Mobile agent-based approach for efficient network management and resource allocation: framework and applications

Papavassiliou, Symeon; Puliafito, Antonio; Tomarchio, Orazio; Ye, J.

doi:10.1109/jsac.2002.1003050

Cited by 42 publications

(19 citation statements)

References 15 publications

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“…Papavassiliou et al [21] presents an implementation framework for the development of effective market-based routes for brokering purposes in the future multioperator network marketplace. On this platform, a genetic algorithm is used to identify optimal resource allocation strategies and the agent-based network management approach is used to represent an underlying structure for the multioperator network model and facilitate the collection and dissemination of the required management data.…”

Section: Computational Paradigm Using Mobile Agentmentioning

confidence: 99%

On computing mobile agent routes for data fusion in distributed sensor networks

Rao

Barhen

et al. 2004

IEEE Trans. Knowl. Data Eng.

240

175

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Abstract-The problem of computing a route for a mobile agent that incrementally fuses the data as it visits the nodes in a distributed sensor network is considered. The order of nodes visited along the route has a significant impact on the quality and cost of fused data, which, in turn, impacts the main objective of the sensor network, such as target classification or tracking. We present a simplified analytical model for a distributed sensor network and formulate the route computation problem in terms of maximizing an objective function, which is directly proportional to the received signal strength and inversely proportional to the path loss and energy consumption. We show this problem to be NP-complete and propose a genetic algorithm to compute an approximate solution by suitably employing a two-level encoding scheme and genetic operators tailored to the objective function. We present simulation results for networks with different node sizes and sensor distributions, which demonstrate the superior performance of our algorithm over two existing heuristics, namely, local closest first and global closest first methods.

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Section: Computational Paradigm Using Mobile Agentmentioning

confidence: 99%

On computing mobile agent routes for data fusion in distributed sensor networks

Rao

Barhen

et al. 2004

IEEE Trans. Knowl. Data Eng.

240

175

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“…We applied the simple Erlang loss formula to get the expected number of calls in the system. The expected number of rows in the control table is given by: (3) where (4) Equation (3) is derived as the equilibrium-state probability solution to the M/M/m/m queueing system. This system is modeled by a birth-death process with birth rate (i.e.…”

Section: -Expected Number Of Rows In Thementioning

confidence: 99%

“…[2][3][4] Recently, several mechanisms have been proposed for end-to-end monitoring and control in the literature. Over the last few years, the mobile agent/code approach 2 has achieved a widespread interest to decentralize network management tasks.…”

Section: Introductionmentioning

confidence: 99%

In-service QoS monitoring of real-time applications using SM MIB

Choi

Hwang

2005

Int. J. Network Mgmt.

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Current network management needs an end-to-end overview of various flows rather than the information that is purely local to the individual devices. The typical manager-centric polling approach, however, is not suitable to understand network-wide behavior of a large-scale Internet. In this paper, we propose a new management information base (MIB) approach called Service Monitoring MIB (SM MIB). The MIB provides a network manager with dynamic end-to-end management information by utilizing special packets. The special packet is an Internet control message protocol (ICMP) application that is sent to a remote network element to monitor Internet services. The SM MIB makes an end-to-end management feasible while it reduces management-related traffic and manager-tomanager interactions. Real examples show that the proposed SM MIB is useful for end-to-end QoS monitoring. We discuss the accuracy of the obtained data as well as the monitoring overhead.

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“…[85] presents an efficient framework for network management and resource allocation. The approach performs collection of information about network state, network devices management through monitoring of health functions (packet loss, bandwidth utilized, buffers utilized, reliability, errors, etc.…”

Section: Network Managementmentioning

confidence: 99%