Dynamic load balancing for cloud systems under heterogeneous setup delays

2025-05-06 · Fernando Paganini, Diego Goldsztajn

We consider a distributed cloud service deployed at a set of distinct server pools. Arriving jobs are classified into heterogeneous types, in accordance with their setup times which are differentiated at each of the pools. A dispatcher for each job type controls the balance of load between pools, based on decentralized feedback. The system of rates and queues is modeled by a fluid differential equation system, and analyzed via convex optimization. A first, myopic policy is proposed, based on task delay-to-service. Under a simplified dynamic fluid queue model, we prove global convergence to an equilibrium point which minimizes the mean setup time; however queueing delays are incurred with this method. A second proposal is then developed based on proximal optimization, which explicitly models the setup queue and is proved to reach an optimal equilibrium, devoid of queueing delay. Results are demonstrated through a simulation example.

📄 PDF Abstract BibTeX arXiv:2505.03596

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

Golden Queue Managers 설명 없음

SET Dynamic Sparse Training method where weight mask is updated randomly periodically

Reinforcement Learning-Based Adaptive Load Balancing for Dynamic Cloud Environments

2024-09-07 · Kavish Chawla

Efficient load balancing is crucial in cloud computing environments to ensure optimal resource utilization, minimize response times, and prevent server overload. Traditional load balancing algorithms, such as round-robin…

Cloud Computing reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Scalability Optimization in Cloud-Based AI Inference Services: Strategies for Real-Time Load Balancing and Automated Scaling

2025-04-16 · Yihong Jin, Ze Yang

The rapid expansion of AI inference services in the cloud necessitates a robust scalability solution to manage dynamic workloads and maintain high performance. This study proposes a comprehensive scalability optimization…

Decision Making Demand Forecasting

Management Meta Reinforcement Learning reinforcement-learning Reinforcement Learning

Dynamic load balancing for cloud systems under heterogeneous setup delays

Code (0)

Methods 이 논문이 사용한 방법론

Similar Papers 제목 키워드 기반

Reinforcement Learning-Based Adaptive Load Balancing for Dynamic Cloud Environments

Scalability Optimization in Cloud-Based AI Inference Services: Strategies for Real-Time Load Balancing and Automated Scaling

Learning Mean-Field Control for Delayed Information Load Balancing in Large Queuing Systems

Intelligent Load Balancing in Cloud Computer Systems

Meta-Reinforcement Learning with Discrete World Models for Adaptive Load Balancing