This paper presents CRAFTER, an automated framework for designing and deploying self-adaptive IoT systems using Causal Reinforcement Learning (CRL). As IoT devices increasingly populate pervasive computing spaces, smart environments are enabled with advanced monitoring and interactive services. The dynamic nature of these environments, such as fluctuating workloads and evolving application demands, poses significant challenges in maintaining consistent Quality of Service (QoS) levels of IoT applications. While existing self-adaptation techniques offer adaptive capabilities, they are often designed to deal with specific application domains, hindering the design of self-adaptive solutions that can be re-used across multiple IoT verticals. In addition, there is a lack of automated pipelines that act on identifying key performance drivers to take effective adaptation decisions. CRAFTER addresses these issues by using Causality as a formal framework for performance analysis of IoT systems. CRAFTER generates causal graphs to uncover dependencies among system components and guide adaptation decisions based on cause–effect relationships. Then, adaptation agents can leverage this knowledge to take more effective adaptation decisions in dynamic situations. Our experimental evaluation demonstrates how CRAFTER enables deriving causal graphs spanning diverse IoT use cases. Furthermore, we showcase 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
As deep learning permeates latency-sensitive domains such as autonomous driving and smart surveillance, deploying neural networks (NNs) across the computing continuum (CC), from IoT devices to edge servers and cloud platforms, has become increasingly important. In such heterogeneous IoT-Edge-Cloud environments, distributed inference promises reduced latency, improved privacy, and better resource utilization. Yet, determining how to deploy NNs over heterogeneous IoT-Edge-Cloud nodes remains a difficult and largely manual process. This paper presents a principled and extensible framework for evaluating distributed inference of NNs in heterogeneous CC infrastructures. We introduce 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. Building on this foundation, we implement a distributed inference orchestrator that supports flexible deployment of partitioned CNNs, and introduce PartiBench, a benchmarking tool that profiles segments and guides their placement. Our evaluation demonstrates how the framework exposes key performance trade-offs, offering actionable insights into latency, memory use, and communication overhead across IoT-Edge-Cloud nodes. These contributions enable empirical, cross-strategy comparison of distributed inference deployments and provide a basis for future automated placement methods in heterogeneous IoT-Edge-Cloud systems.
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