How to schedule computing resources during cross-platform migration? How to keep performance unaffected during VM migration? How to optimize performance when moving to another platform?
Overview of Virtual Machine Migration: Efficiency and Risks Coexist
Value and Application Scenarios of Migration
VM migration enables hardware maintenance, load balancing, or disaster recovery while minimizing service disruption.
Key Factors Affecting Migration Performance
Network latency, storage performance, and VM configuration compatibility are the primary influences.
Analysis of Existing Challenges
Traditional centralized scheduling may overload the scheduler. Additionally, ignoring security-level differences and mutual exclusion policies between nodes can impact VM security isolation and migration efficiency.
Thorough Preparation Before Migration: Planning Ahead
Define Migration Goals and Requirements
Determine scope (such as full VM migration or specific data migration) and performance standards.
Migration Risk Assessment
Anticipate risks like network interruptions, data loss, or configuration incompatibility, and create mitigation strategies.
Develop a Detailed Migration Plan
Outline timelines, resources, personnel roles, and rollback strategies.
Resource Preparation
Ensure sufficient storage, computing resources, and network configurations on the target platform.
VM Configuration Check and Compatibility Enhancement
Verify that source configurations are compatible with the target virtualization environment.
Data Security Assurance
Create backups or snapshots before migration to enable recovery when required.
Performance Optimization Strategies During Migration: Acceleration and Safeguarding
Network Optimization
Ensure sufficient bandwidth, use QoS to prioritize migration traffic, and reduce packet round-trip time.
Storage Optimization
Improve read/write performance, ensure high-bandwidth and low-latency storage networks, and implement load balancing.
Data Transfer Optimization
Use compression and encryption to reduce transmitted data volume while maintaining security.
Efficiency Improvement
Migrate multiple VMs in parallel where possible to reduce overall migration time.
Resource Guarantee
Reserve sufficient resources to prevent performance degradation caused by resource contention.
Real-Time Monitoring and Dynamic Adjustment
Monitor migration metrics in real time and adjust resource allocation as needed.
Post-Migration Performance Testing and Continuous Tuning: Ensuring Stability and Efficiency
Performance Benchmark Testing
Test CPU, memory, storage, and network performance after migration and compare results with pre-migration data.
Continuous Performance Monitoring
Track CPU utilization, memory usage, disk I/O, and network traffic through system monitoring tools.
Resource Allocation Adjustment
Adjust CPU cores, memory, and storage resources based on test results.
OS and Hardware Configuration Optimization
Tune kernel parameters, network settings, and storage configurations.
Long-Term Strategy
Continuous monitoring and optimization are required. Review and improve performance regularly based on evolving business requirements.
Intelligent Resource Scheduling System: Balancing Security and Performance
Security Policy-Based Scheduling Method
After receiving a cross-domain migration request, the system checks whether migration is permitted based on VM security information, source domain policies, and host performance.
If permitted, it determines the target domain based on security levels, mutual exclusion policies, workload conditions, or scheduling algorithms.
VMs are only allowed to migrate from lower-security domains to domains with the same or higher security levels — security-level downgrades are prohibited.
Distributed Cascading Scheduling System
Includes a central scheduler (with a centralized security policy repository) and multiple domain schedulers (with independent domain repositories).
The central scheduler handles cross-domain migrations, while domain schedulers manage intra-domain migrations and process requests from the central scheduler.
This multi-level architecture distributes workloads and prevents single points of failure.
Fine-Grained CPU Resource Management
Optimizes CPU allocation through NUMA adaptation and hyper-threading technologies.
Memory Performance Optimization Techniques
Uses Huge Pages to reduce TLB pressure; evaluates balloon drivers versus swapping (with Swap generally recommended to be disabled in production environments); and leverages transparent page sharing.
Storage I/O Acceleration Solutions
Implements distributed storage optimization strategies, including replica policies, erasure coding, and cache tiering.
Optimizes disk queue depth and combines SSD and RAID technologies to improve storage performance.
Dynamic Computing Resource Pool Implementation
Enables automated resource scaling based on workload demand (for example, creating VMs when CPU utilization reaches a defined threshold) with cooling strategies.
Heterogeneous Cluster Hybrid Management
Manages x86 and non-x86 servers within the same cluster using labels for workload distribution.
Supports unified GPU resource scheduling for AI training workloads.
Cross-Cluster Load Balancing
Achieves load balancing across clusters through multi-data-center scheduling and virtual routers, reducing pressure on individual clusters.
High Availability and Disaster Recovery Performance Optimization
Uses distributed storage HA capabilities to support VM migration within seconds.
Supports memory live migration to minimize service interruption and controls RPO through asynchronous replication.
FAQ
Q: How can enterprises maximize performance optimization during migration?
A: Optimize network and storage resources during migration; perform benchmarking and adjust resources after migration.
ZStack provides fine-grained resource management and I/O acceleration solutions.
Q: What role does VM resource scheduling play in migration?
A: VM resource scheduling dynamically allocates computing, memory, storage, and network resources.
During migration, intelligent scheduling adjusts resource allocation based on workload changes, ensuring balanced resource utilization.
ZStack enables scheduling based on security policies, NUMA optimization, Huge Pages, GPU resources, and other capabilities.
Q: What security risks exist during cross-domain migration and how can they be mitigated?
A: Migration may impact security isolation between domains.
Mitigation approaches include security-policy-based scheduling, permission evaluation, and enforcing migration rules that only allow movement from lower-security domains to equal or higher-security domains.
ZStack integrates centralized and domain-level security policy repositories to support secure migration management.
Q: Beyond performance, what other factors should enterprises consider?
A: Enterprises should consider compatibility, high availability and disaster recovery, data security and integrity, and cost management.
ZStack provides heterogeneous cluster management, multi-replica HA capabilities, and full-link performance monitoring.
Q: How does ZStack support AI training resource scheduling?
A: Heterogeneous cluster management supports x86 and non-x86 server environments.
ZStack provides unified GPU resource scheduling and dynamically manages GPU resources, including environments using NVIDIA A100 GPUs, to support AI workload requirements while improving resource utilization.