Data Storage Resource Allocation List Updating
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Solution Overview
Problem
Current data storage management systems face challenges in efficiently allocating resources during storage operations due to complex multidimensional resource allocation, leading to inefficiencies and increased processing time, especially when dealing with large data loads and varying resource availability.
Innovation Solution
A resource allocation system that employs preordered/intelligent resource checks, category blacklisting, and a resource holding area to optimize resource allocation by prioritizing requests, abbreviating checks, and preserving priority access to resources, thereby improving the efficiency of data storage operations within limited time windows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional resource allocation methods are used to handle increasing data storage demands, then the system can maintain basic storage operations, but the processing time grows exponentially and the system cannot accommodate increased data loads within fixed storage windows
Solution Approach 1:
The patent segments the resource allocation process into distinct phases: sorting requests by priority, performing abbreviated pre-ordered checks on critical resources first, and using category blacklisting to eliminate entire classes of failed requests. This segmentation allows the system to quickly eliminate unpromising allocations without exhaustively checking all resources for all requests, thereby reducing processing time while maintaining allocation quality.
Solution Approach 2:
The patent performs preliminary actions by sorting all requests by priority before allocation begins, and by performing abbreviated checks on the most critical resources first. This preliminary organization and partial verification allow the system to make faster decisions later, as high-priority requests are already positioned at the front of the queue and their critical resource requirements are partially verified in advance.
2Reliability
If the system performs comprehensive resource checks for each request to ensure optimal allocation, then allocation accuracy improves, but the complexity of the allocation process increases exponentially
Solution Approach 1:
The patent applies local quality by performing different levels of checks on different resources based on their importance. Critical resources receive more thorough verification through pre-ordered checks, while less critical resources receive simpler verification. This differentiated approach maintains allocation reliability for critical resources while reducing overall algorithmic complexity.
Solution Approach 2:
The patent performs partial checks on resources rather than exhaustive verification of all resources for all requests. By performing abbreviated pre-ordered checks on the most critical resources first and using category blacklisting to skip entire classes of resources when appropriate, the system achieves sufficient allocation reliability without the exponential complexity of comprehensive checking.
3Measurement precision
If the system maintains a sorted queue of requests and allocates resources to higher priority requests first, then priority-based allocation accuracy improves, but the time required to walk through and match each request with available resources increases
Solution Approach 1:
The patent performs preliminary sorting of all requests by priority before the allocation process begins. This preliminary action ensures that high-priority requests are always at the front of the queue, allowing the system to naturally serve them first without requiring complex real-time priority management during the allocation process, thereby maintaining precision while reducing time.
Solution Approach 2:
The patent segments the request processing by using category blacklisting to group and eliminate entire classes of requests that cannot be satisfied. This segmentation allows the system to quickly skip over large numbers of low-priority or impossible-to-satisfy requests without individually evaluating each one, thereby maintaining priority-based precision while significantly reducing matching time.
4Device complexity
If the system reduces the number of variables and employs dedicated resources for higher priority requests, then allocation complexity decreases, but resource utilization efficiency decreases when dedicated resources sit idle
Solution Approach 1:
The patent implements dynamic resource allocation where resources are not permanently dedicated but are allocated based on current request priorities and availability. The system dynamically adjusts which resources are assigned to which requests based on real-time conditions, allowing high-priority requests to access any available resource rather than being constrained to pre-assigned dedicated resources. This dynamic approach maintains low complexity while improving utilization efficiency.
Data Source
AI summary
A system and method to perform data management operations in a data management system assigns the data management request to one or more available data management resources. If the data management request fails, at least one data management resource at least partially responsible for the failure is determined, as is a category associated with the one data management resource at least partially responsible for the failure. Other data management requests are identified in a list of data management requests that request data management resources having the same category and the list of data management requests is updated to indicate that the data management system should not perform the other identified data management requests.