Deploy Load Balancing To Improve Access Speed And High-concurrency Response Capabilities For Malaysian VPSs

1. Overview: Why in Malaysian VPS Deploy load balancing at the upper level

1) User distribution and latency issues in Malaysia: Local VPS can reduce network hops, but a single server can easily become a bottleneck under high concurrency.
2) Concurrency and throughput requirements: During e-commerce promotions or live streaming sales, concurrent connections can surge from a few hundred to tens of thousands in a short time.
3) Load balancing function: It enables request distribution, connection splitting, session persistence, and health checks to improve availability and stability.
4) Cost and scalability: Horizontal scaling (adding application nodes) is more cost-effective than vertical scaling.
5) CDN and edge caching can be combined to reduce the load on the origin server, thereby improving TTFB and page loading speed.
6) Operations objectives: Ensure that P95 latency, requests per second (QPS), and concurrent connections operate stably within budget.

2. Load balancing solutions and technology selection

1) L4 (TCP/UDP) load balancing: Such as LVS and IPVS, suitable for high-concurrency, low-latency scenarios.
2) L7 (HTTP/HTTPS) load balancing: Like HAProxy and Nginx, it supports intelligent routing, rewriting, and HTTPS termination.
3) High availability: Use Keepalived to implement Virtual IP Drift (VRRP), ensuring a second-level switchover in case of a failure of the primary node.
4) Session persistence and stickiness: Creating sticky sessions based on cookies or source IP is suitable for state servers or logged-in sessions.
5) TLS Termination and Acceleration: Terminating TLS at the LB layer can reduce backend CPU load and enable HTTP/2.
6) Leveraging hardware/cloud networking capabilities: 1Gbps or 10Gbps bandwidth, with direct internal networking for synchronization and replication between nodes.

3. Architecture Design and Deployment Steps

1) Define topology: It is recommended to have at least two LBs (primary/replica) + multiple application servers + database primary/replica or read-write separation.
2) Network and Subnet Design: The LB is placed in the public subnet, while the application servers are placed in the private subnet. Firewall policies are used to restrict ports.
3) Deployment process: Install HAProxy/Nginx -> Configure health checks -> Configure Keepalived virtual IP -> Deploy monitoring alerts.
4) Traffic management: Set connection timeout, maximum concurrent connections, backend weights, and retry policies. Example: timeout connect 5s, maxconn 20000.
5) Logging and Monitoring: Enable Prometheus + Grafana to monitor QPS, number of connections, backend response time, and error rate.
6) Rollback and Drills: Conduct traffic grayscale and failover drills before going live to verify VRRP switching and session tolerance.

4. Performance Comparison and Monitoring Metrics (Sample Data)

1) Key indicators: TTFB (ms), P95 response time (ms), maximum concurrent connections, QPS, packet loss rate.
2) Testing methods: Use wrk/hey for stress testing, with distributed load generation at Singapore and Kuala Lumpur nodes.
3) Comparative Explanation: With load balancing + CDN, latency and concurrency capabilities are significantly improved, while the error rate decreases.
4) The table below shows typical comparison data (baseline = single 4vCPU/8GB application server, no LB):

5. DDoS Defense and High Availability Practices

1) CDN and Anycast: Place static resources and some APIs on CDN nodes to reduce bandwidth pressure on the origin server.
2) Forced rate limiting: Configure connection/s IP throttling and request rate limiting rules at LB.
3) Allowlist/Blocklist and WAF: Combine cloud WAF to block common attacks (SQL injection, XSS, abnormal crawlers).
4) Auto-scaling: Automatically scale up application instances during peak times and update the LB backend pool to reduce response time.
5) Network layer protection: Use ISP or cloud provider’s traffic scrubbing services to create black holes or scrub large-scale traffic attacks.
6) Fault simulation: Regularly practice LB failover, database read-only switch, and rollback processes to ensure SLA compliance.

6. Real-world examples and server configuration examples

1) Case Background: An e-commerce company in Kuala Lumpur with 500,000 daily page views; during promotions, the concurrent usage peaks at 12k. It was initially deployed with a single server.
2) Solution: 3 load balancers (HAProxy+Keepalived) are introduced, along with 6 application nodes and 2 master-slave databases. CDN is used to cache static resources.
3) Load balancing configuration example: LB Node Specifications: 2 vCPU / 4GB RAM / 80GB SSD / 1Gbps public network ； Keepalived priority: primary=150, secondary=100.
4) Example of application server specifications: 4 vCPU / 8GB RAM / 160GB NVMe ； Database Master: 8 vCPU / 32GB / 500GB NVMe.
5) Observe the effect: At a peak concurrency of 12k, P95 dropped from 1.2s to 230ms, peak QPS increased from 1,000 to 7,000, with no downtime during any single promotion.
6) Key Ops Points: Set the health check interval to 2s, with a failure threshold of 3 times ； Set backend maxconn=1000, and enable connection pooling and KeepAlive to reduce backend connection overhead.

7. Summary and Recommendations

1) Assess traffic and business characteristics before starting, and choose an L4 or L7 load balancing strategy.
2) Prioritize deploying a two-node LB + Keepalived for high availability, combined with CDN for edge caching.
3) Implement rate limiting and TLS termination at the LB layer to release backend resources.
4) Continue stress testing and improve monitoring and alerting, and develop capacity and scaling strategies.
5) For low-latency requirements within Malaysia, prioritize local or nearby regional nodes and optimize network links.
6) By combining the aforementioned practical configurations and data, access speed and high-concurrency response capabilities can be significantly improved within a controllable cost range.