At present, public clouds have served millions of tenants. To provide reliable services, cloud vendors need to perceive health status of the cloud network by building a telemetry system to detect possible network failures. While telemetry systems for physical networks have been extensively studied, research on telemetry systems for virtual networks is still insufficient. Different from physical networks, we conclude that building a virtual network telemetry system faces new challenges of feasibility, efficiency, and effectiveness. Specifically, we need to 1) protect privacy of tenants and adapt to heterogeneous middleboxes at the data plane; 2) handle frequent virtual network topology updates and compress large-scale measurement paths for millions of tenants at the control plane; 3) analyze telemetry results to locate network failures at the analysis plane. To address these challenges, we present Zoonet, a proactive virtual network telemetry system for multi-tenant clouds. At the data plane, Zoonet uses host agent and arp-ping to protect tenants’ privacy and defines an elegant generalization of ping and traceroute, which can work on heterogeneous middleboxes. At the control plane, Zoonet conducts update batch processing and substantial probing path pruning to lessen the overhead. At the analysis plane, Zoonet reduces noises and aggregates alerts based on temporal and spatial correlation and conducts the hop-by-hop telemetry mode to locate failures. Zoonet has been deployed in Alibaba Cloud for over two years, covering tens of cloud regions, hundreds of thousands of servers. We become increasingly reliant on Zoonet as it reduces 86% of the personnel engaged in troubleshooting.

In the massive Multiple-Input Multiple-Output (MIMO) system, a large number of transmit antennas are deployed at base stations. The massive MIMO has been proposed as one of the key technologies for the next generation of wireless communication systems. A major problem in practice to realize the capacity improvement promised by the massive MIMO technology is the availability of the channel state information in downlink at the base station. In frequency division duplexing (FDD) massive MIMO systems, the overhead of the training sequences could overwhelm the precious downlink resources due to the large number of transmit antennas. Therefore, a training sequences design approach that exploits the channel spatial correlation is proposed to reduce the overhead of training sequences. The optimal training sequence structure is analyzed and the length of optimal training sequence which could maximize the achievable data rate is evaluated by the Monte Carlo simulations in this paper. It is shown that the overhead is significantly reduced and a trade-off between accuracy of channel estimation and the system achievable data rate is achieved simultaneously by the proposed training sequences design method.