Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
Software-Defined Networking (SDN) has emerged as a revolutionary paradigm. The integration of SDN within fog networks represents a synergistic convergence of two cuttingedge technologies. With the complexity of SDN serving fog networks, the optimization of communication cost becomes paramount. Addressing the intricate challenges of communication cost optimization necessitates the application of sophisticated methodologies. Multi-Objective Optimization (MOO) algorithms present a robust solution, allowing for the simultaneous optimization of multiple conflicting objectives. By employing MOO, this research proposes a bi-objective optimization model for the intra- and interdomain communication cost of controller deployment in an SDN-based computing network. The evaluation performed has captured two aspects of the performance of using Binary Angle quantization Multi-objective Particle swarm optimization (BAMP) and Binary crowding Distance Angle quantization Multi-objective Particle swarm optimization (BDAMP) for SDN controllers’ deployment. The first aspect is multi-objective-based evaluation, and the second aspect is the SDN network performance. Our developed BAMP and BDAMP have shown superiority over the benchmarks in terms of both aspects. Most importantly, the best performance is achieved by BDAMP in terms of both intra- and intercommunication cost....
The proliferation of intelligent edge devices demands compact, low-power hardware capable of dynamically switching between sensing, logic, and learning tasks—a versatility that traditional multi-chip solutions fundamentally lack. Here, we demonstrate a reconfigurable spin–orbit torque (SOT) device based on an FeTb/Ru/Co synthetic antiferromagnetic (SAF) heterostructure. By modulating the input current amplitude, the device dynamically switches between two distinct operating modes: saturation and activation. In the saturation regime (>80 mA), deterministic magnetization reversal enables Boolean logic operations (AND, NOR). In the activation regime (<80 mA), gradual, non-volatile conductance modulation emulates synaptic plasticity. Benefiting from the strong antiferromagnetic coupling and near-zero net magnetization of the SAF structure, all operations are achieved without external magnetic fields. This single-device, dual-mode reconfigurable architecture establishes a new paradigm for high-density, low-power, multifunctional in-memory computing units, with promise for advancing adaptive edge computing chips....
The rapid growth and adoption of the Internet of Things (IoT) have led to ecosystems comprising interconnected devices that can generate and exchange vast amounts of data. However, as promising as the intelligent applications enabled by these devices are, they pose significant challenges, particularly in terms of limited computational resources and the need for timely and accurate decision-making. In this context, we propose a Genetic Algorithm (GA)-based optimization algorithm for hyperparameter tuning of Machine Learning (ML) models aligned with the resource constraints and rapid decision-making demands of IoT. The introduced Time-Sensitive Genetic Algorithm (TSGA) jointly optimizes both the predictive accuracy and the execution time of the ML model. Furthermore, we introduce a novel Data Thinning (DT) component, which achieves a considerable reduction in the input data volume without compromising the model’s accuracy. The component is built upon the Largest-Triangle-Three- Buckets LTTB algorithm and an adaptive oine cumulative sum (CUSUM)-based test. We validated TSGA and DT on real IoT data traces, and the results demonstrate that TSGA can determine hyperparameter settings that strategically balance the accuracy metrics of interest with execution time while reducing the data volume burden. Our approach generally outperforms baseline methods in both predictive performance and runtime responsiveness, with only a few exceptions where a slight decrease in accuracy yields substantial gains in execution time, proving its value for resource-constrained, real-world applications....
Industrial systems generate large volumes of multivariate time series data that are complex, dynamic, and affected by noise, where early anomaly detection is critical to ensure operational safety and reliability of the system. Conventional machine learning methods often struggle with the non-linear behaviors, temporal dependencies, and subtle or latent faults that characterize real-world industrial environments. This paper proposes a hybrid anomaly detection framework that integrates Timed Automata to model the dynamic evolution of system behaviors with a Quantum Fidelity-based Fuzzy C-Means clustering algorithm to identify anomalous patterns. The proposed approach is validated on the Skoltech Anomaly Benchmark (SKAB), which simulates realistic industrial scenarios by injecting temporally localized anomalies that are sometimes masked by normal process fluctuations, making them particularly challenging to detect. The experiments provide a comparative analysis with state-of-the-art classical methods. The results highlight the potential of combining symbolic temporal modeling with quantuminspired clustering to enhance anomaly detection, as demonstrated in an example of a complex, dynamic, and noisy industrial environment....
The rapid expansion of specialized hardware architectures has significantly increased the complexity of software optimization. Modern computing systems now incorporate diverse processors, co-processors, and heterogeneous execution environments. Each type of hardware requires specific optimization strategies to fully exploit its computational potential. Therefore, generic compiler optimizations often fail to account for intricate software–hardware interactions, leading to inefficiencies or performance degradation. Moreover, evolving compilation frameworks like LLVM continually introduce new optimizations and modify their optimization managers. This constant evolution makes it challenging to establish standardized optimization strategies. In this work, we first analyze the impact of the two existing pass managers in LLVM on automatic software optimization. Then, we define a novel two-level combinatorial optimization problem that leverages both pass managers for improved runtime performance. We solve this problem using a cooperative co-evolutionary cellular genetic algorithm and conduct extensive experiments to evaluate the impact of the different pass managers on software runtime. Specifically, we assess three optimization strategies, considering the legacy and new pass managers. Results demonstrate that the proposed methodology significantly enhances the runtime efficiency of the considered software, achieving up to 99.41% runtime improvement over the non-optimized program and 98.88% over the best existing optimization approaches....
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