This paper presents CRAFTER, an automated framework for designing and deploying self-adaptive IoT systems using Causal Reinforcement Learning (CRL), and demonstrates how CRAFTER improves self-adaptation performance by 25% compared to state-of-the-art Reinforcement Learning-based approaches.
Houssam Hajj Hassan, A. Kattepur, Denis Conan et al.· SEAMS@ICSE· 0 citations
A formal model that unifies functional, pipelined, and data-parallel partitioning strategies within a single abstraction over heterogeneous CC topologies, enabling structured cross-strategy comparison and enabling empirical, cross-strategy comparison of distributed inference deployments is introduced.
Nikolaos Papadakis, Alexandros Angourakis, K. Magoutis et al.· International Conference on...· 0 citations
A queueing network (QN) model of the rebalance process of the process used in MongoDB is developed, and a preliminary evaluation of performance prediction by the QN model via simulations is described, to understand the impact of different parameters of the rebalance process.
G. Paterakis, E. Ntallaris, K. Magoutis et al.· International Symposium on C...· 0 citations
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