🤖 AI Summary
This study addresses the challenges of cross-cloud data transfer, including traffic volatility, high fixed leasing costs, and multi-day provisioning delays, which collectively hinder cost optimization. The work presents the first systematic empirical investigation of Cross-Cloud Interconnect (CCI) services and introduces ToggleCCI, a dynamic cost optimization algorithm that intelligently switches between VPN and dedicated interconnects to cope with demand uncertainty and provisioning latency. ToggleCCI integrates sliding-window cost analysis with an online decision-making mechanism and offers theoretical guarantees of asymptotic optimality. Experiments based on real-world traffic traces between AWS and GCP demonstrate that ToggleCCI consistently approaches the performance of the optimal static strategy across diverse scenarios while significantly reducing data transfer costs.
📝 Abstract
New services such as Google Cross-Cloud Interconnect (CCI) address the rise in fast and large-scale cross-cloud data transfers. CCI offers dedicated high-throughput links with low per-GB transfer costs, but also involves high fixed leasing fees and multi-day provisioning delays. This combination makes cost optimization difficult because traffic patterns are unpredictable.
This paper presents the first comprehensive study of CCI-like services. We begin with an empirical characterization of CCI and its alternatives using direct measurements across AWS-GCP interconnects. We then introduce ToggleCCI, a new dynamic cost-optimization algorithm designed to handle provisioning delays and uncertainty in future demand. ToggleCCI adapts by switching between VPN and CCI based on cost trends observed over a sliding time window. We prove that ToggleCCI achieves asymptotic optimality under sustained high-demand or low-demand regimes. Finally, using real-world traffic traces, we show that ToggleCCI consistently tracks the best static policy for each scenario and delivers substantial cost savings.