Multi Server

Multi-server systems are increasingly crucial for handling the computational demands of distributed tasks, particularly in machine learning and network resource allocation. Research focuses on optimizing resource allocation and job scheduling across multiple servers, often employing asynchronous communication and novel algorithms like exponentially weighted methods or MaxWeight with discounted UCB to improve efficiency and robustness. These advancements aim to enhance performance, reduce latency, and increase fault tolerance in applications ranging from federated learning to managing complex network infrastructures. The ultimate goal is to create more efficient and resilient systems capable of handling large-scale, distributed workloads.