Dynamic expansion technology is one of the core capabilities of cloud computing platforms. This feature is particularly useful for scenarios where traffic varies greatly at different time periods, such as the peak and off-peak periods as well as the daily traffic differences in the e-commerce industry. Dynamic expansion can precisely meet the real-time demands of e-commerce business loads. The essence of dynamic expansion is to achieve the elastic scaling of computing resources through automated means to meet the real-time changing demands of business loads. This technology integrates modules such as resource monitoring, policy engines, and orchestration and scheduling. A detailed summary is provided below!
What is the technical architecture of dynamic expansion? It is mainly composed of four core components: the monitoring and collection layer, the strategy decision-making layer, the asset scheduling layer, and the traffic distribution layer. The monitoring and acquisition layer is an agent program deployed on the cloud server, which collects indicators such as CPU usage rate, memory usage, and network throughput in real time. The sampling frequency is generally once every 5 seconds. Meanwhile, the characteristics of network traffic are analyzed through NetFlow to identify burst traffic patterns.
The strategy decision-making layer generates expansion instructions based on preset rules and machine learning models. Common strategies include: Threshold triggering (when the indicator continuously exceeds the threshold, such as CPU > 80% for 3 minutes, the expansion process is initiated), time prediction (predict periodic peaks based on historical data (such as e-commerce promotions), and expand 30 minutes in advance), cost optimization (under the premise of ensuring SLA, select the instance specification with the highest cost performance, such as burst performance instances). A certain video platform adopts the reinforcement learning algorithm to dynamically adjust the expansion threshold, reducing the resource waste rate from 23% to 7%, while ensuring the QoS compliance rate of 99.95%.
The resource scheduling layer scheduler allocates computing nodes from the resource pool based on policy instructions. Key technologies include: rapid startup of virtual machines, and through pre-set image caching and memory hot migration technology, the instance startup time is compressed from the minute level to the second level. Containerized deployment of Kubernetes clusters in conjunction with Cluster Autoscaler automatically creates new nodes and joins the cluster when Node resources are insufficient. The serverless extended FaaS platform automatically instantiates functions based on the number of concurrent requests to achieve millisecond-level elasticity.
The traffic distribution layer is where, after the expansion is completed, the load balancer (such as Nginx, ALB) adds new nodes to the service pool. Requests are allocated by using the weighted polling algorithm, and abnormal nodes are eliminated through the health check mechanism. A certain financial system gradually increases the traffic allocation during the preheating period of new instances from 0% to 100% by dynamically adjusting the weights, avoiding performance fluctuations caused by cold starts.
The core implementation principle of dynamic scaling can be explained from horizontal scaling (Scaleout) and vertical scaling.
Horizontal scaling distributes the load by increasing the number of instances and is suitable for stateless services: When creating a new instance, a standardized image is loaded from object storage (such as S3), the configuration management system (Ansible) injects environment variables and keys, the service is registered to Consul or Eureka for recognition by the service discovery component, and the load balancer updates the backend node list to complete the traffic switching.
Vertical scaling (Scaleup) enhances performance by adjusting the specifications of individual instances and is suitable for stateful services such as databases: online adjustment of CPU/ memory configuration, use of hot migration technology (VMware vMotion) to avoid service disruptions, and keep the storage system mounted to ensure data consistency.
Dynamic adjustment of kernel parameters (such as TCP buffer size)
If the rented cloud server supports online expansion, it takes less than 30 seconds to upgrade a single instance from 4 cores and 8G to 16 cores and 64G. During the upgrade period, the QPS fluctuation of the MySQL database can be less than 5%.
Realization of key technologies
The optimization of fast startup of virtual machines can be achieved through various methods, such as memory pre-allocation and the use of KVM's HugePage technology to reduce memory paging overhead. Image hierarchical loading: The basic image remains in memory permanently, and differentiated data is loaded on demand. NVMe SSD acceleration boosts IOPS to the million level through the SPDK user-space driver. Tests show that after adopting the optimization scheme, the startup time of Ubuntu 20.04 instances has been shortened from 12 seconds to 3.8 seconds.
Containerized elastic scheduling includes the design of elastic units, and the upper limit of resources for a single Pod is set at 1/3 of the Node capacity (for example, a 4-core 8G Pod is deployed in a 12-core 24G Node). Scheduling algorithm optimization, based on the Binpack strategy to improve resource utilization, combined with anti-affinity rules to avoid single point of failure; Ready probe configuration, define the HTTP /health interface, and ensure that the service is fully initialized before receiving traffic.
Hybrid cloud elastic architecture
Unified resource pool management can be achieved, and local IDC and public cloud resources can be orchestrated through Terraform. It is also possible to intercommunicate across cloud networks and establish IPSec or dedicated line connections. Prioritize the expansion of private cloud nodes with lower costs. Once the capacity is exceeded, switch to the public cloud.
Typical application scenario data
During the "618" period of e-commerce flash sales, a certain platform automatically expanded to 5,000 instances, supporting 280,000 orders per second, with a resource utilization rate of 82%. Video live streaming can be dynamically expanded through real-time transcoding clusters. The processing delay of 4K live streaming streams is stable within 200ms, and the expansion response time is less than 15 seconds. AI inference utilizes GPU instances to automatically scale based on the length of the inference queue. The processing throughput of the ResNet50 model has increased from 100 QPS to 2400 QPS.
Dynamic scaling technology is moving towards intelligence. For instance, the AlphaScaler system, through a deep reinforcement learning model, has increased the accuracy of scaling decisions by 40% and reduced the resource waste rate to below 4%. Dynamic expansion easily realizes virtual machines and refines them to functional levels, truly achieving on-demand resource allocation.
