When starting a Japanese cloud server, the "memory" you see is different from the RAM in a physical machine. The virtualization layer builds an efficient bridge between you and the hardware, but it also makes memory management more complex. Many users find that even with sufficient memory allocated, application performance remains unsatisfactory, often due to memory usage efficiency in a virtualized environment. Optimizing this memory is not as simple as adding RAM to a home computer; it requires understanding how the virtualization layer works and making appropriate adjustments.

One of the core goals of virtualization memory management is improving memory utilization. Japanese cloud service providers typically run multiple virtual machines on physical hosts. Simply allocating a fixed amount of physical memory to each virtual machine can easily lead to idle resources. Therefore, memory overprovisioning has become a common technique. It allows the total physical memory to be less than the sum of the promised memory from all virtual machines, assuming that not all virtual machines will simultaneously use up all available memory. This means that the "guaranteed memory" you purchase may differ from the actual "burst memory" available. Understanding your cloud provider's memory model is crucial—is it fixed allocation, overprovisionable, or does it support bursty performance? Over-reliance on over-provisioned memory can lead to virtual machine memory being swapped to disk under heavy host server load, causing a sharp performance drop. Monitoring actual memory usage within the virtual machine, swap partition activity, and whether the host machine's "memory balloon" driver is installed and running are direct ways to determine if you are being affected by over-provisioning.

Within the virtual machine, adjusting the operating system kernel's memory parameters can significantly improve performance. A key parameter is Transparent Pages (TPS). Standard Linux memory management uses 4KB small pages, and when managing large amounts of memory, Translation Bypass Buffer (TLB) misses incur overhead. THP attempts to automatically merge small pages into 2MB large pages, reducing TLB pressure and thus improving the performance of memory-intensive applications. You can check and adjust the THP status using the following command:

# Check the current THP status

cat /sys/kernel/mm/transparent_hugepage/enabled

# The output is usually: [always] madvise never

# 'always' means use it whenever possible, 'madvise' means use it as recommended, and 'never' means disable it.

# For applications known to benefit, such as databases, you can try setting it to "madvise".

echo madvise | sudo tee /sys/kernel/mm/transparent_hugepage/enabled

However, note that THP is not a panacea. Under certain loads, it may cause memory fragmentation or brief latency spikes, requiring testing.

Another powerful feature provided by the virtualization layer is kernel page merging. This technique allows the host machine to scan memory pages used by multiple virtual machines. If it finds memory pages with identical content (for example, multiple virtual machines running the same operating system or loading the same library files), KSM merges them into a read-only shared page and performs copy-on-write when needed. This greatly improves memory density. Inside a virtual machine, you typically cannot directly control KSM, but it explains why observed memory usage within the virtual machine may differ from statistics at the host level. Ensuring your virtual machine kernel supports and enables the relevant features will allow KSM to function better.

For modern multi-CPU Japanese cloud servers, the impact of NUMA architecture on memory performance is significant. In a NUMA architecture, processors access local memory nodes much faster than remote nodes. In a virtualization environment, if a virtual machine's vCPUs are scheduled to different physical NUMA nodes, but memory is primarily allocated on one node, it leads to a large number of remote memory accesses, significantly increasing latency. Optimizing NUMA strategies first requires understanding the virtual machine's topology:

# After installing the numactl tool, view the NUMA topology

numactl --hardware

# View the NUMA memory allocation of a process or application

numastat -c your_application_name

When configuring virtual machines, if the cloud platform allows, try to constrain the virtual machine's vCPUs and memory allocation within the same NUMA node. For latency-sensitive applications such as databases and high-performance computing, setting a NUMA policy in system startup parameters (e.g., `numa=on`) or using the `numactl` command to start processes can force local memory allocation.

Memory monitoring and tuning rely heavily on specific data. You need to focus on several key metrics: First, available memory, not just remaining memory. Linux uses free memory as a cache and buffer, so a high "used memory" doesn't necessarily indicate a problem. When using the `free -h` command, focusing on the "available" column is more accurate. Second, swapping activity. Use the `vmstat 1` command to view the `si` (swap-in) and `so` (swap-out) columns. Any continuous swapping activity indicates insufficient physical memory and will severely impact performance. Finally, memory pressure. The kernel provides memory pressure information that can warn of memory shortages. You can view the process's `PSI` metric using the `ps` command or observe it using dedicated monitoring tools.

Based on monitoring data, dynamic tuning can be performed. For example, adjusting virtual memory parameters. File system caching and dirty page write-back strategies impact memory usage and I/O performance. The following parameters are worth noting:

# Reduce the proportion of memory occupied and the time spent on dirty pages (data awaiting write to disk), reducing burst I/O latency

echo 10 | sudo tee /proc/sys/vm/dirty_ratio

echo 5 | sudo tee /proc/sys/vm/dirty_background_ratio

echo 500 | sudo tee /proc/sys/vm/dirty_expire_centisecs

Furthermore, adjusting the aggressiveness of memory reclamation, i.e., the `vm.swappiness` parameter (value range 0-100), can control the system's tendency to use swap space under memory pressure. For database servers, setting it to a lower value (e.g., 10) can prioritize reducing file caching rather than triggering swapping.

The ultimate goal of memory optimization is to serve applications. Therefore, application-layer memory management is equally critical. Ensure your applications (such as Java, Go, Nginx, and MySQL) have appropriate memory limits and garbage collection strategies. For example, set a clear heap size for the JVM to prevent excessive memory consumption within the container, which could lead to OutOfMemoryError (OOM); configure an appropriate size for MySQL's `innodb_buffer_pool_size` to fully utilize memory for data caching.

From the fundamental principles of virtualization, through coordinated adjustments between the host and guest operating systems, and finally to the appropriate configuration of the application itself, memory optimization is a series of interconnected processes. There is no single, unchanging optimal solution; it requires continuous observation, hypothesis building, testing, and adjustment based on the actual workload.