Data Lake Auto-Scaling with Dynamic Bin Allocation
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Solution Overview
Problem
Conventional data lakes face challenges in scaling and self-healing to accommodate changing load requirements from multiple tenants, making it complex to adjust capacity efficiently.
Innovation Solution
A data lake platform is configured with auto-scaling and self-healing capabilities by allocating and deallocating fixed-capacity bins based on load requirements, allowing for dynamic resource allocation and utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data lakes use static capacity allocation, then system simplicity is maintained, but adaptability to changing load requirements deteriorates
Solution Approach 1:
The patent implements dynamic capacity allocation where bins can be automatically allocated and deallocated based on real-time load requirements. The system transitions from static to dynamic resource management, allowing the data lake to adapt its capacity automatically as tenant load requirements change, resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The system employs self-healing and auto-scaling capabilities that automatically detect capacity issues and allocate or deallocate bins without human intervention. This self-service approach maintains system simplicity while achieving high adaptability, as the system manages its own capacity optimization.
2Productivity
If manual capacity adjustment is used, then system complexity is reduced, but productivity in responding to load changes deteriorates
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor tenant load requirements and automatically trigger bin allocation or deallocation when thresholds are exceeded. This feedback-driven approach enables rapid response to load changes while managing complexity through automated decision-making rules.
Solution Approach 2:
The system pre-configures bin templates with defined capacities and thresholds, allowing for rapid deployment and scaling. When load requirements change, the system can quickly instantiate pre-defined bin configurations, improving response productivity while reducing the complexity of ad-hoc capacity planning.
3Reliability
If fixed-capacity bins are allocated for each tenant, then resource isolation is improved, but loss of resources due to underutilization increases
Solution Approach 1:
The patent allows multiple tenants to share bins when load requirements permit, improving resource utilization. When a tenant's load exceeds thresholds, additional bins are allocated; when below thresholds, bins are deallocated or shared with other tenants. This merging approach reduces resource waste while maintaining isolation through controlled sharing policies.
Solution Approach 2:
Bins are designed as universal resources that can serve multiple tenants dynamically. Rather than dedicating bins exclusively to single tenants, the system allows bins to be allocated to different tenants based on real-time needs, achieving both resource isolation and efficient utilization through multi-functional bin usage.
Data Source
AI summary
A method may include allocating, based on a first load requirement of a first tenant, a first bin having a fixed capacity for handing the first load requirement of the first tenant. In response to the first load requirement of the first tenant exceeding a first threshold of the fixed capacity of the first bin, packing a second bin allocated to handle a second load requirement of a second tenant. The second bin may be packed by transferring, to the second bin, the first load requirement of the first tenant based on the transfer not exceeding the first threshold of the fixed capacity of the second bin. In response to the transfer exceeding the first threshold of the fixed capacity of the second bin, allocating a third bin to handle the first load requirement of the first tenant.