Hybrid Cluster Architecture for Cloud Management Overhead
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Solution Overview
Problem
Cloud computing systems face limitations in scalability and management efficiency due to the significant workload required for management functions such as service deployment, maintenance, and monitoring, which can restrict the resources available for actual computing tasks.
Innovation Solution
A distributed computing system with a separate management cluster that handles service deployment, maintenance, upgrades, monitoring, and reporting for worker clusters, allowing these management functions to be offloaded and freeing resources for processing and storage tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If management functions (service deployment, maintenance, monitoring) are performed on each worker cluster, then service management capability is improved, but the resources available for processing and storage tasks deteriorate
Solution Approach 1:
The system divides the cluster into two distinct segments: a management cluster dedicated to service management functions (deployment, maintenance, monitoring) and worker clusters dedicated to processing and storage tasks. This segmentation allows each part to specialize in its function without interfering with the other, resolving the contradiction between management capability and processing capacity.
Solution Approach 2:
Management functions are extracted from the worker clusters and consolidated into a separate management cluster. This extraction removes the burden of management tasks from worker nodes, freeing their resources to focus entirely on processing and storage operations while maintaining full service management capability through the dedicated management infrastructure.
2Adaptability or versatility
If cloud computing resources are scaled up to provide virtually infinite scalability, then service capacity is improved, but the complexity of provisioning and managing each additional cluster deteriorates
Solution Approach 1:
The management cluster is designed as a universal platform that can manage any number and type of worker clusters through standardized interfaces and protocols. This multi-functionality allows the same management infrastructure to scale with the system, providing service deployment, maintenance, and monitoring capabilities across expanding clusters without proportionally increasing management complexity.
3Ease of manufacture
If physical machines are purchased and maintained for test lab infrastructure, then hardware control is improved, but the flexibility of adding or removing hardware deteriorates
Solution Approach 1:
A virtualization layer acts as an intermediary between the physical hardware and the test lab infrastructure. This intermediary abstracts the physical machines, allowing the management cluster to provision, configure, and manage hardware resources dynamically without direct physical intervention. The virtualization layer enables flexible addition or removal of hardware while maintaining controlled access and management through software-based interfaces.
Data Source
AI summary
A distributed computing system. The system separates management and worker functionality into different clusters thus relieving the worker clusters of management overhead. In particular, the system includes a management cluster. The management cluster is configured to perform service deployment of worker nodes, service maintenance of worker nodes, service upgrade and roll-back of worker nodes, service monitoring of worker nodes, and service reporting of worker nodes. The system further includes one or more worker clusters coupled the management cluster, but separate from the management cluster. Each of the worker clusters includes one or more worker nodes that are configured to provide one or more of processing power, storage, and network connectivity but do not exercise one or more of the management configured functions.