A hybrid end-to-end QOS architecture for heterogeneous networks (like the global information grid)

Doshi, Bharat T.; Benmohamed, Lotfi; DeSimone, A.

doi:10.1109/milcom.2005.1606078

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…In MAAG, each application has an Importance Level (IL) defined by organizational policy and can assumed as QoS class of the application [7] . Table 2 depicts steps of negotiation, scheduling and reservation in an AMA.…”

Section: D) Application Main Agent (Ama)mentioning

confidence: 99%

“…In the MAAG, each service is managed independently by an intelligent agent. The agent manages requests, negotiates with service consumers, performs advanced reservation [4][5][6][7][8][9][10][11][12][13][14][15][16] and measures QoS metrics of all instances of a grid service in a specific grid site. Also each grid application has an intelligent agent who is responsible for application execution respecting to the QoS constraints defined by its owner.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Agent Architecture for QoS Support in Grid Environment

Rezaee¹,

Rahmani²,

Parsa³

et al. 2008

J. of Computer Science

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Grid computing is emerged as a new distributed computing technology that implements flexible and coordinated resource sharing among dynamic, heterogeneous, unpredictable and geographically distributed collections of resources owned by different individuals and organizations. Due to heterogeneous and dynamic nature of the grid, resource management and application scheduling is a complex undertaking. This paper proposed a multi-agent architecture that addressed resource management and application execution with support for Quality of Services (QoS) in grid environment. Five types of collaborative intelligent and mobile agents proposed to manage grid resources and applications in a decentralized, autonomous and intelligent manner. In the proposed architecture, negotiation, advanced reservation and QoS measurements handled by collaborative intelligent agents. Resources and applications are managed independently with respect to their defined policies. The simulation results showed that the multi-agent architecture is practical, flexible and effective.

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Section: D) Application Main Agent (Ama)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-Agent Architecture for QoS Support in Grid Environment

Rezaee¹,

Rahmani²,

Parsa³

et al. 2008

J. of Computer Science

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“…Today's adhoc networks support a diverse set of services that require different priorities and different allocations of bandwidth for traffic delivery. P2P topologies are scalable to meet the needs of hundreds, thousands, and even tens of thousands of users [5].…”

Section: Related Workmentioning

confidence: 99%

Management and Service Discovery in Satellite and Avionic Networks

Sproull

Lockwood

Meier

2007

2007 IEEE Aerospace Conference

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Command and control services manage networkattached assets deployed in distributed systems that can be separated by thousands of miles. Networks that rely on satellite communications to transit all data to a centralized control center are troubled by high latency due to long propagation delays to satellites and limited data transit over bandwidth constrained links. Low latency communications can be achieved by using a combination of distributed airborne and space-based systems. This research investigates how deployment of a Peer-to-Peer (P2P) overlay network in a region of conflict can reduce the latency for real time control and communication. This overlay network utilizes a hybrid of both satellite and aircraft links to provide services that best satisfy the immediate needs of ground units. Experiments have been performed with an emulation testbed using 147 compute nodes in the Emulab testbed to study the latency and throughput of the overlay network. The overlay network is developed using a Peer-to-Peer Application Programmers Interface (API) called JXTA. Nodes simulate resources requesting and offering several types of video and data services.

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“…Each incoming request has its own Importance Level (IL) [2] which simply identifies its QoS class. The smaller IL means the higher importance.…”

mentioning

confidence: 99%

A Fuzzy-Based Adaptive Agent for Grid Services

Rezaee

Rahmani

Adabi

2008

2008 IEEE Asia-Pacific Services Computing Conference

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In service oriented Grids; everything is represented by a service. Computational resources, storage resources, programs, and so forth are all services and thus managing the services in a proper manner is an important issue. in this article an intelligent and adaptive agent for managing Grid services is presented named FAGS. The FAGS manages service consumers advanced reservation requests, schedules service instances, monitors the instances, and manages service factory. The main objective of the proposed agent is balancing between service instances utilization and service consumers QoS delivery in respect of their QoS class. As usually these parameters are in conflict with each other, the agent continuously monitors system status and uses its novel fuzzy-based algorithms to make proper decisions with aim to adapt with latest conditions. The simulation results confirm the validity of the proposed agent.

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A hybrid end-to-end QOS architecture for heterogeneous networks (like the global information grid)

Cited by 7 publications

References 4 publications

A Multi-Agent Architecture for QoS Support in Grid Environment

A Multi-Agent Architecture for QoS Support in Grid Environment

Management and Service Discovery in Satellite and Avionic Networks

A Fuzzy-Based Adaptive Agent for Grid Services

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