Resource Allocation
Resource allocation research focuses on optimizing the distribution of limited resources—computational power, bandwidth, energy—to maximize efficiency and fairness across diverse applications. Current research emphasizes developing sophisticated algorithms, including deep reinforcement learning, graph neural networks, and dynamic programming, to address complex, real-time resource allocation problems in areas like federated learning, edge computing, and wireless networks. These advancements are crucial for improving the performance and scalability of various systems, from mobile communications to large-scale AI training, and for ensuring equitable access to resources.
Papers
Active Learning for Fair and Stable Online Allocations
Riddhiman Bhattacharya, Thanh Nguyen, Will Wei Sun, Mohit Tawarmalani
Resource Allocation with Karma Mechanisms
Kevin Riehl, Anastasios Kouvelas, Michail Makridis
Online Learning of Weakly Coupled MDP Policies for Load Balancing and Auto Scaling
S. R. Eshwar, Lucas Lopes Felipe, Alexandre Reiffers-Masson, Daniel Sadoc Menasché, Gugan Thoppe
Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey
Feng Liang, Zhen Zhang, Haifeng Lu, Chengming Li, Victor C. M. Leung, Yanyi Guo, Xiping Hu
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation
Jingwen Tong, Xinran Li, Liqun Fu, Jun Zhang, Khaled B. Letaief
DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach
Zhang Liu, Hongyang Du, Junzhe Lin, Zhibin Gao, Lianfen Huang, Seyyedali Hosseinalipour, Dusit Niyato
ElasticRec: A Microservice-based Model Serving Architecture Enabling Elastic Resource Scaling for Recommendation Models
Yujeong Choi, Jiin Kim, Minsoo Rhu