Container Workload Placement Balancing CPU Memory Resources
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Solution Overview
Problem
Current container orchestration systems inefficiently allocate resources, often failing to place containers due to insufficient CPU and memory capacity on individual nodes within a cluster, leading to unnecessary resource allocation and increased costs, as they do not effectively migrate existing workloads to create sufficient capacity before adding additional resources.
Innovation Solution
The proposed solution involves an orchestrator service that migrates existing workloads to optimize resource usage within a cluster, prioritizing nodes with lower resource utilization to free up sufficient CPU and memory capacity before allocating additional resources, thereby reducing operational costs and improving resource balancing across nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If container schedulers allocate physical resources to a deployment when minimum capacity is not available on any one node, then additional hardware resources are assigned to the workload, but this results in increased costs to the computing environment as unneeded resources might be allocated to the cluster
Solution Approach 1:
The orchestrator performs preliminary actions by evaluating all possible container placements across the cluster before making a deployment decision. It calculates placement scores considering current resource utilization, container requirements, and cluster state to determine the optimal node for placement, rather than simply allocating resources when a single node has insufficient capacity
Solution Approach 2:
The system changes the decision parameters from a binary 'available/not available' check to a multi-factor evaluation including resource utilization metrics, placement scores, and cluster-wide resource distribution. This allows the orchestrator to make informed decisions about whether to place containers on existing nodes or allocate new hardware
2Productivity
If schedulers fail to place the container or request additional server resources within the HCI without first attempting to relocate or migrate existing containers to other existing nodes, then container deployment is achieved, but resource utilization efficiency deteriorates
Solution Approach 1:
Before deploying a new container, the orchestrator performs preliminary evaluation of the cluster state and calculates placement scores for all nodes. This advance planning ensures that containers are placed on the most appropriate existing nodes, maximizing resource utilization before deployment occurs
Solution Approach 2:
The orchestrator autonomously manages container placement by continuously monitoring cluster resource states and making intelligent placement decisions without external intervention. It self-adjusts the deployment strategy based on real-time cluster conditions, optimizing both deployment speed and resource efficiency
3Reliability
If container orchestrators continually allocate physical resources to a deployment even when sufficient CPU and memory resources are available for new deployments across multiple nodes, then container deployment is maintained, but resource allocation efficiency worsens
Solution Approach 1:
The orchestrator implements continuous feedback loops by monitoring cluster resource utilization states and using this information to adjust container placement decisions. It evaluates current resource usage patterns and modifies deployment strategies accordingly, preventing unnecessary resource allocation while maintaining deployment reliability
Solution Approach 2:
The system transitions from static resource allocation thresholds to dynamic parameter evaluation that considers real-time resource utilization metrics. The orchestrator adjusts placement decisions based on changing cluster conditions, optimizing the balance between deployment continuity and resource efficiency
Data Source
AI summary
Various examples are disclosed for workload placement and workload balancing across a cluster of nodes. Workloads can be migrated between nodes to free sufficient CPU and memory resources to place a new workload onto a cluster. Workloads can also be migrated between pods to balance CPU and memory utilization of nodes in a cluster.