Recent Research Publications
Computer Science - Karlstad University
Edge-cloud continuum has become a critical paradigm for supporting latency-sensitive applications that depend on efficient coordination among distributed resources. However, unpredictable hardware or software faults can disrupt services, leading to downtime degradation, performance degradation, and reduced user satisfaction. Enhancing the resilience of edge-cloud computing systems against unpredictable failures remains a critical and challenging issue. This paper introduces FT-PSOGA, a fault-tolerant edge-cloud service placement strategy that integrates Particle Swarm Optimisation (PSO) with a Genetic Algorithm (GA) to enable rapid and efficient redeployment of failed service components. By combining the fast convergence of PSO alongside the global search capabilities of the GA, the proposed method aims to minimise service recovery time while maintaining post-recovery performance in terms of service response time and reliability. FT-PSOGA is evaluated across diverse failure scenarios and system scales and benchmarked against several single- and multi-objective algorithms. The experimental results show that FT-PSOGA can rapidly restore the system in the event of failures and maintain post-recovery performance across different system scales and failure ratios (for example, recovering 60 failed components out of 600 in under six seconds). It minimises the service recovery time without compromising other performance metrics, showing FT-PSOGA as a robust and effective solution for resilient service placement in failure-prone edge-cloud environments.
This editorial highlights the transition from 5G to Beyond 5G and 6G networks, emphasizing their role in supporting high data rates, ultra-low latency, and massive connectivity for applications like smart cities and autonomous systems. The main finding is that future networks require intelligent, secure, and adaptive architectures integrating wireless communication, AI, and emerging technologies. The conclusion emphasizes that advancing these networks demands multidisciplinary collaboration to address evolving challenges in communication, security, and performance optimization.
This study examined how people make decisions when faced with a trade-off between potential rewards and risks across different contexts. The researchers developed a computer-based sequential foraging task where participants had to choose between options with varying probabilities of reward (food gain) and threat (predator encounter). They found that avoidance contextsโwhere avoiding higher-threat options was more advantageousโled participants to rely less on reward probability information and more on integrated value signals derived from the task structure. This suggests that adaptive decision-making arises from context-dependent changes in how decision-relevant information is weighted, rather than switching between entirely different strategies. The findings support a flexible, low-dimensional approach to decision-making that adjusts feature weights based on environmental demands.
The use of Machine Learning (ML) is rapidly expanding across diverse scientific and engineering domains. ML offers a powerful advantage over traditional modeling approaches for predictive modeling and analysis of variables of interest. This makes it particularly useful for developing advanced methods in analytical chemistry and residential energy systems, including forecasting (such as predicting hot water demand or chromatographic peak behavior), data quality assessment (such as detecting sensor drift or anomalous consumption patterns), and fault detection (such as identifying heat pump malfunctions or degraded separation performance). While traditional modeling approaches struggle to fully exploit complex, high-dimensional features, existing ML studies in the target domains often rely on limited datasets and lack automated and adaptive frameworks capable of handling the scale, variability, and non-stationary data generated in real operational settings. The availability of large amounts of data in this digital era offers unprecedented opportunities for analysis. However, the successful application of ML depends critically on the quality, preparation, and robustness of ML models and their underlying data. In industrial systems, these requirements are shaped by several interacting factors, particularly feature representation, model robustness, and adaptation under changing conditions. The challenges addressed in this research include data quality management, model selection, robustness assessment, and adaptation under changing real-world conditions. The main contributions of this research are: (i) a semi-automatic data preparation workflow with domain-specific feature engineering for large-scale oligonucleotide chromatography datasets; (ii) an unsupervised quality-centric evaluation framework that automatically clusters input data by quality level without requiring labeled annotations; (iii) the FIUL-Data fault injection framework, which quantifies the resilience boundaries of ML models under controlled data degradation; and (iv) a composite adaptive framework that integrates predictive ML with anomaly detection to enable demand-driven heat pump management in residential energy systems. Together, these contributions demonstrate that reliable industrial ML is achieved not by increasing model complexity, but through systematic data-centric practices including structured data preparation, quality-aware pipelines, robustness testing, and adaptive learning, applied across two complementary industrial domains.
DDoSimu5G introduces a new simulation framework that for the first time combines realistic Device-to-Device malware propagation with 5G network traffic modeling, showing how infected mobile devices can form botnets that generate coordinated DDoS attacks. The main finding is that D2D-propagated botnet attacks cause nonlinear stress on 5G infrastructure, increasing UPF data rates by 30 times and raising benign user packet loss by 7 times compared to normal traffic loads. The conclusion is that this open-source simulator provides researchers with a valuable tool to study emerging 5G threats and develop mitigation strategies in a controlled, reproducible environment.
