Dynamic Resource Allocation via Logical Area Mapping
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Solution Overview
Problem
Conventional approaches to managing resource sharing and allocation in cloud computing environments face challenges such as insufficient storage capacity, I/O operations per second (IOPS), and bandwidth, requiring complex redistribution of data and additional resource provisioning, which can be costly and time-consuming.
Innovation Solution
The system allows users to request specific quality of service levels for IOPS, bandwidth, and storage capacity, dynamically allocating resources across multiple instances, using logical areas for efficient data distribution and rebalancing, and automatically adjusting resource provisioning based on usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number and/or amount of resources dedicated to tasks is increased to address insufficient storage capacity, IOPS, and bandwidth, then resource performance is improved, but device complexity and cost increase
Solution Approach 1:
The patent segments resources into multiple tiers (e.g., hot, warm, cold storage) and uses logical units to divide data storage across multiple physical devices. This allows incremental resource addition without requiring complete system redesign or complex redistribution of all data.
Solution Approach 2:
The system dynamically adjusts resource allocation based on usage patterns and performance requirements. Resources can be added, removed, or reconfigured without static commitments, allowing the system to adapt to changing demands while maintaining guaranteed service levels.
2Quantity of substance
If data is redistributed across resource instances to address capacity issues, then storage capacity is improved, but loss of time increases due to complex redistribution and mapping updates
Solution Approach 1:
The system performs preliminary actions by pre-allocating logical units and establishing mapping structures before data redistribution is needed. When capacity expansion is required, the mapping framework is already in place, allowing rapid data migration without time-consuming reconstruction of allocation structures.
Solution Approach 2:
Logical units serve as intermediaries between physical devices and data. This abstraction layer simplifies redistribution operations by providing a stable mapping interface that remains consistent even as underlying physical resources change, reducing the time required for capacity expansion.
3Quantity of substance
If additional components are purchased and installed to increase resources, then resource capacity is improved, but device complexity and cost increase
Solution Approach 1:
The system designs resources to be multi-functional and universally compatible. Physical devices can serve multiple purposes (storage, compute, networking) and can be dynamically assigned to different logical units based on demand, reducing the need for specialized components and simplifying system architecture.
Solution Approach 2:
The system allows for partial resource allocation where components can be added incrementally rather than requiring complete system upgrades. Resources can be over-provisioned or under-provisioned based on current needs, with the flexibility to adjust allocation without replacing entire component sets.
Data Source
AI summary
Various aspects of a data volume or other shared resource are determined and updated dynamically for purposes such as to provide guaranteed qualities of service. For example, the number of partitions in a data volume and/or the way in which data is stored across those partitions can be updated dynamically without significantly impacting the customer using the volume. The data stored to the volume can be striped or otherwise distributed across a number of logical areas, which then can be distributed across the partitions. Separate mappings can be used for the data in each logical area, and the logical areas in each partition, such that when moving a logical area only a single mapping has to be updated, regardless of the amount of data in that logical area. Further, logical areas can be moved between partitions without the need to repartition or redistributed the data in the data volume.