Architectural Practice Of Building A High-concurrency Japanese Cs Server Cloud Platform From Scratch

1.

breakdown of architectural goals and requirements

(1) project background: cs game (multiplayer battle) for japanese players, aiming to achieve a stable 20k concurrent connections;
(2) core requirements: low latency (average rtt < 50ms), high availability, anti-ddos, and controllable costs;
(3) scalability: nodes can be expanded horizontally, and the load balancer supports session persistence and forwarding strategies;
(4) monitoring and alarming: tps, number of connections, packet loss, cpu, memory, and network bandwidth need to be reported at the minute level;
(5) maintainability: automated deployment (ansible/terraform), rollback release process and rolling upgrade.

2.

network and host selection strategies

(1) region selection: tokyo computer room (ap-northeast-1 or jp physical computer room) is preferred to reduce player rtt;
(2) instance type: recommended hybrid solution: the control layer uses c5.large (c5.xlarge) cloud host, and the game process is placed in a dedicated vps with independent public network bandwidth;
(3) disk and io: use nvme or ssd (raid1) to ensure that log and playback writing are not blocked;
(4) bandwidth and network card: at least 1gbps dedicated line or 500mbps exclusive bandwidth. for multiple nodes, the peak outbound bandwidth needs to be measured;
(5) operating system: debian 11 / ubuntu 22.04 minimal installation, using kernel network parameter presets.

3.

core server configuration and examples

(1) game logic server (example): 4 cores, 8gb memory, 2 x 500gb nvme, 1gbps bandwidth;
(2) gateway/forwarding layer (example): 8 cores 16gb, bgp anycast ip, 2gbps outbound, configured with lvs + keepalived for layer 4 load balancing;
(3) session/authentication server: 2 cores 4gb, redis cluster (3 nodes), using aof for persistence;
(4) database: master-slave mysql 8.0, master 8vcpu/32gb/1tb ssd, semi-sync enabled;
(5) logging/monitoring: prometheus + grafana, install node_exporter and black box detection on the node.

4.

network tuning and operating system level optimization

(1) kernel parameters: net.core.somaxconn=65535, net.ipv4.tcp_max_syn_backlog=65535;
(2) tcp tuning: enable tcp_tw_reuse, tcp_tw_recycle (pay attention to compatibility) and tcp_fin_timeout=30;
(3) file handle: ulimit -n 200000, system-level fs.file-max=500000;
(4) syn cookies and conntrack: enable tcp_syncookies=1 and adjust the conntrack table size to prevent overflow;
(5) hardware queue: adjust network card interrupt binding (irqbalance or manual binding) and rss to improve multi-core parallel processing.

5.

cdn, dns and ddos defense practice

(1) domain name and anycast dns: use anycast dns to improve resolution stability, and a low ttl value facilitates switching;
(2) cdn strategy: static resources (launchers, patches) go through cdn; real-time game connections are directly connected or through intelligent scheduling nodes;
(3) ddos defense: basic use of cloudflare spectrum/akamai or domestic third-party cleaning (access to cleaning center on demand);
(4) edge protection: enable rate limiting, blacklisting, waf rules and behavioral analysis at the edge;
(5) traffic escape and cleaning: set the bgp traffic absorption policy to direct abnormal traffic to the cleaning pool and keep legitimate connections uninterrupted.

6.

real case: japanese cs gimbal online and performance data

(1) case introduction: an fps manufacturer deployed a ptz in a tokyo computer room, with a target peak concurrency of 20k;
(2) deployment plan: 4 game partition nodes + 2 gateway load balancing + redis3 nodes + mysql master-slave + cdn to distribute static resources;
(3) stress test results: use self-developed stress test tools to simulate player connections and udp package interactions;
(4) optimized data: average rtt 38ms, packet loss rate < 0.2%, and stable support for 22k concurrent connections;
(5) lessons learned: ignoring conntrack in the early stage caused congestion in the middle layer. the problem was solved after adding netfilter space.

7.

configuration and performance comparison table (sample data)

node	cpu	memory	bandwidth	peak concurrency	average rtt
gateway (2 units)	8 vcpus	16 gb	2 gbps	22,000	38 ms
game server (4 units)	4 vcpus	8gb	1 gbps	5,500/unit	35-45 ms
redis (3 units)	4 vcpus	16 gb	500mbps	n/a	<50 ms

8.

post-launch operation and maintenance and continuous optimization suggestions

(1) daily monitoring: build a real-time dashboard, set traffic thresholds and automatic expansion triggers;
(2) disaster recovery drills: regularly conduct ddos fake load & node failover drills;
(3) automation: use ci/cd, blue-green or canary release to reduce version risks;
(4) cost control: expand and shrink capacity on demand and record single traffic costs, and optimize hot and cold data stratification;
(5) iterative optimization: continuously adjust scheduling strategies, network parameters and cleaning rules based on real user data.