Early Performance Prediction of Co-Operative Multi-Agent Systems

Ajitha, S.; Kumar, T. V. Suresh; Geetha, D. Evangelin; Rajanikanth, K.

doi:10.1016/j.proeng.2012.06.354

Cited by 3 publications

(1 citation statement)

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“…The performance prediction of agents by considering the cooperation character of agent the authors have published different articles in [8][9][10][11]. In the early development stages measuring the negotiation workload and predicting the performance remains an important but largely unsolved problem.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Negotiation of Agents in MAS and Predicting the Performance an SPE Approach

Santhakumari

2019

IJCAI

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Software performance engineering(SPE) process starts at the early stages of software development life cycle which helps to develop software that meets the performance requirements on time and budget. Multi-Agent Systems(MAS) are comprised of one or more agents who coordinate each other to accomplish some task. The coordination can be achieved through cooperation and negotiation. In the early development stages measuring the negotiation workload and predicting the performance remains an important but largely unsolved problem. The problem of uncertainty regarding the negotiation workload is required to be addressed by estimation techniques. Hence, in this research we developed a probabilistic model for the negotiation scenario among the agents in a given time horizon. The negotiation workload obtained from the probabilistic model is integrated with the representative workload of the agents for predicting the performance of agents in MAS. The tool SMTQA is used for obtaining the performance metrics. Analysis of the execution environment is done by considering various configurations in the hardware resources based on the dynamic workload of the negotiation agents over a time horizon. From the sensitivity analysis, the bottleneck resources are identified and suggestions for improvement are proposed.Povzetek: Predstavljena je izvirna metoda za ocenjevanje in napovedovanje delovanja večagentnih sistemov.

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Section: Introductionmentioning

confidence: 99%