Cluster Node Scaling Strategy for Maintenance Availability
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Solution Overview
Problem
Maintenance activities in cluster systems reduce availability levels as nodes need to be shut down or made unavailable, potentially leading to a decrease in the number of nodes ready to process user requests, which can hinder disaster recovery operations.
Innovation Solution
Implement a scaling out and scaling in strategy where additional nodes with completed maintenance are added before removing non-upgraded nodes, ensuring the availability level remains consistent or higher than before maintenance, and iteratively replacing all nodes with upgraded software applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If maintenance activities are performed on cluster nodes, then software/hardware is updated or repaired, but the availability level of the cluster decreases
Solution Approach 1:
The system performs scaling out (adding maintenance-upgraded nodes) before scaling in (removing non-upgraded nodes). This preliminary action ensures that upgraded nodes are already available in the cluster before older nodes are removed, preventing availability degradation during maintenance transitions.
Solution Approach 2:
The cluster dynamically adjusts its node composition by iteratively adding upgraded nodes and removing non-upgraded nodes. This dynamic approach allows the cluster to maintain optimal availability levels throughout the maintenance process by continuously balancing node additions and removals.
2Quantity of substance
If nodes are removed from the cluster after maintenance, then the cluster size is reduced, but the availability level may drop below the original level
Solution Approach 1:
The system adds upgraded nodes to the cluster before removing non-upgraded nodes. This preliminary action ensures that the cluster maintains sufficient node capacity throughout the maintenance process, preventing availability drops below original levels.
Solution Approach 2:
The system changes the node composition parameters by iteratively adding upgraded nodes and removing non-upgraded nodes in controlled increments. This parameter change approach ensures that the cluster maintains adequate node count and availability levels throughout the maintenance transition.
Data Source
AI summary
An aspect of the present invention mitigates reduction in availability level during maintenance of nodes in a cluster. In one embodiment, on receiving an indication that a maintenance activity is to be performed on the cluster, a scaling out of the cluster is first performed to add some nodes having the maintenance activity already performed, followed by a scaling in of the cluster to remove some of the nodes in the cluster which do not yet have the maintenance activity performed. The scaling out is performed before any scaling in of the cluster such that the number of nodes available in the cluster after the scaling in is not less than the number of nodes in the cluster at the time of receiving the indication. Accordingly, the reduction in availability level (which is based on the number of nodes available) is mitigated.