multi-agent based integration of scheduling algorithms EMBA论文

d id of an algorithm means agents can response the scheduling model.

4) reason machine

if there are several algorithms in the algorithms base that are suited with the scheduling model, selects the best one according to capability and efficiency under the support of the rule base.

5) computing cell

finishing computation with selected algorithm.

6) sensor

receives information such as jobs, resources and scheduling models from the manager and responds with bidding or not.

7) driver

outputs result.

in order to harmonize computing agents, we need a manager. it’s a special agent as shown in fig.2 and is responsible for following functions:

1) registers each computing agent with register model and stores their properties in database.

2) searches information from environment through the sensor and translates it into appropriate scheduling model in the modeler under the support of the knowledge base.

3) communicates with computing agents via communicator.

4) records and analyzes middle result in blackboard and then outputs final result via driver.

we’ll present the reciprocity between the manager and the computing agents in 3.2.

3.2 system architecture

thousands of scheduling algorithms has been proposed so far. these scheduling algorithms can be classified into several categories. the hierarchy is shown in fig. 3:

of course we can not and need not design agents for each algorithm. but we can do that for each class. our solution is to joint different classes of computing agents into a mass to realize dynamic integration of scheduling algorithms. except for a manager, every node of the system is a computing agent, which provides congener algorithms that store in its algorithm base. a starlike architecture of the system is shown in fig.4.

fig.4 is only one example of multi-agent based integration of scheduling algorithms system. we may choose either two or more computing agents to build a mass according to the requir

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