Concurrent Time Bucket Generations for Scalable Object Scheduling
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Solution Overview
Problem
Current storage systems face inefficiencies in scheduling and processing objects across different time buckets, as they often rely on single time bucket generations, which can lead to suboptimal performance and scalability issues when handling varying workloads or types of processing operations.
Innovation Solution
The proposed solution involves concurrently using multiple time bucket generations for scheduling and processing objects, allowing for flexible configuration of time bucket intervals, partitions, and assignment algorithms, enabling simultaneous processing of objects across different time buckets by multiple object processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single time bucket generation is used for scheduling objects, then the system structure is simple, but the scalability and performance are limited when handling varying workloads
Solution Approach 1:
The patent segments the scheduling system into multiple time bucket generations, where each generation handles specific workload characteristics. This allows the system to divide complex scheduling tasks into manageable segments that can be processed independently, improving scalability without overwhelming the system with monolithic complexity.
Solution Approach 2:
The system dynamically selects which time bucket generation to use based on workload characteristics. Different generations can be activated or deactivated depending on the specific processing requirements, allowing the system to adapt its complexity level to match the actual workload demands rather than maintaining fixed high complexity.
2Productivity
If multiple time bucket generations are used concurrently, then performance and scalability improve, but the system complexity increases
Solution Approach 1:
By segmenting the processing system into multiple specialized time bucket generations, each generation can be optimized for specific processing tasks. This segmentation enables parallel processing of different object types or priority levels, significantly improving productivity while keeping each individual generation's complexity manageable.
Solution Approach 2:
The multiple time bucket generations serve universal scheduling functions but with specialized optimizations. Each generation can handle various types of objects and processing operations, allowing the system to improve productivity across different workload scenarios without requiring entirely separate specialized systems for each case.
3Adaptability or versatility
If time bucket configurations are fixed, then the system is simple to manage, but it cannot optimize for different workload changes and processing types
Solution Approach 1:
The system employs dynamic configuration where time bucket parameters such as interval sizes, partition counts, and assignment algorithms can be adjusted based on detected workload characteristics. This dynamic adaptation allows the system to optimize performance for different processing types without requiring manual reconfiguration, balancing adaptability with operational simplicity.
Solution Approach 2:
The patent changes key parameters of the time bucket configuration including interval durations, number of partitions, and assignment algorithms based on workload analysis. By systematically varying these parameters across different generations, the system achieves workload adaptability while maintaining structured configuration management rather than arbitrary complexity.
Data Source
AI summary
Concurrent processing of objects is scheduled using time buckets of different time bucket generations. A time bucket generation includes a configuration for time buckets associated with that time bucket generation. The concurrent use of different time bucket generations includes the concurrent processing of objects referenced by time buckets of different time bucket generations.