Abstract
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.
Highlights
A Multi-Agent System (MAS) is usually understood as a system composed of interacting autonomous agents
This research aims at making a contribution towards the non-functional characteristics Performance of agents in a MAS by considering the negotiation character of agents
We presented a methodology to model the negotiation between the agents and predicting the performance of the system
Summary
A Multi-Agent System (MAS) is usually understood as a system composed of interacting autonomous agents. The important characteristics of the agents which distinguish it from an object are Autonomous, Cooperation, Goal oriented, Adaptability, Mobility, Negotiation etc. Many articles on MAS have been mainly concerned with functional characteristics such as coordination, rationality and knowledge modeling. The nonfunctional characteristics have the equal importance as the functional characteristics for any software system [1-5]. This research aims at making a contribution towards the non-functional characteristics Performance of agents in a MAS by considering the negotiation character of agents. In SPE, system does not exist so it is not possible to develop the work load parameters from measurement data. Models of the system are used to collect the data required to predict the performance. The different data required for the SPE approach are Workload scenarios, Performance goals, Software Design concepts, Execution environment and Resource usage estimates [5-7]
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