Network throughput for Japanese servers is a key indicator affecting overall performance. In actual operations, the default Linux system configuration often fails to fully utilize the bandwidth and hardware performance potential. Therefore, it's necessary to develop a reasonable throughput optimization plan based on real-world data to ensure the system remains stable and efficient under high load.

The first step in optimization is benchmarking to identify the server's current throughput bottleneck. Common testing tools include iperf3 and netperf, which can simulate high-concurrency traffic and monitor bandwidth utilization. For example, to test the network performance between the Japanese server and the client using the iperf3 command:

iperf3 -s
iperf3 -c server_ip -P 10 -t 60

The -P parameter indicates the number of concurrent connections, and the -t parameter indicates the duration. This test provides information on average bandwidth, jitter, and packet loss. These metrics determine whether kernel parameters and the network stack need optimization.

After confirming the throughput bottleneck, the first area to optimize is the TCP/IP protocol stack. The Linux kernel's default buffer size is small, which cannot efficiently utilize network resources for high-bandwidth, long-distance transmission. Therefore, you need to adjust the relevant parameters via sysctl. For example:

net.core.rmem_max = 134217728
net.core.wmem_max = 134217728
net.ipv4.tcp_rmem = 4096 87380 134217728
net.ipv4.tcp_wmem = 4096 65536 134217728
net.ipv4.tcp_congestion_control = bbr
net.ipv4.tcp_mtu_probing = 1

These settings increase the TCP buffer size and enable the BBR congestion control algorithm, improving throughput over long-distance networks. Optimizing the TCP window size is particularly critical for Japanese servers connecting to Europe, the US, or Southeast Asia.

In addition to optimizing the network stack, tuning network interface card (NIC) parameters is also crucial. Modern server NICs support multiple queues and interrupt bundling. Adjusting these settings can effectively reduce CPU load and increase throughput. For example, you can use the ethtool command to view and adjust parameters:

ethtool -K eth0 gro off
ethtool -K eth0 gso off
ethtool -K eth0 tso off

Disabling some sharding and merging operations can reduce CPU processing pressure under high concurrency, thereby improving overall performance. Furthermore, using irqbalancing or manually binding interrupts to multiple CPU cores can further enable multi-core parallel processing.

For high-concurrency scenarios, the kernel connection tracking table (conntrack) size also needs to be adjusted. The default value is prone to overflow when handling large numbers of concurrent connections, resulting in packet loss and connection aborts. This can be optimized using the following configuration:

net.netfilter.nf_conntrack_max = 262144
net.netfilter.nf_conntrack_tcp_timeout_established = 600

This adjustment can significantly improve the server's ability to handle concurrent connections.

In addition to kernel and network card parameters, optimizing the file system and I/O throughput also indirectly affects network throughput performance. Inadequate I/O performance can slow down overall response times, especially in businesses that require extensive log writing and cache updates. It's recommended to use the XFS or EXT4 file system, combined with SSD storage, to improve I/O efficiency. Adjusting the I/O scheduling algorithm can also yield optimization results, for example:

echo deadline > /sys/block/sda/queue/scheduler

At the application level, optimizing the configuration of web servers or database services is equally important. For example, when handling high-concurrency requests in Nginx, throughput can be improved by adjusting the number of workers and connection limits:

worker_processes auto;
worker_connections 65535;
multi_accept on;

Enabling both the sendfile and tcp_nopush parameters can improve the transmission efficiency of static resources, significantly improving the access experience for cross-border e-commerce websites.

After adjusting the parameters, verify the optimization results through field testing. Use iperf3 for comparison testing again to observe the throughput improvement. In addition, using tools like iftop and nload to monitor bandwidth usage in real time, and tools like sar and dstat to monitor CPU and memory usage, can help identify new system bottlenecks.

For ongoing optimization, you can also deploy an automated monitoring system, such as a Prometheus + Grafana architecture, to collect real-time data on network throughput, latency, packet loss, and system resource usage, and analyze it using visual charts. This allows you to proactively identify potential issues during peak business periods and prevent sudden traffic spikes from causing system performance degradation.

Further optimization methods include load balancing and distributed architecture. If a single Japanese server still cannot meet business requirements after throughput optimization, multi-node load balancing can be achieved using LVS, HAProxy, or Nginx reverse proxy to improve overall throughput. In cross-border e-commerce and video acceleration scenarios, combining CDN with Anycast routing can also reduce latency and improve throughput stability.

Overall, optimizing Linux throughput for Japanese servers is a systematic project, from the underlying kernel to application configuration. Parameters must be continuously adjusted through field testing to meet specific business needs. The core optimization strategy is to first identify bottlenecks through benchmarking, then improve overall performance through TCP/IP stack adjustments, network card parameter optimization, I/O scheduling, and application configuration, and ensure continuous performance through monitoring and load balancing. This ensures that Japanese servers can deliver optimal performance in cross-border, high-concurrency, and high-traffic scenarios, meeting the needs of e-commerce, live streaming, and foreign trade users.