Smart Disaggregated Memory for Object Store Offloading
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Solution Overview
Problem
Current data access solutions fail to optimize input/output operations by ignoring or not understanding the semantics of data access, leading to inefficiencies in processing and scaling across networks, and they either offload operators without proper processing or cannot scale to distributed clients.
Innovation Solution
The system employs smart disaggregated memory that uses machine learning models to identify and process the semantic structure of data objects, offloading memory-intensive tasks and optimizing data access by predicting the semantic structure and relationships, thereby reducing data movement and network bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is stored in local fast memory to reduce I/O overheads, then data access speed is improved, but memory capacity is insufficient to accommodate increasing data
Solution Approach 1:
The patent transitions from local memory storage to remote memory storage by utilizing network infrastructure, effectively adding a spatial dimension to the storage architecture. This allows compute nodes to access data stored in remote memory devices over the network, overcoming the capacity limitations of local memory while maintaining data accessibility.
Solution Approach 2:
The patent introduces a file system layer as an intermediary between compute nodes and remote memory devices. This file system handles data buffering, caching, and management, enabling efficient data access patterns that mitigate the speed penalty of remote storage while providing virtually unlimited capacity.
2Productivity
If more data is stored in local memory to reduce I/O operations, then I/O overhead is reduced, but memory requirements cannot be practically increased
Solution Approach 1:
The patent creates a universal memory pool that can be shared across multiple compute nodes through the network. This remote memory system serves multiple functions: it provides capacity for all nodes, enables collaborative data access, and allows dynamic allocation of storage resources, replacing the need for each node to maintain large local memory.
Solution Approach 2:
The system moves storage from the local memory dimension to the networked remote storage dimension, creating a hierarchical memory architecture where frequently accessed data can be cached locally while less frequently accessed data resides remotely, combining the benefits of both local speed and remote capacity.
3Quantity of substance
If disaggregated memory is used to scale storage, then memory capacity is improved, but data movement and network bandwidth usage increase
Solution Approach 1:
The patent implements predictive preloading mechanisms that anticipate future data access patterns and preload data into local cache memory before it is actually needed. This preliminary action reduces the amount of data that needs to be transferred over the network during actual computation, lowering network bandwidth usage while maintaining large effective memory capacity.
Solution Approach 2:
The system employs feedback mechanisms that monitor data access patterns and dynamically adjust caching strategies and data placement in remote memory. By continuously adapting to actual usage patterns, the system optimizes network traffic by keeping frequently accessed data locally and minimizing redundant data transfers.
Data Source
AI summary
Systems and methods are provided for performing object store offloading. A user query can be received from a client device to access a data object. The semantic structure associated with the data object can be identified, as well as one or more relationships associated with the semantic structure of the data object. A view of the data object can be determined based on the one or more relationships and said view can be provided to a user interface.