Reinforcement Learning for Optimal Load Distribution Sequencing in Resource-Sharing System
Divisible Load Theory (DLT) is a powerful tool for modeling divisible load problems in data-intensive systems. This paper studied an optimal divisible load distribution sequencing problem using a machine learning framework. The problem is to decide the optimal sequence to distribute divisible load to processors in order to achieve minimum finishing time. The scheduling is performed in a resource-sharing system where each physical processor is virtualized to multiple virtual processors. A reinforcement learning method called Multi-armed bandit (MAB) is used for our problem. We first provide a naive solution using the MAB algorithm and then several optimizations are performed. Various numerical tests are conducted. Our algorithm shows an increasing performance during the training progress and the global optimum will be acheived when the sample size is large enough.
Code (0)
등록된 구현이 없습니다.
Tasks
reinforcement-learningReinforcement LearningReinforcement Learning (RL)SchedulingSimilar Papers 제목 키워드 기반
Utilizing Load Shifting for Optimal Compressor Sequencing in Industrial Refrigeration
The ubiquity and energy needs of industrial refrigeration has prompted several research studies investigating various control opportunities for reducing energy demand. This work focuses on one such opportunity, termed co…
Reinforcement Learning Controlled Adaptive PSO for Task Offloading in IIoT Edge Computing
Industrial Internet of Things (IIoT) applications demand efficient task offloading to handle heavy data loads with minimal latency. Mobile Edge Computing (MEC) brings computation closer to devices to reduce latency and s…
Edge-computingManagementReinforcement Learning-Based Adaptive Load Balancing for Dynamic Cloud Environments
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 Computingreinforcement-learningReinforcement LearningReinforcement Learning (RL)Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning
In this paper, we investigate a radio access network (RAN) slicing problem for Internet of vehicles (IoV) services with different quality of service (QoS) requirements, in which multiple logically-isolated slices are con…
Reinforcement Learning (RL)Bayesian Learning with Adaptive Load Allocation Strategies
We study a Bayesian learning dynamics induced by agents who repeatedly allocate loads on a set of resources based on their belief of an unknown parameter that affects the cost distributions of resources. In each step, be…