Workload Engineering: Optimising WAN and DC Resources Through RL-Based Workload Placement

TL;DR

This paper introduces a workload placement architecture that uses reinforcement learning to optimize resource use across data centers and wide area networks, improving efficiency by 5-8%.

Contribution

It proposes a novel centralized controller architecture that is aware of both network and data center resources for workload placement.

Findings

Reinforcement learning-based placement outperforms heuristics in resource utilization.

The approach increases workload placement efficiency by approximately 5-8%.

Simulation results demonstrate improved resource management in data centers and networks.

Abstract

With the rise in data centre virtualization, there are increasing choices as to where to place workloads, be it in web applications, Enterprise IT or in Network Function Virtualisation. Workload placement approaches available today primarily focus on optimising the use of data centre resources. Given the significant forecasts for network traffic growth to/from data centres, effective management of both data centre resources and of the wide area networks resources that provide access to those data centres will become increasingly important. In this paper, we present an architecture for workload placement, which uniquely employs a logically centralised controller that is both network and data centre aware, which aims to place workloads to optimise the use of both data centre and wide area network resources. We call this approach workload engineering. We present the results of a simulation study, where we use a reinforcement-learning based placement algorithm on the controller. Results of the study show this algorithm was able to place workloads to make more efficient use of network and data centre resources and placed ~5-8% more workloads than other heuristic placement algorithms considered, for the same installed capacity.