29 citations · 36 across the 17 of their papers we have counts for
19 papers
A Taxonomy of Performance Metrics for the Distributed Computing Continuum
Praveen Kumar Donta, Boris Sedlak, Alfreds Lapkovskis +6
Performance evaluation is essential for understanding, comparing, and improving computing systems, including Distributed Computing Continuum Systems (DCCS). In recent years, comput…
Clustered Edge Intelligence: Beyond Just Convergence of Edge Computing and AI
Chinmaya Kumar Dehury, Boris Sedlak, Alaa Saleh +4
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…
Fair Comparison of Scheduling Algorithms on Heterogeneous Edge Clusters: A Continuous Adaptive Benchmark
Zihang Wang, Boris Sedlak, Juan Luis Herrera +1
Modern Artificial Intelligence (AI) workloads deployed across the heterogeneous tiers of an edge--cloud continuum must satisfy multi-dimensional Service Level Objectives (SLOs) ove…
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 challen…
Pricing-Driven Resource Allocation in the Computing Continuum
Alejandro García-Fernández, Boris Sedlak, José Antonio Parejo +3
Deploying applications across the computing continuum requires selecting infrastructure nodes from geographically distributed and heterogeneous environments while satisfying constr…
Visual Insights into Agentic Optimization of Pervasive Stream Processing Services
Boris Sedlak, Víctor Casamayor Pujol, Schahram Dustdar
Processing sensory data close to the data source, often involving Edge devices, promises low latency for pervasive applications, like smart cities. This commonly involves a multitu…
Service Orchestration in the Computing Continuum: Structural Challenges and Vision
Boris Sedlak, Víctor Casamayor Pujol, Ildefons Magrans de Abril +3
The Computing Continuum (CC) integrates different layers of processing infrastructure, from Edge to Cloud, to optimize service quality through ubiquitous and reliable computation.…
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…
Multi-dimensional Autoscaling of Processing Services: A Comparison of Agent-based Methods
Boris Sedlak, Alireza Furutanpey, Zihang Wang +2
Edge computing breaks with traditional autoscaling due to strict resource constraints, thus, motivating more flexible scaling behaviors using multiple elasticity dimensions. This w…
Distributed Intelligence in the Computing Continuum with Active Inference
Victor Casamayor Pujol, Boris Sedlak, Tommaso Salvatori +2
The Computing Continuum (CC) is an emerging Internet-based computing paradigm that spans from local Internet of Things sensors and constrained edge devices to large-scale cloud dat…
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…
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…
Benchmarking Dynamic SLO Compliance in Distributed Computing Continuum Systems
Alfreds Lapkovskis, Boris Sedlak, Sindri Magnússon +2
Ensuring Service Level Objectives (SLOs) in large-scale architectures, such as Distributed Computing Continuum Systems (DCCS), is challenging due to their heterogeneous nature and…
Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated Learning
Anastasiya Danilenka, Alireza Furutanpey, Victor Casamayor Pujol +5
Handling heterogeneity and unpredictability are two core problems in pervasive computing. The challenge is to seamlessly integrate devices with varying computational resources in a…
Adaptive Stream Processing on Edge Devices through Active Inference
Boris Sedlak, Victor Casamayor Pujol, Andrea Morichetta +2
The current scenario of IoT is witnessing a constant increase on the volume of data, which is generated in constant stream, calling for novel architectural and logical solutions fo…
SLO-Aware Task Offloading within Collaborative Vehicle Platoons
Boris Sedlak, Andrea Morichetta, Yuhao Wang +4
In the context of autonomous vehicles (AVs), offloading is essential for guaranteeing the execution of perception tasks, e.g., mobile mapping or object detection. While existing wo…
Equilibrium in the Computing Continuum through Active Inference
Boris Sedlak, Victor Casamayor Pujol, Praveen Kumar Donta +1
Computing Continuum (CC) systems are challenged to ensure the intricate requirements of each computational tier. Given the system's scale, the Service Level Objectives (SLOs) which…
Active Inference on the Edge: A Design Study
Boris Sedlak, Victor Casamayor Pujol, Praveen Kumar Donta +1
Machine Learning (ML) is a common tool to interpret and predict the behavior of distributed computing systems, e.g., to optimize the task distribution between devices. As more and…
Designing Reconfigurable Intelligent Systems with Markov Blankets
Boris Sedlak, Victor Casamayor Pujol, Praveen Kumar Donta +1
Compute Continuum (CC) systems comprise a vast number of devices distributed over computational tiers. Evaluating business requirements, i.e., Service Level Objectives (SLOs), requ…