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Related Concept Videos

Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Introduction to Learning01:18

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
Scale-Up Processes01:14

Scale-Up Processes

The scale-up of microbial fermentation processes is essential in industrial biotechnology, allowing the transition from laboratory-scale experiments to commercial-scale production while aiming to maintain product yield and quality. This process requires meticulous adjustment of equipment design, process parameters, and contamination control strategies to accommodate increasing culture volumes.At the laboratory scale, cultures are typically maintained in 1 to 10-liter glass or autoclavable...

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Related Experiment Videos

Learning-based orchestration for low-latency AI deployment in hybrid cloud-edge platforms.

Ahmed Albugmi1

  • 1Computer and Information Technology Department, Applied College, King Abdulaziz University, 80213, Jeddah, Saudi Arabia. analbogome@kau.edu.sa.

Scientific Reports
|June 20, 2026
PubMed
Summary

This study introduces a deep learning scheduler for AI model deployment in hybrid cloud-edge systems. It optimizes performance and resource use, achieving over 90% success rates for new AI models.

Keywords:
Adaptive orchestrationCloud–edge computingDeep learning deploymentDistributed AIInference optimizationMLPerf benchmarkModel placementResource-aware scheduling

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Cloud Computing
  • Edge Computing

Background:

  • AI applications in hybrid cloud-edge environments face challenges with latency, throughput, and resource utilization.
  • Traditional deployment models lack the flexibility for dynamic workload and hardware changes.

Purpose of the Study:

  • To introduce and analyze a resource-conscious, deep learning-based scheduling system for AI model deployment on distributed cloud edges.
  • To improve inference performance and maintain quality of service (QoS) compliance.

Main Methods:

  • Developed a scheduling framework using a fully connected neural network trained on MLPerf Inference Benchmark features.
  • Utilized real-time system telemetry and model features from hybrid infrastructure (NVIDIA GPUs, Jetson Xavier).
  • Evaluated four MLPerf workloads (ResNet 50, BERT, SSD ResNet34, DLRM) across various batch sizes and latency thresholds.

Main Results:

  • Achieved deployment success rates >90% for unseen models (GPT 2, YOLOv5) in generalization experiments.
  • Demonstrated significant latency reduction and throughput gains.
  • Validated the effectiveness of learning-based orchestration for adaptive AI service deployment.

Conclusions:

  • Learning-based orchestration provides adaptive, resource-aware solutions for low-latency AI deployment in hybrid cloud-edge systems.
  • The solution's effectiveness hinges on profiling data representativeness and environment similarity between training and deployment.