AP-Atoms: A High-Accuracy Data-Driven Client Aggregation for Global Load Balancing
Yibo Pi, Sugih Jamin, Peter B. Danzig, Jacob Shaha · IEEE/ACM Transactions on Networking · 2018
In Internet mapping, IP address space is divided into a set of client aggregation units, which are the finest-grained units for global load balancing. Choosing the proper level of aggregation is a complex problem, which determines the number of aggregation units that a mapping system has to maintain and client redirection. In this paper, using Internet-wide measurements provided by a commercial global load balancing service provider, we show that even for the best existing client aggregation, almost 17% of clients have latency more than 50 ms apart from the average latency of clients in the same aggregation unit. To address this, we propose a data-driven client aggregation, AP-atoms, which can trade off scalability for accuracy and adapt for changing network conditions. Since AP-atoms are obtained from the passive measurements of existing traffic between server providers and clients, no extra measurement overheads are incurred. Our experiments show that by using the same scale of client aggregations, AP-atoms can reduce the number of widely dispersed clients by almost $2\times $ and the 98th percentile difference in clients' latencies by almost 100 ms.