Gang Migration Deduplication for Data Center Network Overload
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Solution Overview
Problem
Simultaneous live migration of multiple virtual machines (VMs) in data centers is network-intensive, causing adverse performance impacts due to high network traffic and resource overload, particularly in high-bandwidth low-delay environments, where existing methods fail to effectively reduce data transmission and migration time.
Innovation Solution
The implementation of gang migration with global deduplication (GMGD) identifies and tracks identical memory content across VMs, eliminating duplicate page transmissions through a distributed deduplication mechanism, using a combination of duplicate tracking and deduplication phases to reduce network traffic and migration time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simultaneous live migration of multiple VMs is performed using traditional online compression methods, then migration can be completed, but network traffic is excessive and migration time is prolonged
Solution Approach 1:
The patent merges the migration streams of multiple VMs and applies cluster-wide deduplication to identify and eliminate duplicate memory pages across all migrating VMs. By combining the deduplication process at the cluster level rather than individually per VM, the system reduces total network traffic volume while maintaining migration productivity.
Solution Approach 2:
The patent performs a duplicate tracking phase before the actual migration to pre-identify redundant memory pages across all VMs in the cluster. This preliminary action allows the system to eliminate duplicates in advance, reducing the volume of data that needs to be transmitted during the migration execution phase.
2Adaptability or versatility
If gang migration is performed to handle peak workloads and maintenance, then resource re-allocation is achieved, but core network links and switches are overloaded
Solution Approach 1:
The patent combines multiple VM migration operations into a coordinated gang migration process with cluster-wide deduplication. By merging the deduplication function across the entire cluster, the system reduces the total network traffic generated by simultaneous migrations, thereby reducing stress on core network links and switches while maintaining resource re-allocation adaptability.
3Productivity
If gang migration is performed, then multiple VMs can be migrated simultaneously, but performance at network edges deteriorates due to bandwidth competition
Solution Approach 1:
The patent performs duplicate tracking and identification before the actual migration data transfer. By pre-identifying redundant pages across all migrating VMs, the system reduces the volume of data that must traverse network edges during migration, thereby minimizing bandwidth competition and protecting application performance while maintaining migration throughput.
4Quantity of substance
If deduplication is applied to reduce migration data, then network traffic is reduced, but coordination overhead increases
Solution Approach 1:
The patent implements a cluster-wide deduplication mechanism that serves multiple migrating VMs simultaneously through a shared deduplication infrastructure. This universal approach allows the system to reduce data transmission volume for all VMs while amortizing the coordination overhead across the entire cluster, making the overhead manageable despite the increased scope.
Data Source
AI summary
Datacenter clusters often employ live virtual machine (VM) migration to efficiently utilize cluster-wide resources. Gang migration refers to the simultaneous live migration of multiple VMs from one set of physical machines to another in response to events such as load spikes and imminent failures. Gang migration generates a large volume of network traffic and can overload the core network links and switches in a data center. The present technology reduces the network overhead of gang migration using global deduplication (GMGD). GMGD identifies and eliminates the retransmission of duplicate memory pages among VMs running on multiple physical machines in the cluster. A prototype GMGD reduces the network traffic on core links by up to 51% and the total migration time of VMs by up to 39% when compared to the default migration technique in QEMU/KVM, with reduced adverse performance impact on network-bound applications.


