Task management has always been a key issue in collective computing, including task decomposition, distribution, execution and results integration, but there is little research on task decomposition. In order to improve multi-tasks execution efficiency and promote the full utilization of collective resources, a task decomposition model based on extended task-tree is proposed in this paper. Meanwhile, a series of pruning and reorganization algorithms are proposed, and the performance of the algorithms is analyzed and evaluated. Experiments verify that the proposed algorithms outperform traditional methods, and prove that the practicality and efficiency of the strategy.

A Multi-task Decomposition and Reorganization Scheme for Collective Computing Using Extended Task-Tree

Gregory D. Abowd shows a new vision of computer framework - collective computing. In this framework, kinds of remote computing devices, including even people who are regarded as a kind of computing device, are connected with each other into a group to complete a complex work. Therefore, the various computing devices with the different computing capacities can be fully used in different tasks. However, most of the relevant researches focus on improving infrastructure to address specific functions, the heterogeneous tasks performed in a common architecture and the large-scale integration are not paid enough attention. This paper presents a collective computing architecture for supporting concurrent multi-sourced heterogeneous tasks. The whole architecture is layered to provide different functions and obtain extensibility, loads balance, centralized dispatch and low delay communication. The extendible collective computing engine is used for analysing and allocating heterogeneous tasks, and the distributed device management controls heterogeneous computing devices. This architecture provides a common infrastructure for processing heterogeneous tasks by heterogeneous devices which dose not only design for some specialized systems or functions. At last, we implement a prototype system by this architecture for proving that the architecture can perform multi-sourced heterogeneous tasks well.

An Extendable Layered Architecture for Collective Computing to Support Concurrent Multi-sourced Heterogeneous Tasks

Abowd shows a new vision of computer framework - collective computing. In this case, kinds of remote computing devices including people who is regarded as a kind of computing devices connect with each other in a group for completing a complexed work. Therefore, the computing capacity of the various computing devices is fully exploited in different tasks. However, most of the current researches focus on the dedicated system, the heterogeneous tasks and computing devices performance in the infrastructure is not paid enough attentions. This paper presents a collective computing architecture that supports heterogeneous tasks and computing devices, which uses a series of centralized managers for analysing and distributing tasks and controlling heterogeneous computing devices. The whole architecture is layered in order to obtain loads balance, centralized dispatch and low delay communication. This architecture provides a common infrastructure for processing heterogeneous tasks by heterogeneous devices but not for some specialized systems or functions. At last, we implement a prototype system by virtual computers and android phones for proving that the architecture can use heterogeneous devices to perform heterogeneous tasks well.

A Collective Computing Architecture Supporting Heterogeneous Tasks and Computing Devices

As the ability of a single agent is limited while information and resources in multi-agent systems are distributed, cooperation is necessary for agents to accomplish a complex task. In the open and changeable environment on the Internet, it is of great significance to research a system flexible and capable in dynamic evolution that can find a collaboration method for agents which can be used in dynamic evolution process. With such a method, agents accomplish tasks for an overall target and at the same time, the collaborative relationship of agents can be adjusted with the change of environment. A method of task decomposition and collaboration of agents by improved contract net protocol is introduced. Finally, analysis on the result of the experiments is performed to verify the improved contract net protocol can greatly increase the efficiency of communication and collaboration in multi-agent system.

Man Zhang

Papers

Collaboration strategy for software dynamic evolution of multi-agent system