Determination of the itinerary of imprecise mobile agents using an adaptive approach

Rech, Luciana; Montez, Carlos; Cecin, F.R.; Geyer, Cláudio F. R.

doi:10.1109/etfa.2008.4638560

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…As the MA in the next mission uses the same behavior (decision making) and considering that it is a myopic MA [10], this heuristics goal is to find the "optimal percentage", that will allow the MA to reach the maximum level of QoS, respecting deadline of the current mission and spending the minimal number of trips (consecutive missions) possible. For example, using an adjustment factor of 10%, the MA in its third trip, if it has not found the optimal point, it can perform only 80% of the resources of the mission.…”

Section: Greedy Heuristic With Variable Decrease At Start (Greedy-vd)mentioning

confidence: 99%

Decentralized Services Orchestration Using Intelligent Mobile Agents with Deadline Restrictions

Magalhaes

Lung

Rech

2010

IFIP Advances in Information and Communication Technology

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The necessity for better performance drives service orchestration towards decentralization. There is a recent approach where the integrator -that traditionally centralizes all corporative services and business logics -remains as a repository of interface services, but now lacks to know all business logics and business workflows. There are several techniques using this recent approach, including hybrid solutions, peer-to-peer solutions and trigger-based mechanisms. A more flexible approach regarding environment configuration and not fully explored in services orchestration technology is the use of intelligent mobile agents to execute it. In this paper, we present new adaptive heuristics for mobile agents to execute the decentralization of orchestration through missions (services) that correspond to the stages of business flow, with the ability to trade-off the quality of the result with the deadline of the mission. Some test case scenarios are presented and collected data are analyzed pointing the advantages and disadvantages of each heuristic.

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Section: Greedy Heuristic With Variable Decrease At Start (Greedy-vd)mentioning

confidence: 99%

Decentralized Services Orchestration Using Intelligent Mobile Agents with Deadline Restrictions

Magalhaes

Lung

Rech

2010

IFIP Advances in Information and Communication Technology

Self Cite

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“…Heuristic LG (Lazy-Greedy) is described and evaluated in [5]. The originally chosen behaviour will remain the sole agent behaviour to the end of the mission.…”

Section: Description Of the Adaptive Heuristicsmentioning

confidence: 99%

“…The definition of adaptive heuristics uses some of the simple heuristics that are described in detail in [4] and evaluated in [5]. We briefly present the decision making of each simple heuristic, showing the necessary information for understanding the description of adaptive heuristics:…”

Section: Description Of the Adaptive Heuristicsmentioning

confidence: 99%

“…From this assessment of performance it was clear the need to create heuristics that are adapted to the needs of the mobile agent. The first step toward this behaviour was the creation of adaptive heuristics able to decide the best behaviour to be used for the mission [5]. Based on a log of previous executions, the mobile agent is able of selecting the most appropriate behaviour for the current mission.…”

Section: Introductionmentioning

confidence: 99%

“…Based on a log of previous executions, the mobile agent is able of selecting the most appropriate behaviour for the current mission. In [5], adaptation happens on startup, that is, the agent has always the same behaviour throughout the mission.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using intelligent mobile agents to dynamically determine itineraries with time constraints

Magalhaes

Rech

Lung

2010

2010 IEEE 15th Conference on Emerging Technologies &Amp; Factory Automation (ETFA 2010)

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Algorithms to determine the itinerary of intelligent agents are fundamental in the context of distributed applications based on mobile agents with time constraints. In order to establish the agent itinerary it is necessary to consider the trade-offs between high-quality of results and meeting the deadline. In this paper, we describe and evaluate adaptive heuristics able to choose the best behaviour to be used for decision making in the itinerary definition. This decision-making is based on a log of benefits collected by the mobile agent in past executions. The paper's objective is to propose a new heuristic able to realize the change in the environment and adapt itself dynamically, changing its behaviour in order to meet the deadline of the mission and to achieve the greatest possible benefit.

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Deadline missing predictor based on aperiodic server queue length for distributed systems

Plentz¹,

Montez²

2008

Computer Communications

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Determination of the itinerary of imprecise mobile agents using an adaptive approach

Cited by 4 publications

References 17 publications

Decentralized Services Orchestration Using Intelligent Mobile Agents with Deadline Restrictions

Decentralized Services Orchestration Using Intelligent Mobile Agents with Deadline Restrictions

Using intelligent mobile agents to dynamically determine itineraries with time constraints

Deadline missing predictor based on aperiodic server queue length for distributed systems

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