Cluster Defragmentation for Expansion Failure Prediction
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Solution Overview
Problem
Conventional cloud computing systems face challenges in managing resource allocation efficiently, leading to expansion failures and high costs due to fragmented capacity across server nodes and clusters, as they often react to failures rather than proactively addressing potential issues.
Innovation Solution
A cluster defragmentation management system that predicts expansion failures by analyzing utilization data and generates defragmentation instructions to proactively manage capacity, ensuring efficient resource utilization and reducing hardware overhead costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If cloud computing systems allocate additional server nodes to accommodate deployment expansions, then resource capacity is increased, but hardware overhead costs increase and resource utilization efficiency decreases
Solution Approach 1:
The system performs preliminary defragmentation actions by identifying and consolidating fragmented resources before expansion failures occur. The defragmentation management system proactively reconfigures resource allocations to create contiguous available capacity, preventing the need for additional hardware nodes and reducing hardware overhead costs while maintaining adequate resource capacity
2Quantity of substance
If cloud computing systems allocate additional server nodes to accommodate deployment expansions, then resource capacity is increased, but resource utilization efficiency decreases
Solution Approach 1:
The system performs preliminary defragmentation actions by identifying and consolidating fragmented resources before expansion failures occur. The defragmentation management system proactively reconfigures resource allocations to create contiguous available capacity, preventing the need for additional hardware nodes and reducing hardware overhead costs while maintaining adequate resource capacity
3Reliability
If cloud computing systems react to expansion failures after they occur, then allocation failures are addressed, but system reliability decreases due to reactive rather than proactive management
Solution Approach 1:
The system performs preliminary defragmentation actions by identifying and consolidating fragmented resources before expansion failures occur. The defragmentation management system proactively reconfigures resource allocations to create contiguous available capacity, preventing the need for additional hardware nodes and reducing hardware overhead costs while maintaining adequate resource capacity
Solution Approach 2:
The system implements feedback mechanisms by monitoring resource utilization patterns and fragmentation levels across the cloud computing infrastructure. The defragmentation management system uses this feedback to dynamically adjust resource allocations and trigger defragmentation operations when fragmentation thresholds are exceeded, creating a closed-loop control system that proactively maintains system reliability
Data Source
AI summary
The present disclosure relates to systems, methods, and computer readable media for predicting expansion failures and implementing defragmentation instructions based on the predicted expansion failures and other signals. For example, systems disclosed herein may apply a failure prediction model to determine an expansion failure prediction associated with an estimated likelihood that deployment failures will occur on a node cluster. The systems disclosed herein may further generate defragmentation instructions indicating a severity level that a defragmentation engine may execute on a cluster level to prevent expansion failures while minimizing negative customer impacts. By uniquely generating defragmentation instructions for each node cluster, a cloud computing system can minimize expansion failures, increase resource capacity, reduce costs, and provide access to reliable services to customers.