solution in large-scale IoT systems, guaranteeing load distribution and privacy. However, FL does not natively consider infr…
MACH: Multi-Agent Coordination for RSU-centric Handovers
Nikolaus Spring, Andrea Morichetta, Boris Sedlak +1
This paper introduces MACH, a novel approach for optimizing task handover in vehicular computing scenarios. To ensure fast and latency-aware placement of tasks, the decision-making…
Formal and Empirical Study of Metadata-Based Profiling for Resource Management in the Computing Continuum
Andrea Morichetta, Stefan Nastic, Victor Casamayor Pujol +1
We present and formalize a general approach for profiling workload by leveraging only a priori available static metadata to supply appropriate resource needs. Understanding the req…
Towards Multi-dimensional Elasticity for Pervasive Stream Processing Services
Boris Sedlak, Andrea Morichetta, Philipp Raith +2
This paper proposes a hierarchical solution to scale streaming services across quality and resource dimensions. Modern scenarios, like smart cities, heavily rely on the continuous…
Towards Adaptive Asynchronous Federated Learning for Human Activity Recognition
Rastko Gajanin, Anastasiya Danilenka, Andrea Morichetta +1
In this work, we tackle the problem of performing multi-label classification in the case of extremely heterogeneous data and with decentralized Machine Learning. Solving this issue…