Dynamic Resource Pooling for Cloud Storage Rebalancing
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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 across resources and additional component purchases, which are 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 by spreading commitments across multiple instances, using logical areas for data distribution, and automatically adjusting resource instances based on usage, enabling fine-grained workload balancing and efficient resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If additional resources are purchased and installed to address insufficient storage capacity, IOPS, and bandwidth, then resource capacity is improved, but device complexity and cost increase
Solution Approach 1:
The patent combines multiple resource instances into a shared resource pool that can dynamically serve multiple users. Instead of purchasing separate resources for each user, the system merges resources into a pool that is allocated based on demand, reducing total resource requirements and complexity.
Solution Approach 2:
The shared resource pool serves multiple functions and multiple users simultaneously. The same physical resources can be allocated to different users at different times based on workload demands, making the resource infrastructure universal and multi-functional rather than dedicated to single purposes.
2Quantity of substance
If data is redistributed across additional resource instances, then storage capacity is improved, but loss of time increases due to complex redistribution and mapping updates
Solution Approach 1:
The system implements dynamic resource allocation where the shared resource pool can automatically adjust its composition and allocation without static pre-planning. When resources need to be added or removed, the system dynamically rebalances the pool rather than requiring complete static redistribution, reducing time losses.
Solution Approach 2:
The shared resource pool performs self-allocation and self-balancing based on monitored workload demands. The system automatically determines when and how to redistribute data across resources without requiring manual intervention or complex external coordination, reducing the time loss associated with manual redistribution processes.
3Adaptability or versatility
If the number of concurrent requests exceeds the processing ability of a single instance, then service coverage is improved, but reliability deteriorates due to insufficient processing capacity
Solution Approach 1:
The patent segments the shared resource pool into multiple virtual instances or allocation units that can independently handle concurrent requests. When demand exceeds single-instance capacity, the system divides the workload across multiple segments of the resource pool, maintaining reliability through distributed processing while preserving service coverage.
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 services. 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 redistribute the data in the data volume.