Global Object Storage Placement for Low-Latency Retrieval
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Solution Overview
Problem
Distributed networks face high latency and redundancy issues due to long distances between object storage locations and request origins, leading to inefficient data transmission and increased costs.
Innovation Solution
Objects are dynamically stored closer to where they are accessed most frequently, minimizing latency and redundancy by analyzing request patterns to optimize storage across a global network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If objects are stored in distant regions, then storage capacity is increased, but latency increases and transmission efficiency decreases
Solution Approach 1:
The patent implements local quality by storing object copies in specific regions based on request patterns. Instead of uniform distribution, objects are strategically replicated in regions where they are most frequently accessed, making the storage system adapt to local access characteristics and reduce latency for those regions.
Solution Approach 2:
The patent applies dynamics by making the storage configuration changeable over time. The system analyzes request patterns and dynamically adjusts object placement and replication strategies, allowing the storage architecture to adapt to evolving access patterns and optimize performance continuously.
2Reliability
If objects are replicated in multiple regions, then availability is improved, but redundancy costs increase
Solution Approach 1:
The patent implements feedback by continuously monitoring request patterns and using this information to adjust replication strategies. The system analyzes actual access data and modifies object placement decisions accordingly, ensuring replication occurs only where it provides value and avoids unnecessary redundancy costs.
Solution Approach 2:
The patent applies parameter changes by adjusting replication factors and placement strategies based on analyzed request patterns. The system changes replication parameters dynamically, replicating objects more aggressively for high-demand items and less so for low-demand items, thereby optimizing the balance between availability and cost.
3Loss of time
If objects are stored closer to request origins, then latency is reduced, but storage distribution complexity increases
Solution Approach 1:
The patent implements self-service by enabling the storage system to automatically analyze request patterns and make intelligent placement decisions without manual intervention. The system self-adjusts object distribution based on observed access patterns, reducing the need for complex manual configuration while achieving optimized latency performance.
Solution Approach 2:
The patent replaces manual storage configuration mechanisms with automated pattern analysis and intelligent decision-making systems. Instead of mechanically configuring storage based on fixed rules, the system uses data-driven analysis to dynamically determine optimal placement, simplifying the overall management complexity.
Data Source
AI summary
Disclosed herein are methods for efficient distribution of one or more objects. The method dynamically moves an object to a region on a network. The methods disclosed herein can be used to lower latency across a network and decrease the storage space lost to a system where each object is stored at every server.