Routing Path Determination Using QoS Metrics and Priority Based Evolutionary Optimization

Kumar, Divya; Kashyap, Divya; Mishra, K. K.; Mishra, Alekha Kumar

doi:10.1109/hpcc.2011.87

Cited by 9 publications

(5 citation statements)

References 19 publications

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“…A priority based evolutionary multi-objective optimization has been presented in [23] for estimating the optimized routes for data transfer. Different QoS parameters have been optimized for establishing the whole system.…”

Section: Qos Based Approachesmentioning

confidence: 99%

A Review on Evolutionary Multiobjective Optimization for Routing Problem of Computer Networks

Kaur¹,

Shukla²

2013

Confluence 2013: The Next Generation Information Technology Summit (4th International Conference)

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Evolutionary Algorithms are the stochastic optimization methods, simulating the behavior of natural evolution. These algorithms are basically population based search procedures efficiently dealing with complex search spaces having robust and powerful search mechanism. EAs are highly applicable in multiobjective optimization problem which are having conflicting objectives. Resource optimization in computer network is an important research problem and it can be easily handled by the use of multiobjective optimization approach. This paper reviews the work carried out for optimization in network routing.

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Section: Qos Based Approachesmentioning

confidence: 99%

A Review on Evolutionary Multiobjective Optimization for Routing Problem of Computer Networks

Kaur¹,

Shukla²

2013

Confluence 2013: The Next Generation Information Technology Summit (4th International Conference)

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“…Kumar et al [2011] use EA to construct a network path considering path reliability, network bandwidth, and end-to-end delay for different applications. Huang and Nahrstedt [2012] use EA to search for an optimal playout buffering size.…”

Section: Related Workmentioning

confidence: 99%

“…Evolutionary algorithms (EAs) have emerged as powerful heuristic tools for solving NP-complete and NP-hard problems and have received a great deal of attention because of their ability to solve multiobjective optimizations in many areas such as network routing [Kumar et al 2011;Xiang et al 1999] and real-time playout scheduling [Huang and Nahrstedt 2012]. We adopt genetic algorithm (GA) (a genre of evolutionary algorithms) as a heuristic algorithm to solve the priority-based multiobjective optimization problem for 3DTI multisite and multistream content distribution.…”

Section: Priority-based Multiobjective Session Optimizationmentioning

confidence: 99%

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Osm

Arefin

Rivas

Nahrstedt

2014

ACM Trans. Multimedia Comput. Commun. Appl.

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Different 3D tele-immersive (3DTI) activities pose different prioritized requirements for application and network-level quality of service (QoS) to ensure a strong quality of experience (QoE) for participants. Some applications put heavy weight on audio quality, some consider higher quality for upper body video streams, and some seek very low end-to-end interactivity delay. In addition, a variation in streaming content may arise in the 3DTI space due to the participants' change in interests (e.g., view change). Therefore, there is a need for an adaptive multistream, multisite 3DTI session management strategy that is not only unobtrusive, but also optimizes prioritized QoS parameters in 3DTI content distribution based on user activity and content variation. To address this next generation session management problem, we revisit the design space of multistream and multisite 3DTI session layer. We present an evolutionary 3DTI session optimization approach using Open Session Management (OSM) architecture that uses a global view of participants and overlays network conditions to optimize QoS parameters. Experimental results with PlanetLab traces show that our optimization process is unobtrusive, and the optimized TI sessions provide higher satisfaction to the participants (in some cases up to 50% higher) compared to the current solutions in the 3DTI space.

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“…These nature inspired algorithms are typically applied over optimization problems which can't be tackled through conventional mathematics. It is observed that EAs are good in approximating the best solutions in various engineering applications [33], [34]. That too, Evolutionary Algorithms are available in many variants [35] and this gives the user the power to choose algorithm which suits the problem.…”

Section: Introductionmentioning

confidence: 98%

Incorporating logic in Artificial Bee Colony (ABC) algorithm to solve first order logic problems: The logical ABC

Kumar

Mishra

2015

2015 7th International Conference on Knowledge and Smart Technology (KST)

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The fascination for generating reasons and drawing inferences has given a tremendous impetus to research in theoretical computer science. In spite of having well defined constructs and globally accepted notations for logic and Firstorder theorem provers, theorem proving is still a semidecidable problem having exponential time complexity. On the other hand swarm intelligence is a swiftly growing research area for solving optimization problems. This paper presents a novel approach for automated theorem proving using metaheuristics. In the present research we have tried to combine these two entirely different zones of computer science, i.e. meta-heuristics and concrete logic via modeling theorem provers as an optimization problem in a sound practical manner. Also we have experimentally shown how to automate first order reasoning using Artificial Bee Colony algorithm on a sample problem expressed in First-order predicate calculus.

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Routing Path Determination Using QoS Metrics and Priority Based Evolutionary Optimization

Cited by 9 publications

References 19 publications

A Review on Evolutionary Multiobjective Optimization for Routing Problem of Computer Networks

A Review on Evolutionary Multiobjective Optimization for Routing Problem of Computer Networks

Osm

Incorporating logic in Artificial Bee Colony (ABC) algorithm to solve first order logic problems: The logical ABC

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