Cross-Cluster Load Balancer for Hybrid Cloud Workload Migration
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Solution Overview
Problem
Existing technologies struggle to effectively manage multi-cluster datacenter configurations, failing to efficiently balance workloads across hybrid cloud environments due to scalability requirements and resource heterogeneity.
Innovation Solution
A cross-cluster load balancer that dynamically monitors and redistributes workloads across multiple clusters, utilizing a management service to perform cross-cluster migrations with minimal downtime, and incorporates a mutual exclusion control rule to avoid conflicts with cluster scheduling services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single cluster resource scheduler is used to manage workloads within one cluster, then workload distribution and resource allocation can be effectively managed, but the system cannot effectively manage multi-cluster configurations or hybrid cloud environments
Solution Approach 1:
The system divides the workload management function into two segments: cluster-level schedulers that manage individual clusters and a cross-cluster load balancer that manages workloads across multiple clusters. This segmentation allows each component to focus on specific responsibilities, enabling multi-cluster management without overwhelming complexity in a single scheduler.
Solution Approach 2:
The cross-cluster load balancer acts as an intermediary between multiple cluster schedulers and the workload management layer. It receives workload information from cluster schedulers, makes cross-cluster placement decisions, and coordinates migrations, thereby enabling multi-cluster management while maintaining simplicity at the cluster level.
2Productivity
If workloads are redistributed within a cluster for load balancing, then resource utilization can be optimized, but migration downtime and service disruption increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating migration paths and preparing destination resources before actual workload migration occurs. The cross-cluster load balancer proactively identifies optimal destination clusters and prepares migration plans, allowing workloads to be redistributed with minimal disruption to ongoing services.
Solution Approach 2:
The load balancer enables continuous workload migration by maintaining service availability during the migration process. Workloads are migrated in a way that preserves continuous operation, with the system ensuring that service requests continue to be processed without interruption or minimal interruption throughout the redistribution process.
3Ease of operation
If cluster schedulers independently manage workloads without coordination, then each cluster can be optimized locally, but conflicts arise during cross-cluster workload migrations
Solution Approach 1:
The cross-cluster load balancer implements a feedback mechanism that continuously monitors cluster resource status, workload locations, and migration progress. It receives feedback from cluster schedulers about resource availability and workload states, adjusts migration decisions accordingly, and coordinates cross-cluster operations to prevent and resolve conflicts while maintaining independent cluster management capabilities.
Data Source
AI summary
Various examples are disclosed for performing cross-cluster load balancing. In some aspects, a workload is selected for cross-cluster migration. A destination cluster is identified for a migration of the workload from a source cluster to the destination cluster. A cross-cluster migration recommendation is generated to migrate the workload from the source cluster to the destination cluster.