Mobile Agents based Distributed Data Mining
Introduction
A software agent is essentially a piece of code or an entity which has some task to perform. A mobile agent is a software agent that has the advantage of motion. Currently an agent’s features are inherent from the language in which they are implemented. We try to build an agent such that it possesses all the characteristics that are unique to each agent and gauge its performance.
Related work
There has already been numerous mobile agents developed over time. The mobile agent platforms we have considered are aglets, voyager, grasshopper, tryllian, Jade, tracy and springs. The reason for this being that, grasshopper, aglets and voyager were given very good ranking. The others have been chosen as they are more recent and hence relevant today. Each agent competes with other agents but lag in some other vital areas.
Aglets:
They were developed by IBM in 1997. it follows MASIF specification. It is based on single thread model and communication is largely based on message passing. Synchronous and asynchronous messaging is supported. Proxies are used for abstraction to refer to remote agents.
Though aglets is probably the most popular mobile agent platform, it has some demerits. In single threaded model may lead to deadlock during synchronous messaging. Dynamic proxies are not supported and the user has to obtain an updated proxy.
Voyager:
It was developed initially by ObjectSpace in 1997 and currently by Recursion software. It simplifies the management of remote communications of traditional protocols. It supports dynamic generation of CORBA proxies, mobile codes, mobile agents. Location transparency by forwarding chains of proxies is provided, the voyager is a commercial product and it is not available for free. Tracking the mobile agents using forward chains is not so efficient considering the traversal to locate the agent or a weak link.
Grasshopper:
It was developed by IKV ++ in 1999... [continues]
Introduction
A software agent is essentially a piece of code or an entity which has some task to perform. A mobile agent is a software agent that has the advantage of motion. Currently an agent’s features are inherent from the language in which they are implemented. We try to build an agent such that it possesses all the characteristics that are unique to each agent and gauge its performance.
Related work
There has already been numerous mobile agents developed over time. The mobile agent platforms we have considered are aglets, voyager, grasshopper, tryllian, Jade, tracy and springs. The reason for this being that, grasshopper, aglets and voyager were given very good ranking. The others have been chosen as they are more recent and hence relevant today. Each agent competes with other agents but lag in some other vital areas.
Aglets:
They were developed by IBM in 1997. it follows MASIF specification. It is based on single thread model and communication is largely based on message passing. Synchronous and asynchronous messaging is supported. Proxies are used for abstraction to refer to remote agents.
Though aglets is probably the most popular mobile agent platform, it has some demerits. In single threaded model may lead to deadlock during synchronous messaging. Dynamic proxies are not supported and the user has to obtain an updated proxy.
Voyager:
It was developed initially by ObjectSpace in 1997 and currently by Recursion software. It simplifies the management of remote communications of traditional protocols. It supports dynamic generation of CORBA proxies, mobile codes, mobile agents. Location transparency by forwarding chains of proxies is provided, the voyager is a commercial product and it is not available for free. Tracking the mobile agents using forward chains is not so efficient considering the traversal to locate the agent or a weak link.
Grasshopper:
It was developed by IKV ++ in 1999... [continues]
Cite This Essay
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"Mobile Agents Based Distributed Data Mining." StudyMode.com. 11, 2009. Accessed 11, 2009. http://www.studymode.com/essays/Mobile-Agents-Based-Distributed-Data-Mining-253602.html.
