Multi-Agent System for Irregular Parallel Genetic Computations

Momot, J.; Kosacki, K.; Grochowski, Marek; Uhruski, Piotr; Schaefer, Robert

doi:10.1007/978-3-540-24688-6_81

Cited by 16 publications

(23 citation statements)

References 3 publications

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“…From the very beginning, the Octopus development was driven by particular computational needs (see [9,10]). Therefore it features a set of components that help building up an application which benefits from agent-based processing.…”

Section: Building Octopus-based Applicationsmentioning

confidence: 99%

“…by using the virtual topology parameters) to support a certain application with particular runtime characteristics, but the platform itself provides only basic mechanisms to run the application agents. Secondly, the agent application layer supports readymade Smart Solid agents (see [9]) that implement required environment integration functionalities. Therefore the actual application implementation is done solely in the upper layer, which requires the embedded tasks to be partitionable, meaning the agents actually divide overall computational task into smaller sub-processes.…”

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confidence: 99%

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Octopus - Computation Agents Environment

Uhruski¹,

Grochowski²,

Schaefer³

2006

Int. Artif.

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This paper presents a platform named Octopus that facilitates the building and execution of mobile agent based applications. It presents the key ideas of how agents embed the computational task and how they cooperate to find the solution. The Octopus is presented with its key mechanisms used to sustain and execute the agents. The cornerstones of the Octopus design are described in detail, giving readers an overview on how to implement a computation problem within the platform. Finally, actual application examples are shown with a short discussion of the Octopus based implementation properties.Keywords: mobile agents, migration, computational tasks, local scheduling. The Structure of Agent Computational SystemParallel computing systems based on distributed network resources become the most powerful tools in high performance computing. To effectively utilize often heterogeneous network resources, a universal solution allowing for the easy design, implementation, deployment and finally control of the application is required. We propose an agent based computing system with two layer architecture that clearly divides such system required functionalities. The upper layer is the application while the lower is a platform sustaining agents -the Octopus. Multi Agent ApplicationThis layer provides the means and tools to wrap the computational task into agents. The layer is build from agents, but the agents themself are further decomposed into two sub-layers -a shell and an embedded task (see [10]). A task is the particular problem with the data required for computations. The task should have the ability to divide itself, but that is a natural property in parallel computing. The outer agent is a task container -it requires its execution environment to provide load information (including RAM and CPU utilization) to compute a local scheduling policy and autonomously take the decision to continue internal task computing or migrate to find better resources. Agents require the execution environment to let them communicate with each other for cooperation. Agents Execution PlatformThe application agents require an environment to execute them. The specific requirements for this layer originated from CAE computing and mainly included minimum runtime overhead with maximum performance. The platform provides the minimal set of functionalities clearly required by large computation problems, but these have to be performing well. Thus we did not require a universal solution that would fit any agent based applications. The platform has to be well scal-

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Section: Building Octopus-based Applicationsmentioning

confidence: 99%

mentioning

confidence: 99%

Octopus - Computation Agents Environment

Uhruski¹,

Grochowski²,

Schaefer³

2006

Int. Artif.

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show abstract

“…[6]) we propose the policy that consists in on-demand task partitioning and task remaping obtained by the dynamic agent creation and migration. It was implemented and initially tested for the regularly and irregularly concurrent computations (see [2,1,5,7]). The brief report of the test results is stressed in the section 6.…”

Section: Motivationmentioning

confidence: 99%

“…The results cover two major problem domains examined in course of our experiments: Mesh Generator (MG) and Hierarchic Genetic Strategy (HGS). The presented tests are broadly described in [5,7] while papers [1,2] contain the MAS implementation details. Computations were run in the same physical environment with LAN of 30 to 50 PCs connected with 100MBit network.…”

Section: Numerical Testsmentioning

confidence: 99%

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The Dynamics of Computing Agent Systems

Smółka

Uhruski

Schaefer

et al. 2005

Lecture Notes in Computer Science

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Abstract. The paper presents the Multi Agent System (MAS) designed for the large scale parallel computations. The special kind of diffusionbased scheduling enables to decompose and allocate the migrable computing agents basing only of the local information. The paper introduces the formal model of the MAS under consideration in order to depict the roles of agent behavior and the whole system dynamics. The optimal scheduling problem for MAS as well as the way of its verification was presented in terms of such model. The brief report of the test results is stressed in the section 6. MotivationThe Multi-Agent System (MAS) governed by the local diffusion scheduling is a reasonable alternative for the centrally governed distributed computing systems. Although the idea of mobile task diffusion is well known since more then ten years (see. e.g. [6]) we propose the policy that consists in on-demand task partitioning and task remaping obtained by the dynamic agent creation and migration. It was implemented and initially tested for the regularly and irregularly concurrent computations (see [2,1,5,7]). The brief report of the test results is stressed in the section 6. The paper introduces the formal model of the MAS under consideration in order to depict the roles of agent behavior and the whole system dynamics. The optimal scheduling problem for MAS as well as the way of its verification was presented in terms of such model. Formal Description of the ArchitectureThe MAS under consideration that allows the diffusion governed scheduling is a collection of: a computational environment (MAS platform) and a computing application composed of mobile agents called Smart Solid Agents (SSA). The computational environment is a triple (N, B H , perf), where: N = {P 1 , . . . , P n } , where P i is the Virtual Computation Node (VCN). Each VCN can maintain more than one agent (the number of hardware processors usage is not relevant in our assumptions).

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The Autonomous Concurrent Strategy for Large Scale CAE Computation

Uhruski

Toporkiewicz

Schaefer

et al. 2006

Computational Science – ICCS 2006

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The paper presents the Agent-Oriented technology for running the parallel CAE computation. Fast and effective distributed diffusion scheduling is available that minimizes computation and communication time necessary for task governing and provides transparency in resource availability. Detailed evaluation of the diffusion rule parameters was obtained in the course of analysis of computational, memory and communicational complexity of CAE tasks.

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Multi-Agent System for Irregular Parallel Genetic Computations

Cited by 16 publications

References 3 publications

Octopus - Computation Agents Environment

Octopus - Computation Agents Environment

The Dynamics of Computing Agent Systems

The Autonomous Concurrent Strategy for Large Scale CAE Computation

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