3 results listed
Large Language Models can enhance Kubernetes- based auto-scaling processes by acting as both predictive analysis tools and decision-support mechanisms. In Kubernetes, auto- scaling is typically managed using Horizontal Pod Autoscaler and Vertical Pod Autoscaler, while Cluster Autoscaler handles scaling at the node level. However, traditional metric-based scaling approaches often fall short when dealing with dynamic and unpredictable workloads. This mini-review explores the integration of LLMs into Kubernetes auto-scaling. In predictive scaling, LLMs can analyze historical workload data to forecast demand and optimize scaling decisions. As decision-support tools, they assist in diagnosing system failures, detecting anomalies, and improving key operational factors such as energy efficiency and resource allocation. The review categorizes LLM-enhanced auto- scaling solutions into traditional metric-based approaches, machine learning-driven prediction models, and hybrid systems, addressing key challenges such as latency, scaling sensitivity, and computational costs. By incorporating LLMs into Kubernetes auto-scaling workflows, systems can become more autonomous and intelligent, leading to improved efficiency and resilience in cloud-native environments.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
Canberk Duman
S. Eken
In this study we present a two-step map/reduce framework to stitch satellite mosaic images. The proposed system enable recognition and extraction of objects whose parts falling in separate satellite mosaic images. However this is a time and resource consuming process. The major aim of the study is improving the performance of the image stitching processes by utilizing big data framework. To realize this, we first convert the images into bitmaps (first mapper) and then String formats in the forms of 255s and 0s (second mapper), and finally, find the best possible matching position of the images by a reduce function.
International Workshop on GeoInformation Science
GEOADVANCES
H. Sarı
S. Eken
A. Sayar
In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.
International Workshop on GeoInformation Science
GEOADVANCES
S. Eken
E. Aydın
A. Sayar