How To Optimize The Bandwidth And Disk IO Performance Of Alibaba Cloud Malaysia Lightweight Servers

Question 1: How to accurately monitor the bandwidth and disk IO performance of Alibaba Cloud Malaysia Lightweight Server?

To optimize, you must first be able to observe. It is recommended to use Alibaba Cloud's own CloudMonitor to add instance monitoring items to the console, paying attention to network card incoming/outgoing bandwidth, bandwidth utilization, packet loss rate and delay; as well as disk , throughput (MB/s), average waiting time (avgqu-sz, await/await_ms) and utilization (%util).

Use commands to assist positioning within the instance:

• iostat -x 1 5: View iops, svctm, %util of each device;

• sar -n DEV 1 5 or ifstat/vnstat: View network traffic trends;

• ioping: Test disk latency;

Set alarm thresholds (such as bandwidth utilization continuously >80%, disk avg wait >10ms, or %util >70%), and analyze peak periods based on historical curves.

Question 2: How to directly improve bandwidth performance through instance specifications and network solutions?

Lightweight application servers have fixed bandwidth and billing plans. Optimization methods include: upgrading bandwidth packages or higher specification instances, purchasing independent bandwidth packages or on-demand elastic public IP bandwidth. If accessing from overseas, priority is given to selecting Malaysian nodes and enabling global acceleration or dedicated line acceleration.

At the architecture level, load balancing (SLB) can be used to distribute traffic and CDN can be used to cache static resources to reduce the bandwidth of the origin site; for large file transfers, breakpoint resumption and fragmented upload can be used to reduce single connection usage.

Question 3: How should the disk type and file system level be selected to optimize disk IO performance?

Prefer SSD or high-efficiency cloud disks. If the lightweight server supports elastic expansion of multiple disks or cloud disks, you should choose a disk type with higher IOPS. It is recommended to use XFS or ext4 as the file system and use the appropriate inode and readonly options when using mkfs.

Add noatime and nodiratime to the mount options to reduce unnecessary writes; enable discard (or scheduled fstrim) for SSDs; for database-type loads, consider enabling writeback cache or using a local cache layer (such as Redis) to reduce disk writes.

Question 4: What sysctl and scheduler adjustments can improve bandwidth and IO performance at the kernel and network stack levels?

Network: Adjust net.core.rmem_max/wmem_max, net.ipv4.tcp_rmem/tcp_wmem to increase the buffer, enable tcp_window_scaling, increase net.core.somaxconn and tcp_max_syn_backlog to cope with concurrent connections; use ethtool to adjust the GRO/GSO and RX/TX buffer pools to reduce CPU overhead.

Disk aspect: Change the IO scheduler to noop or deadline (especially on virtualization/SSD), adjust the block device queue depth (queue_depth or nr_requests), and control the writeback behavior through vm.dirty_ratio and vm.dirty_background_ratio to avoid spikes in latency caused by short-term high writes.

Question 5: How to reduce the pressure on bandwidth and disk IO at the application and architecture levels?

The focus of application layer optimization is to reduce unnecessary data transmission and disk reading and writing: enable HTTP compression (gzip/brotli), merge and streamline static resources, use long cache headers with CDN, lazy loading of images, and compression of WebP and other formats. Migrate large files and static assets to object storage (OSS) or dedicated storage to reduce host IO.

In terms of database and logs: use read-write separation, caching (Redis/Memcached), paging and index optimization to reduce full table scans; log asynchronous writing or use centralized log services to avoid a large number of synchronous disk writes blocking applications.

In addition, implement current limiting, circuit breaker and queue (such as message queue) strategies to smooth instantaneous peak traffic, and reduce burst IO and network pressure through grayscale/batch release.