Pattern matching improves online resource allocation in cloud networks
Pattern-Aware Virtual Network Embedding Optimization for Cloud Data Centers
Networking and Internet Architecture
Summary
Allocating resources for virtual networks in cloud data centers is tricky because many requests come in all the time and use resources differently. Existing methods don't fully take advantage of how these requests complement one another, causing resource waste. The authors propose a method that groups similar virtual network requests and matches patterns to use resources more efficiently. Their approach runs quickly in real time and performs almost as well as offline methods.
What this means in practice
- •For cloud network operators: Improve real-time allocation of virtual network resources in cloud data centers to increase workload acceptance by 25%-30%.
- •For data center infrastructure teams: Use pattern-based matching rules to reduce fragmentation and better utilize existing network resources during virtual network embedding.
Authors
Binquan Guo, Zhou Zhang, Junfeng Zhai, Zheng Zhang, Marie Siew, Zehui Xiong
Abstract
The network virtualization (NV) technology has enabled the sharing of multiple resources among virtual networks (VNs) in cloud data centers. One of the key challenges is to allocate resources in real-time for virtual network request (VNR), which is known as online virtual network embedding (VNE). However, the existing online VNE methods do not exploit the multi-dimensional complementary relationship among diverse VNRs, resulting in the fragmentation and waste of substrate resources. In this paper, we propose the pattern matching based online VNE approach by constructing appropriate matching rules among observed patterns to maximize resources utilization. We devise the clustering based VNRs quantization method and conduct rigorous study on the pattern combination filtering problem. Then, we utilize the column generation to solve it and construct the pattern matching rules. Based on the rules, we propose an online pattern matching VNE algorithm with linear worst-case complexity. Evaluation on a 106-server testbed using Alibaba production cluster trace dataset shows that our algorithm achieves close-to-offline performance and more accepted workloads that outperforms traditional designs by 25%-30%.