Optimizing Bandwidth Usage and Response Time Using Lightweight Agents on Data Communication Network

Olajubu, Emmanuel Ajayi; Aderounmu, G. A.; Adagunodo, Emmanuel R.

doi:10.1007/978-1-4020-8737-0_60

Cited by 2 publications

(2 citation statements)

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“…The authors of [9] opined that these agents are entities with the ability to sense their immediate environment and embark on computational task in the environment. A global intelligence comes out as these agents communicate locally among themselves.…”

Section: Lightweight Agentsmentioning

confidence: 99%

“…The agent or process assignment is to find the sequence of tours so that the overall distance traveled or cost of travelling is minimized. This is synonymous to mobile agent routing system of [9], but different from routing technique developed in [12]. Our model calculates the cost between two nodes on computer networks during simulation time rather than using routing table used in [13] which is prone to memory consumption and which may consequently slowdown the system.…”

Section: Routing Modelmentioning

confidence: 99%

See 1 more Smart Citation

Lightweight Agents, Intelligent Mobile Agent and RPC Schemes: A Comparative Analysis

Olajubu

Aderounmu

Ajayi

2011

CIT

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This paper presents the performance comparison of Lightweight Agents, Single Mobile Intelligent Agents and Remote Procedure Call which are tools for implementing communication in a distributed computing environment. Routing algorithms for each scheme is modeled based on TSP. The performance comparison among the three schemes is based on bandwidth overhead with retransmission, system throughput and system latency. The mathematical model for each performance metric is presented, from which mathematical model is derived for each scheme for comparison. The simulation results show that the LWAs has better performance than the other two schemes in terms of small bandwidth retransmission overhead, high system throughput and low system latency. The Bernoulli random variable is used to model the failure rate of the simulated network which is assumed to have probability of success p = 85% and the probability of failure q = 15%. The network availability is realized by multiplicative pseudorandom number generator during the simulation. The results of simulation are presented.

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Section: Lightweight Agentsmentioning

confidence: 99%