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.

I. Introduction

With the ever-increasing demand of cloud services, the data center network (DCN) has become an efficient and promising data processing infrastructure for cloud computing. As reported in the public data of Microsoft Azure [1], the demand and deployment size of the tenant is very bursty and unpredictable in terms of memory, cores, and bandwidth. One fundamental challenge of DCNs is to serve the varying needs of multi-tenant without requiring frequent provisioning changes. This paper proposes an elastic resource provisioning scheme to deal with the scaling without load redistribution during a run time. In order to simplify the description of the resource provisioning problem, we use virtual clusters to denote the requests of multi-tenant, and each virtual cluster is an abstraction of a set of virtual machines (VMs), which has the requirements on both computing and communication resources [3]. We use the notion of elasticity to measure the potential growth of multi-tenant in terms of computing and communication resources at the same time, which is defined as the degree of a system that is able to adapt to the workload changes by provisioning and releasing resources in an autonomic manner [5], [6], [8]. We consider the virtual clusters with hose model under constraints, and our objective is to maximize the elasticity while achieving rapid response to multi-tenants in the DCNs.