We present DSIPTS-P, a Kubernetes-native platform for interactive, distributed spatiotemporal forecasting across edge-cloud infrastructures. DSIPTS-P enables split learning and federated-style execution while automating the full ML lifecycle, including deployment, training, monitoring, and recovery. Built on the DSIPTS stack, the platform provides GPU-and edge-aware scheduling of training and inference pods, declarative YAML-based or GUI-based configuration, and zero-touch deployment with integrated telemetry through Prometheus, Grafana, Kepler, and AIM. In the live demo, attendees actively configure forecasting models, split learning partition points, and hardware placement of training and inference pods, then trigger failures and network impairments on the fly. Real-time dashboards immediately reveal how these choices impact forecasting accuracy, convergence behavior, communication overhead, latency, and energy consumption across heterogeneous edge and cloud resources.ย
Large-scale agile frameworks like SAFe intended to streamline coordination in fintech organizations paradoxically create systemic tensions that degrade developer well-being. The study identified six interacting socio-technical tensionsโmost critically coordination overload and governance ambiguityโthat fragment cognitive focus, erode psychological safety, and accumulate technical debt. The main conclusion is that developer well-being functions as a leading indicator of socio-technical sustainability, not a peripheral HR concern, and that sustainable scaling requires balancing coordination intensity, governance clarity, and contextual flexibility rather than optimizing any single dimension in isolation.
Network diversity, high reliability, intelligence, and the Internet of Things (IoT) are at the forefront of the distributed Edge-native computing vision, which aims to provide orchestration, better resource utilization, and self-optimizing networks. The integration of emerging disruptive technologies, namely AI, network autonomy, and a hybrid cloud computing architecture, with edge-native computing, propels the rapid development of an upgraded service-oriented architecture at the networkรขยยs core/edge level. This acceleration is intended to support on-demand microservices, specifically focusing on visibility services essential for intelligent network management. This article explores the significance of AI-enabled ENC, including its design requirements and projected benefits. To address the challenges of managing and optimizing complex, heterogeneous distributed networks, we present a use case study and experimental evaluation based on the design of an edge intelligence framework. This framework leverages resources of a multisite cloud/edge-native microservices testbed to enhance network visibility and automation, enabling the deployment and orchestration of AI-powered microservices at the network edge. By applying statistical, machine learning, and deep learning-based analyses to this diverse dataset, we compare the accuracy of these forecasting models and demonstrate the effectiveness of our approach in accurately predicting network traffic patterns, even under varying network conditions. This accuracy is validated through low root-mean-square error and high coefficient of determination values. Our results highlight the potential of AI-enabled edge-native computing to revolutionize network management and optimization, enabling timely detection of performance degradation and proactive mitigation strategies.ย
The edge-cloud paradigm improves service delivery by orchestrating resources across edge nodes and cloud data centres. These environments consist of heterogeneous, interconnected computing nodes that cooperate to deliver continuous services. However, their scale and complexity increase vulnerability to failures from hardware malfunctions, software defects, and dynamic operating conditions. These failures can disrupt system configurations and service execution, leading to reduced reliability, performance degradation, and violations of service-level objectives. Ensuring service execution requires adaptive service placement strategies across edge-cloud resources. This study introduces a fault-tolerant service placement approach (Enhanced Evolution Strategy for Collaborative Neural Decision-making, EES-CND) for edge-cloud environments. The method employs collaborative decision-making, wherein multiple lightweight neural networks jointly infer redeployment strategies during failure events. To address the system dynamics and mitigate performance drift, adaptive models are updated online using an enhanced evolution strategy. Extensive simulations show that EES-CND effectively handles performance drift and significantly outperforms existing methods in service recovery time, response time, and reliability, achieving a 44.8% reduction in fault-tolerance cost compared to standalone models.ย
In modern video streaming systems, clients typically make independent bitrate decisions without full knowledge of network conditions, often leading to inefficient resource usage and unstable quality. Network-assisted adaptive streaming is rapidly gaining importance in Network and Service Management (NSM), as operators strive to deliver consistently high Quality of Experience (QoE) under dynamic traffic conditions. This paper introduces FROG, a novel, real-time Software-Defined Networking (SDN)-assisted framework designed to coordinate multi-server and multipath HTTP Adaptive Streaming (HAS) using standardized metrics carriage: Common Media Client Data (CMCD) and Common Media Server Data (CMSD). FROG employs an innovative two-stage optimization workflow in which an initial Linear Programming (LP) model rapidly determines feasible bandwidth bounds, server selection, and path capacities, thereby transforming the remaining optimization into a sequential layer-selection process for coordinated quality selection and flow allocation. This decomposition enables sub-second optimization at the scale of thousands of users, while buffer-aware client feedback is integrated to proactively prevent stalls and maintain system stability. Experiments on an emulated SDN testbed demonstrate that FROG achieves QoE comparable to that of the optimal MILP solution on tractable instances while virtually eliminating video stalls, and significantly reduces quality oscillations, outperforming state-of-the-art network-assisted approaches by up to 2.32ร under playback-driven evaluation scenarios. Scalability experiments with up to 4,000 clients further demonstrate sub-second optimization runtimes, confirming the practicality of FROG for large-scale deployments.ย
The research presents a web-based orchestration system prototype designed to simplify containerized microservices management for Industry 5.0 applications. The main finding is that the prototype achieved a System Usability Scale (SUS) score of 77.25, indicating good usability and demonstrating significant improvements over traditional Kubernetes interfaces. The conclusion highlights that human-centric design can enhance trust, collaboration, and adoption of cloud-native platforms in industrial settings by reducing complexity and cognitive load for users with varying expertise levels.
