Boris Sedlak

A Multiplicative Innovation–Retention Balance Governs Epistemic Depth in Bounded Multi-Agent Systems

Ildefons Magrans de Abril, Nefeli-Marina Rouska, Boris Sedlak, Víctor Casamayor Pujol et autres

Decentralized adaptive systems must generate new structure while operating under finite retention. Cells, organizations, and artificial agents alike face a common tension: innovation introduces novelty, but finite retention limits how long intermediate structures persist. We introduce a minimal multi-agent model in which …

es, at (code pays fourni par la source)

A Taxonomy of Performance Metrics for the Distributed Computing Continuum

Praveen Kumar Donta, Boris Sedlak, Alfreds Lapkovskis, Alaa Saleh et autres

Performance evaluation is essential for understanding, comparing, and improving computing systems, including Distributed Computing Continuum Systems (DCCS). In recent years, computational requirements have changed substantially with the growth of artificial intelligence and large-scale data-driven applications. These application tasks are increasingly distributed between …

Clustered Edge Intelligence: Beyond Just Convergence of Edge Computing and AI

Chinmaya Kumar Dehury, Boris Sedlak, Alaa Saleh, Ilir Murturi et autres

We are moving from an information age to the age of intelligence. A decade, or possibly less than that, data will not be the gold anymore rather the derived intelligence out of the data and the information we posses from the edge …

borissedlak/elastic-workbench: TSC Zenodo

Boris Sedlak, Philipp Raith, Andrea Morichetta, Víctor Casamayor Pujol et autres

for Zenodo and TSC submission

at, es (code pays fourni par la source)

borissedlak/elastic-workbench: TSC Zenodo

Boris Sedlak, Philipp Raith, Andrea Morichetta, Víctor Casamayor Pujol et autres

for Zenodo and TSC submission

at, es (code pays fourni par la source)

Towards a Unified View of Configuration Problems in the Computing Continuum

Alejandro García-Fernández, Boris Sedlak, José Antonio Parejo, Pantelis A. Frangoudis et autres

es, at (code pays fourni par la source)

Invited Paper: Composable Autonomic Offerings for Decentralized Service Provisioning in the Computing Continuum

Ildefons Magrans de Abril, Boris Sedlak, Víctor Casamayor Pujol, Schahram Dustdar

es (code pays fourni par la source)

Fair Comparison of Scheduling Algorithms on Heterogeneous Edge Clusters: A Continuous Adaptive Benchmark

Zihang Wang, Boris Sedlak, Juan Luis Herrera, Schahram Dustdar

Modern Artificial Intelligence (AI) workloads deployed across the heterogeneous tiers of an edge--cloud continuum must satisfy multi-dimensional Service Level Objectives (SLOs) over latency, throughput, and output quality. For each incoming task, the scheduler picks both a target node and a processing mode …

Fair Comparison of Scheduling Algorithms on Heterogeneous Edge Clusters: A Continuous Adaptive Benchmark

Zihang Wang, Boris Sedlak, Juan Luis Herrera, Schahram Dustdar

Modern Artificial Intelligence (AI) workloads deployed across the heterogeneous tiers of an edge--cloud continuum must satisfy multi-dimensional Service Level Objectives (SLOs) over latency, throughput, and output quality. For each incoming task, the scheduler picks both a target node and a processing mode …

at, es (code pays fourni par la source)

Fair Comparison of Scheduling Algorithms on Heterogeneous Edge Clusters: A Continuous Adaptive Benchmark

Zihang Wang, Boris Sedlak, Juan Luis Herrera, Schahram Dustdar

at, es (code pays fourni par la source)

Active Inference-Based Adaptive Routing for Heterogeneous Edge AI Services

Zihang Wang, Boris Sedlak, Schahram Dustdar

Edge computing enables AI inference closer to data sources, reducing latency and bandwidth costs. However, orchestrating AI services across the cloud-edge continuum remains challenging due to dynamic workloads and infrastructure variability. We present AIF-Router, an Active Inference--based routing framework that autonomously learns …

Active Inference-Based Adaptive Routing for Heterogeneous Edge AI Services

Zihang Wang, Boris Sedlak, Schahram Dustdar

Edge computing enables AI inference closer to data sources, reducing latency and bandwidth costs. However, orchestrating AI services across the cloud-edge continuum remains challenging due to dynamic workloads and infrastructure variability. We present AIF-Router, an Active Inference--based routing framework that autonomously learns …

at, es (code pays fourni par la source)