RT‐JADE: A preemptive real‐time scheduling middleware for mobile agents

Abstract

Summary Mobile agents are examples of distributed systems which may dispute for the same resources on their hosts. Treating such concurrency adequately is essential, particularly in real‐time applications. Due to intrinsic time restrictions, mobile agents in real‐time environments are only considered successful if they fulfill their mission by respecting their deadlines. Scheduling algorithms with different policies can be applied in these scenarios. However, the efficiency of these algorithms may deviate according to the missions and deadlines of the mobile agents. Also, these algorithms can be preemptive, or calculate the order of executions without interrupting an ongoing task. In this paper, we propose a middleware extension to the JADE platform that brings real‐time scheduling support with preemption to mobile agents. The proposed solution uses best effort scheduling policy in the context of soft real‐time applications. We evaluate the performance of the scheduling algorithms, with and without preemption, and the impact of the selected algorithms on mission fulfillment. The results of the proposed middleware showed a great improvement on mission accomplishment when compared to the FIFO algorithm provided by the JADE platform.

Intelligent agents in biomedical engineering: a systematic review

“…Distributed Artificial Intelligence (DAI) is the system class that allows for various processes called agents to interact, distributing themselves logically or spatially, being autonomous and smart; it is also the intersection between Distributed Computing and Artificial Intelligence (AI). 1 From an architectural point of view, intelligent agents (IAg) are entities composed of a unique identifier and three main components: code, data and status, in addition to having a life cycle associated with their state of execution. 1,2 In a Multi Agent System (MAS), it is possible to delegate different tasks to different agents, where a larger problem is divided into small sub problems, and each agent produces an output in accordance with its mission (task), where, later, all outputs are joined and converted into the final answer to the total problem.…”

“…1 From an architectural point of view, intelligent agents (IAg) are entities composed of a unique identifier and three main components: code, data and status, in addition to having a life cycle associated with their state of execution. 1,2 In a Multi Agent System (MAS), it is possible to delegate different tasks to different agents, where a larger problem is divided into small sub problems, and each agent produces an output in accordance with its mission (task), where, later, all outputs are joined and converted into the final answer to the total problem. [1][2][3][4] In addition, agents can interact with each other and share common data in order to increase the speed of problem resolution.…”

“…1,2 In a Multi Agent System (MAS), it is possible to delegate different tasks to different agents, where a larger problem is divided into small sub problems, and each agent produces an output in accordance with its mission (task), where, later, all outputs are joined and converted into the final answer to the total problem. [1][2][3][4] In addition, agents can interact with each other and share common data in order to increase the speed of problem resolution. As for the characteristics, a given IAg can be classified according to the system architecture (Open or Closed Code), the knowledge representation, its mobility (Static and Mobile Agents), its intelligence mechanisms (AI, ontology, etc.…”

“…[2][3][4] Static (or stationary) agents can only be executed on the machine they were started on, and cannot perform tasks remotely, even though they have the ability to interact with remote agents. 1,5 Mobile agents are those which can migrate from one device to another, taking data and status with them, being able to perform tasks locally and remotely. Mobile agents can migrate between MAS hosts to use their resources locally.…”

Avaliação Da Prontidão Da Organização De Ait De Satélites Artificiais Para O Atendimento De Requisitos De Seus Stakeholders

“…IoT, também conhecida no Brasil como Internet das Coisas, introduz o conceito de transformar objetos físicos em parte de um Sistema Distribuído, com o objetivo de colaborarem entre si e com outros dispositivos da rede em realizar tarefas de maneira mais robusta e ágil(HASSANALIERAGH et al, 2015).No contexto medicinal, o custo elevado no cuidado com a saúde, o aumento da população idosa e das doenças crônicas no mundo demandam urgência em mudar o atual cenário centrado em hospitais para um cenário centrado no paciente(HASSANALIERAGH et al, 2015)(HIREMATH et al, 2014. Para tal fim, pesquisadores do ramo da engenharia biomédica não têm medido esforços para Do ponto de vista arquitetônico, agentes são entidades compostas por um identificador único e três componentes principais: código, dados e estado, além de possuírem um ciclo de vida associado ao seu estado de execução(FILGUEIRAS et al, 2018). Os agentes interagem entre si, formando o que se denomina de Sistema MultiAgente (MAS) e compartilham dados comuns, a fim de aumentar a velocidade da resolução do problema.Além disso, eles cooperam entre si, dividindo a resolução de diferentes subproblemas, ou podem decidir, autonomamente, por aplicar diferentes estratégias de resolução a uma mesma tarefa.…”