Browsing by Autor "Satoru Izumi"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Item type: Item ,
    Energy‐Aware Controller Placement With Delay Guarantees for In‐Band SDN
    (Wiley, 2026) T. Kondo; Luis Andrés Guillén; Satoru Izumi; Toru Abe; Takuo Suganuma
    ABSTRACT Software‐defined networking (SDN) enables flexible, programmable networks, but in large deployments, poor controller placement can raise latency and energy use. This paper presents a practical in‐band SDN model and an optimization method that jointly accounts for control‐plane delay and device power consumption to place controllers and configure switches. We formulate the problem as a binary integer programming (BIP) that decides controller locations, which switches remain active, and port bit‐rates and routes. Experiments on a large WAN topology (Janos‐US) show the approach can cut total network energy by up to 15% while keeping control‐plane delays within required bounds, offering network operators a straightforward way to trade responsiveness for energy savings.
  • Thumbnail Image
    Item type: Item ,
    WISEST: Weighted Interpolation for Synthetic Enhancement Using SMOTE with Thresholds
    (Multidisciplinary Digital Publishing Institute, 2025) Ryotaro Matsui; Luis Guillen; Satoru Izumi; Takuo Suganuma
    Imbalanced learning occurs when rare but critical events are missed because classifiers are trained primarily on majority-class samples. This paper introduces WISEST, a locality-aware weighted-interpolation algorithm that generates synthetic minority samples within a controlled threshold near class boundaries. Benchmarked on more than a hundred real-world imbalanced datasets, such as KEEL, with different imbalance ratios, noise levels, geometries, and other security and IoT sets (IoT-23 and BoT-IoT), WISEST consistently improved minority detection in at least one of the metrics on about half of those datasets, achieving up to a 25% relative recall increase and up to an 18% increase in F1 compared to the original training and other approaches. However, in most cases, WISEST's trade-off gains are in accuracy and precision depending on the dataset and classifier. These results indicate that WISEST is a practical and robust option when minority support and borderline structure permit safe synthesis, although no single sampler uniformly outperforms others across all datasets.