Distributed Computing Task Segmentation in Secure Containers
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Solution Overview
Problem
Current computing systems face challenges in efficiently distributing computing tasks across multiple nodes while ensuring security and scalability, particularly in scenarios where resources are not always actively utilized, leading to inefficiencies and potential security breaches.
Innovation Solution
A computer-implemented method and system that identifies and assigns computing tasks to secure and isolated containers on registered nodes, allowing for distributed task execution while ensuring security and scalability by utilizing a pool of registered nodes, which can be either public or private, and managing resources through a broker device that provides additional computing resources on demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If computing tasks are distributed across multiple nodes, then computing capacity and scalability are improved, but security risks and system complexity increase
Solution Approach 1:
The patent segments computing tasks into separate containers that are distributed across multiple nodes. Each container is isolated and contains specific task components, allowing the system to achieve high computing capacity through distribution while maintaining security through container isolation. The segmentation principle directly addresses the contradiction by enabling task distribution without compromising security.
Solution Approach 2:
The patent introduces a broker as an intermediary component that manages communication between requesting devices and assigned nodes. The broker handles task distribution, container management, and coordination, thereby reducing the complexity of direct node-to-node communication and enhancing security through centralized control of the distributed system.
2Adaptability or versatility
If computing tasks are distributed across multiple nodes, then computing capacity and scalability are improved, but device complexity and management overhead increase
Solution Approach 1:
The patent employs universal containers that can execute various computing tasks across different node types. The containers are designed to be multi-functional and adaptable to different hardware configurations, reducing the complexity of managing diverse nodes. The broker also serves multiple functions including task distribution, monitoring, and coordination, thereby simplifying overall system management.
Solution Approach 2:
The broker acts as a central intermediary that abstracts the complexity of distributed node management from individual components. It handles task routing, resource allocation, and coordination between containers and nodes, thereby reducing the operational complexity of the distributed system while maintaining high computing capacity.
3Reliability
If nodes are kept online to provide computing resources, then availability and service quality are improved, but energy consumption and resource idle time increase
Solution Approach 1:
The patent implements dynamic resource allocation where nodes are assigned and unassigned based on actual task demands. The system can dynamically adjust the number of active nodes and container configurations to match workload requirements, thereby maintaining high availability when needed while reducing energy consumption during low-demand periods. This dynamic approach resolves the contradiction between availability and energy efficiency.
4Object-affected harmful factors
If computing tasks are isolated in secure containers, then security and isolation are improved, but resource utilization efficiency may decrease
Solution Approach 1:
The patent segments resources into isolated containers that can be efficiently allocated and deallocated. This segmentation enables secure isolation while maintaining the ability to consolidate and share resources across multiple containers and nodes. The segmentation allows for fine-grained resource management that optimizes utilization without compromising security.
Solution Approach 2:
The patent employs parameter changes in container configuration and resource allocation to optimize the balance between security isolation and resource utilization efficiency. By dynamically adjusting container parameters such as resource limits, memory allocation, and CPU priorities, the system can maximize resource sharing while maintaining secure isolation boundaries.
Data Source
AI summary
The present specification describes a computer-implemented method. According to the method, a request is received to execute a computing task. The request includes parameters for the computing task. A processor identifies, based on the parameters for the computing task and from a pool of registered nodes, a set of assigned nodes amongst which the computing task is to be distributed. A computing assignment of the computing task is transmitted to a secure and isolated container on each of the assigned nodes. A completed computing assignment is received from each of the assigned nodes and the completed computing task is assembled and distributed to a requesting device.