Towards Dynamic Request Updating With Elastic Scheduling for Multi-Tenant Cloud-Based Data Center Network

Serving the ever-growing demand for computation, storage, and networking resources for multi-tenant in cloud computing is an important mission of Data Center Networks (DCNs). In this paper, we study the dynamic request updating problem, and our objective is to maximize the elasticity of cloud-based DCNs while achieving rapid response to multi-tenants. We use virtual clusters under the hose communication model to denote requests. Instead of using heuristic algorithms as the existing work does, this paper introduces a novel two-stage dynamic request updating framework with elastic resource scheduling strategy. In the first stage, we propose a multi-tenant fast initial provisioning scheme to realize the real-time response and analyze its optimality and complexity. Additionally, we provide a deep reinforcement learning-based dynamic updating strategy to enhance the elasticity of virtual clusters that are being used or scaling during the second stage. We train a fully connected neural network by creating a new feasible action set to realize the reduction, and it approximates the policy based on a proposed aggressive objective selection method to improve training speed while avoiding high dimensions caused by large scales of tenants and DCNs. Extensive evaluations demonstrate that our scheme outperforms baselines in terms of both elasticity and efficiency.