Object Domain Segmentation for Distributed Computing Scalability
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Solution Overview
Problem
Current technologies face challenges in managing computationally intensive distributed computing systems for large-scale virtual environments, such as virtual worlds and games, as they struggle to efficiently distribute and process diverse data types across multiple servers, leading to bottlenecks and limitations in scalability and performance.
Innovation Solution
The implementation of a distributed computing system that organizes computational units using object domains and processing partitions, allowing for the separation and processing of data objects based on their types, and utilizes a distributed shared memory system for low-latency data management and replication, enabling efficient distribution and processing across multiple hardware hosts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computational units are distributed across multiple servers to handle diverse data types, then system capacity and scalability are improved, but system complexity and management difficulty increase
Solution Approach 1:
The patent segments computational units into distinct processing partitions based on data object types (e.g., character data, item data, location data). Each partition is assigned to specific servers or server groups, allowing independent management and scaling of different data types without increasing overall system complexity. This segmentation enables modular architecture where each server handles a specific subset of computational tasks.
Solution Approach 2:
The patent introduces a distributed shared memory system as an intermediary layer between processing partitions and servers. This intermediary abstracts the complexity of data distribution and synchronization, providing a unified memory access interface while enabling parallel processing across multiple servers. The distributed shared memory manages data replication and consistency automatically, reducing the management burden on individual servers.
2Reliability
If data objects are processed in a centralized manner, then data consistency is maintained, but processing latency and bottlenecks increase
Solution Approach 1:
The patent divides the centralized processing architecture into multiple distributed processing partitions, each handling specific data object types independently. This segmentation allows parallel processing of different data types simultaneously, reducing overall processing latency while maintaining data consistency within each partition through localized management protocols.
Solution Approach 2:
The patent implements a distributed shared memory system that creates replicated copies of data objects across multiple servers. Each processing partition maintains local copies of the data objects it needs to process, eliminating the need for frequent centralized data access. This copying approach reduces processing latency while consistency is maintained through coordinated replication protocols.
3Productivity
If computational units are organized by data object types, then processing efficiency is improved, but system architecture complexity increases
Solution Approach 1:
The patent segments the system architecture into processing partitions organized by data object types (characters, items, locations, etc.). Each partition contains specialized processing logic optimized for its specific data type, improving processing efficiency through targeted algorithms and data structures. The segmentation is managed through automated provisioning that reduces the perceived complexity for system operators.
Solution Approach 2:
The patent creates a universal processing framework that can handle multiple data object types through a common architecture. The distributed shared memory system and partition management mechanisms provide multi-functional capabilities that work across different data types, reducing architecture complexity by avoiding the need for completely separate systems for each data type.
4Adaptability or versatility
If multiple servers are used to distribute computational load, then system scalability is improved, but data synchronization and coordination difficulty increase
Solution Approach 1:
The patent introduces a distributed shared memory system as an intermediary that manages data synchronization and coordination across multiple servers. This intermediary provides automated replication, consistency management, and conflict resolution protocols, enabling scalable system growth without proportionally increasing coordination complexity. The distributed shared memory abstracts the synchronization challenges from individual processing partitions.
Solution Approach 2:
The patent segments the coordination and synchronization tasks into partition-specific management routines rather than requiring global coordination across all servers. Each processing partition manages its own data objects independently, reducing the coordination overhead. This segmented approach allows servers to be added or removed from specific partitions without affecting the entire system.
Data Source
AI summary
Technology is described for computationally intensive distributed computing systems and methods. A method for using object types to distribute processing may include determining object types for data objects. A mapping between the data objects and processing partitions may be determined using the object types. A processing partition may be associated with a processing application that processes data objects of a given object type. A hardware host in a distributed computing system may be identified to process the data objects of the given object type, which are mapped to the processing partition. The processing partition may be sent to the hardware host to process the data objects, and the hardware host may include an instance of the processing application to process the data objects in the processing partition.