3GPP Access Traffic Steering, Switching, and Splitting (ATSSS) enables traffic to be distributed across heterogeneous 3GPP and non-3GPP access networks to improve performance, reliability, and resilience. ATSSS can use multipath transport protocols such as Multipath QUIC (MP-QUIC), where packet scheduling plays a central role in determining latency and resource utilization for delay-sensitive applications. Many existing MP-QUIC scheduling policies rely on instantaneous path measurements or fixed rules rather than forecasts of future application demand. In multi-flow scenarios, such decisions can lead either to contention on the preferred low-latency path and transient latency inflation for priority traffic or to overly conservative use of available capacity. This paper proposes iScavenger, a predictive, machine-learning-based multi-flow scheduling policy for ATSSS environments. iScavenger employs a Long Short-Term Memory (LSTM) model to forecast near-future bandwidth demand for delay-sensitive Sticky traffic. Based on this prediction, background packets are admitted to the preferred low-latency path only when sufficient residual capacity is expected to remain; otherwise, they are steered to the alternative path. The policy is implemented within the Monty MP-QUIC framework and evaluated in a controlled Mininet testbed using traffic traces from the online game League of Legends, with fixed and variable path capacities and controlled jitter and packet loss. The results indicate that, under the evaluated conditions, iScavenger provides configurable operating points in the latencyโutilization trade-off, limiting additional Sticky-flow RTT while achieving higher background throughput than conservative baseline policies. These findings highlight the potential of short-term traffic-demand prediction for proactive contention management in ATSSS-enabled multi-access networks.
Modern high-speed packet processing systems must enforce strict Quality-of-Service (QoS) guarantees on loss, latency, and throughput across many concurrent flows, typically using hierarchical QoS (HQoS) schedulers. In NFV environments, software schedulers provide fine-grained control, but maintaining QoS at line rate requires careful coordination between software and hardware. We show that uncoordinated per-core software schedulers can oversubscribe a shared NIC, causing packet drops in NIC transmit queues that violate HQoS guarantees even when multiple hardware priority queues are available. To address this, we propose Priority-Aware Backpressure (PAB), a host-NIC coordination mechanism that feeds NIC congestion signals back to the software scheduler to selectively throttle low-priority traffic while protecting high-priority flows. We implement PAB in the DPDK QoS framework and leverage the shaping hardware capabilities of a commodity NIC. We demonstrate that PAB preserves hierarchical QoS guarantees under sustained overload, while maintaining fairness, low latency for high-priority traffic and only modestly reducing throughput compared to uncoordinated schedulers. ยฉ 2026 IEEE.
The main finding of this research is that implementing the rights to rectification and erasure under GDPR in machine learning systems is technically challenging due to the complex nature of ML models and their supply chains. The study concludes that current technical methods are insufficient to fully meet GDPR requirements, particularly when models are part of multi-stage supply chains where personal data persists in downstream derivatives, termed "models in the dark."
This study investigated onboarding processes in large-scale agile (LSA) projects through qualitative interviews with 41 participants across four Swedish software companies. The main finding was that inadequate onboarding in LSA contexts creates significant socio-technical debt, manifesting as cognitive overload, psychological safety constraints, and cross-team collaboration difficulties that lead to steep learning curves and knowledge gaps. The conclusion recommends designing competency-based rather than time-based onboarding plans, promoting structured cross-team social integration, and explicitly aligning real-world LSA practices with textbook agile methodologies to improve new employee integration.
As 5G networks adopt cloud-native architectures, operational complexity motivates automation of several parts of the network, especially fault management. While Large Language Models (LLMs) show promise as autonomous operators, current evaluations rely on static benchmarks that measure plausibility rather than functionality. We introduce OperAID, an open-source testbed that evaluates LLM agents through a closed-loop pipeline comprising fault injection, diagnosis, remediation, and executionbased verification on a live Open5GS deployment in Kubernetes. Across 900 experiments spanning three fault scenarios with five open-source LLMs, we find that: (i) tool access increases average performance from 10.9 % to 61.1 %; (ii) model size alone does not guarantee success even with tools; and (iii) without tools, models fail to diagnose faults requiring live system observation.ย