Autonomous Mobile Agents for Intent-Based Grid Task Execution
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Solution Overview
Problem
Current Grid computing technologies face challenges in specifying tasks and efficiently executing complex tasks using heterogeneous resources, requiring significant human intervention and lacking a universal task specification language and scalable execution mechanisms.
Innovation Solution
The development of a Web Services-based Grid architecture with an advanced task specification and execution platform, utilizing a unified ontology language and autonomous mobile agents for intent-based task specification and execution, along with a secure decision-making framework to manage tasks and resources within the Semantic Grid framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a universal task specification language is introduced, then task specification capability is improved, but system complexity increases
Solution Approach 1:
The patent introduces a universal task specification language that can describe different kinds of problems and map to various Grid services. This language serves multiple functions: it specifies user tasks, translates them into service invocations, and handles heterogeneous resources uniformly, thereby improving adaptability without proportionally increasing complexity
Solution Approach 2:
The patent employs an intermediary layer consisting of task specification language and service mapping mechanisms. This intermediary translates high-level user task descriptions into executable service invocations, shielding users from Grid complexity while enabling universal task specification across different domains
2Adaptability or versatility
If heterogeneous resources are used to execute tasks, then resource utilization is improved, but task execution complexity increases
Solution Approach 1:
The patent changes the parameters of task execution by introducing semantic descriptions and ontologies that standardize how heterogeneous resources are described and selected. By transforming resource heterogeneity into standardized semantic parameters, the system can utilize diverse resources while maintaining execution simplicity through uniform selection criteria
Solution Approach 2:
The patent uses virtualization and abstraction layers that create virtual copies of resource interfaces. Heterogeneous physical resources are mapped to standardized virtual resource representations, allowing uniform task execution across diverse hardware while hiding the underlying complexity through consistent virtual interfaces
3Extent of automation
If autonomous mobile agents are used for task execution, then automation level is improved, but agent management complexity increases
Solution Approach 1:
The patent segments the autonomous agent into modular functional components: task analysis module, service selection module, execution monitoring module, and delegation module. This segmentation allows each component to operate independently with well-defined interfaces, improving automation while reducing overall management complexity through modular architecture
Solution Approach 2:
The autonomous mobile agent performs self-service functions including autonomous task analysis, service discovery, resource selection, and execution monitoring without human intervention. The agent manages its own lifecycle and coordinates sub-tasks independently, achieving high automation while minimizing the complexity of centralized agent management
4Productivity
If sub-tasks are generated and executed separately, then scalability is improved, but coordination complexity increases
Solution Approach 1:
The patent introduces dynamic task decomposition where the generation and execution of sub-tasks adapts to runtime conditions. The system can dynamically adjust the number, type, and coordination of sub-tasks based on resource availability and task requirements, enabling scalability while managing coordination complexity through adaptive rather than fixed coordination mechanisms
Solution Approach 2:
The patent employs a coordination intermediary layer that manages communication and synchronization between separate sub-tasks. This intermediary handles task dependency tracking, result aggregation, and coordination protocol execution, allowing scalable distributed task execution while abstracting the coordination complexity from the individual sub-tasks
Data Source
AI summary
A Grid application framework uses semantic languages to describe the tasks and resources used to complete them. A Grid application execution framework comprises a plurality of mobile agents operable to execute one or more tasks described in an intent based task specification language, Input/Output circuitry operable to receive input that describes a task in the task specification language, an analysis engine for generating a solution to the described task, and an intent knowledge base operable to store information contained within tasks of the plurality of mobile agents.