Dynamic Memory Mapping for Edge Shared-Memory Latency Bottlenecks
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Solution Overview
Problem
Existing information handling systems face challenges in efficiently managing shared memory resources across edge networks, leading to processing bottlenecks and latency issues, particularly in edge computing environments where data and processing are distributed across client devices and edge systems.
Innovation Solution
Implementing a latency-aware dynamic memory map that utilizes a Smart Data Accelerator Interface (SDXI) architecture to manage shared memory across client devices and edge systems, with a memory optimization manager to monitor and balance memory traffic, distribute data, and optimize data placement to reduce latency and processing bottlenecks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If shared memory is distributed across multiple edge systems, then memory capacity and processing capability are improved, but access latency and processing bottlenecks increase
Solution Approach 1:
The system performs preliminary actions by predicting future memory access patterns using machine learning models and pre-positioning data in optimal memory locations before actually needed. This reduces access latency by eliminating the need for real-time memory allocation decisions during critical processing operations.
Solution Approach 2:
The memory map is made dynamic and adaptive, continuously adjusting memory allocation and data placement based on real-time workload characteristics and access patterns. This dynamic reconfiguration optimizes the balance between memory capacity utilization and access speed, resolving the contradiction between distributed memory benefits and latency penalties.
2Reliability
If data is locked in shared memory of a first edge system, then data consistency is improved, but processing throughput decreases
Solution Approach 1:
The shared memory space is segmented into multiple partitions or regions, allowing different edge systems to lock and process different segments simultaneously. This segmentation enables parallel processing while maintaining data consistency within each segment, resolving the trade-off between consistency and throughput.
Solution Approach 2:
An intermediary memory optimization manager is introduced to coordinate locking and access between multiple edge systems. This intermediary manages lock granularity and timing, enabling fine-grained concurrent access that maintains consistency while maximizing throughput through controlled parallelism.
3Speed
If a latency-aware dynamic memory map is implemented, then access speed is improved, but system complexity increases
Solution Approach 1:
The memory system implements self-service through automated machine learning models that predict access patterns and autonomously optimize memory placement without requiring complex manual configuration or intervention. This self-optimizing capability achieves high access speeds while keeping the control logic relatively simple and adaptive.
Solution Approach 2:
The system dynamically changes memory management parameters such as allocation sizes, placement strategies, and locking granularities based on workload characteristics. These parameter adjustments are made automatically by the memory optimization manager, achieving high performance without requiring complex fixed-structure designs.
Data Source
AI summary
An edge network includes an information handling system configured to manage a shared memory, and a client device. A series of edge systems each include a portion of the shared memory. The information handling system determines that the client device is bottlenecked in processing a service, locks data associated with the service in the shared memory of a first edge system, permits a second edge system to access the first data, and directs the second edge system to process the service.