Multi-resource Energy-efficient Routing in Cloud Data Centers with Networks-as-a-Service

TL;DR

This paper addresses energy-efficient routing in cloud data centers under the NaaS model, extending optimization to multi-resource scenarios and proposing practical greedy and heuristic algorithms with significant energy savings.

Contribution

It introduces a multi-resource energy-efficient routing framework for NaaS data centers and develops scalable algorithms leveraging network structure for practical deployment.

Findings

Greedy routing scheme achieves significant energy savings.

Topology-aware heuristic reduces computation time substantially.

Proposed methods outperform baseline approaches in simulations.

Abstract

With the rapid development of software defined networking and network function virtualization, researchers have proposed a new cloud networking model called Network-as-a-Service (NaaS) which enables both in-network packet processing and application-specific network control. In this paper, we revisit the problem of achieving network energy efficiency in data centers and identify some new optimization challenges under the NaaS model. Particularly, we extend the energy-efficient routing optimization from single-resource to multi-resource settings. We characterize the problem through a detailed model and provide a formal problem definition. Due to the high complexity of direct solutions, we propose a greedy routing scheme to approximate the optimum, where flows are selected progressively to exhaust residual capacities of active nodes, and routing paths are assigned based on the distributions of both node residual capacities and flow demands. By leveraging the structural regularity of data center networks, we also provide a fast topology-aware heuristic method based on hierarchically solving a series of vector bin packing instances. Our simulations show that the proposed routing scheme can achieve significant gain on energy savings and the topology-aware heuristic can produce comparably good results while reducing the computation time to a large extent.