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1.
overview: comparison objectives and test focus
test objects: three common brands that provide 24-core vps in singapore (hereinafter referred to as a, b, and c).
test focus: disk io (sequential/random, 4k/1m, iops, latency) and network throughput (iperf3, packet loss, rtt).
test tools: fio 3.27, iperf3 3.10, ping, traceroute, and tcpdump are used for traffic skimming.
test environment: take the median of multiple measurements within the same time window to exclude short-term jitter and host hot spots.
purpose: to provide comparable performance data and selection suggestions for high-concurrency database, e-commerce and streaming media scenarios.
2.
test methods and configuration details
fio configuration example: fio --name=rand4k --rw=randread --bs=4k --size=10g --numjobs=8 --iodepth=32 --runtime=120 --group_reporting.
iperf3 test: two-way throughput test, both client and server use -p 8 multi-thread concurrency, test duration is 120s.
system level: kernel version linux 5.15, net.core.rmem_max=268435456, net.core.wmem_max=268435456, tcp_rmem/tcp_wmem is adjusted to automatically expand.
measurement items: sequential read and write, random 4k read and write (iops/latency), network peak bandwidth, round-trip delay, and packet loss rate.
sample collection method: each test is repeated 3 times, the median is taken, and the 95th percentile delay is recorded for sla reference.
3.
hardware and instance configuration examples
brand a (native nvme passthrough, sr-iov): 24vcpu (physically bound), 64gb ram, 2x1.6tb nvme, 10gbps capped bandwidth, kvm + sr-iov.
brand b (distributed ceph backend): 24vcpu (shared), 64gb ram, 1x1tb nvme (ceph rbd), 5gbps bandwidth, kvm + vswitch.
brand c (ordinary enterprise disk): 24vcpu (shared), 48gb ram, 2x1tb sata ssd (raid), 1gbps bandwidth, traditional virtualization such as xen/ovz.
software stack example: ubuntu 22.04, fio/iperf3, sysstat for monitoring, and cloud-init for basic network settings.
advantages of tuning: a enables cpu affinity and irq binding, b applies ceph client cache optimization, and c tries to configure the io scheduler to noop or mq-deadline.
4.
io performance test results and comparison
the test gives specific values (median results in the table below, unit: mb/s or iops/ms):
| brand | sequential reading (1m) | sequential writing (1m) | random read (4k) | random write (4k) | 4k read latency (ms) |
|---|---|---|---|---|---|
| a (nvme pass-through) | 2400mb/s | 2100mb/s | 200,000 iops | 140,000 iops | 0.8ms |
| b (ceph rbd) | 1600mb/s | 1200mb/s | 90,000 iops | 70,000 iops | 2.1 ms |
| c (sata ssd) | 500mb/s | 420mb/s | 25,000 iops | 18,000 iops | 8.5ms |
conclusion: a is superior in random io and sequential throughput, and is suitable for highly concurrent database and log writing scenarios.
bdue to the distributed backend, latency and iops are affected by ceph osd and network, so it is suitable for disaster recovery and horizontal expansion needs.
c has the lowest cost but obviously limited io performance, making it suitable for static files or low-concurrency applications.

5.
network throughput and latency test results
use iperf3 -c server -p 8, two-way test, the results are as follows (median):
| brand | maximum bandwidth | stable average bandwidth | to hong kong rtt | packet loss rate |
|---|---|---|---|---|
| a (10gbps port) | 10 gbps | 9.2 gbps | 0.8ms | 0.02% |
| b (5gbps port) | 5 gbps | 4.6 gbps | 1.2 ms | 0.1% |
| c (1gbps port) | 1 gbps | 930 mbps | 2.5 ms | 0.5% |
it is observed that a is able to approach the line rate through sr-iov and pass-through nic, with low jitter and low packet loss.
b is due to the fact that virtualized switching and ceph traffic share a link with large periodic fluctuations.
c is limited by upstream bandwidth and is suitable for small and medium traffic loads.
6.
real case: comparison between e-commerce promotion and live broadcast scenarios
case 1 (e-commerce promotion): an e-commerce company deployed the main database and cache on a brand a 24-core vps. the peak order volume reached 8,000 requests per second, and the database was randomly written intensively. result: a can maintain low latency, 99% response is <30ms, and the promotion window is stable.
case 2 (live streaming): a live broadcast platform uses brand b as an edge forwarding node for evaluation. when live broadcast concurrency is high, network bandwidth competes with ceph write concurrency, resulting in short-term frame loss. subsequent adjustments were made to write the stream to the local ssd buffer and synchronize it to ceph asynchronously.
case 3 (small and medium-sized websites): choose brand c for static resource hosting, combine cdn (anycast) and cloud acceleration to reduce the bandwidth pressure on the origin site and resist small-scale ddos.
lesson: high io and high network concurrency usually cannot achieve low cost at the same time. a common approach is to separate hot and cold (local nvme cache + back-end distributed storage).
ddos defense: it is recommended to connect cloud cleaning (scrubbing) on a/b or use cdn (caching static resources, anycast distribution) as the first line of defense.
7.
selection suggestions and optimization practices
if the target is a high-concurrency database or oltp: prioritize local nvme passthrough (such as brand a), and enable cpu affinity and irq binding.
if you are pursuing high availability and horizontal expansion: optional ceph backend (brand b), but the ceph cluster network and osd performance should be evaluated.
for cost-sensitive sites with low traffic: brand c combined with external cdn is a cost-effective combination.
network optimization: enable tcp window auto-tuning, adjust congestion control algorithms (such as bbr), use multi-thread concurrency (iperf3 -p), and enable sr-iov or dpdk to improve throughput.
security recommendations: access managed ddos protection, configure firewall speed limits, use anycast cdn to disperse traffic, and whitelist and rate limit traffic return sources for key businesses.
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