• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.636-638
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• 2010

Task scheduling is an integral part of parallel and distributed computing. Extensive research has been conducted in this area leading to significant theoretical and practical results. Stochastic reward nets (SRN) is an extension of stochastic Petri nets and provides compact modeling facilities for system analysis. In this paper, we address task scheduling model using extended timed marked graph, which is a special case of SRNs. And we analyze this model by giving reward measures in SRN.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2828-2835
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• 2015

In this paper, we analyse the effect of redundancy types and non-stop forwarding scheme on network service availability. We use stochastic reward net models as enabling modeling approach for the analytical evaluation. We first design stochastic reward nets for redundancy models with or without non-stop forwarding and then evaluate their availability using Stochastic Petri Net Package.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.45-52
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• 2009

In this paper, we develop queueing network models of communication networks with reliability model considering link failures. The reliability of a communication network with a virtual connection exposed to link failures is analyzed. Stochastic Reward Nets (SRN) is an extension of stochastic Petri nets and provides compact modeling facilities for system analysis. To get the performance index, appropriate reward rates are assigned to its SRN. It is shown that SRN modeling is well suited to specify, automatically generate and solve for reliability under rerouting. Markov models using SRN are developed and solved to depict various rerouting caused by link failures and reliability analysis in communication networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2634-2639
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• 2014

The idea of redundancy is used in order to improve the availability of networks and systems and there are various methods for implementing redundancy. To perform the availability analysis various stochastic models have been used. In this paper, 2N redundancy with one active service unit and one standby service unit is considered. To evaluate the expected availability, we model 2N redundancy using Stochastic Reward Nets. This model can be solved using the SPNP package.

• Journal of Korea Multimedia Society
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• v.5 no.6
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• pp.704-711
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• 2002

Multimedia communication systems are characterized by supporting three different typer of services such as circuit switched services, and packet switched real Lime and non real time services. The wireless channels in a cell ate allocated by calls of these different service classes and the different service requirements have to be met. SRN is an extension of stochastic Petri nets and provides compact Modeling facilities for system analysis. To get the performance index, appropriate reward rates are assigned to its SRN. In this paper, we present a SRN model for performance analysis of channel allocation of multimedia mobile communication systems. The key contribution of this paper constitutes the Petri nets modeling techniques instead of complicate numerical analysis of Markov chains and easy way of performance analysis for channel allocations under SRN rewards concepts.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.479-485
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• 2013

Virtualization is a promising approach to consolidating multiple online services onto a smaller number of computing resources. A virtualized server environment allows computing resources to be shared among multiple performance isolated platforms called virtual machines. Through server virtualization software, applications servers are encapsulated into VMs, and deployed with APIs on top generalized pools of CPU and memory resources. Networking and security have been moved to a software abstraction layer that transformed computing, network virtualization. And it paves the way for enterprise to rapidly deploy networking and security for any application by creating the virtual network. Stochastic reward net (SRN) is an extension of stochastic Petri nets which provides compact modeling facilities for system analysis. In this paper, we develop SRN model of network virtualization based on virtual switch. Measures of interest such as switching delay and throughput are considered. These measures are expressed in terms of the expected values of reward rate functions for SRNs. Numerical results are obtained according to the virtual switch capacity and number of active VMs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.355-358
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• 2007

In wireless mobile communication systems, priority of voice service through high speed data and multimedia transmission requires increased service diversification. Research is being carried out in this environment, on the call admission control techniques to guarantee the diversified service's QoS. SRN (Stochastic Reward Net) is an extended version of Petri nets, well know modeling and analysis tool. In this paper, we develop SRN call admission control model considering the 4 classes of services in the 4th generation IEE 802.16e mobile communication Technology.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.6
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• pp.1032-1037
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• 2006

The channel is one of the precious and limited resources in wireless networks. There are many researches on the channel management. Recently, the optimization problem of guard channels has been an important issue. In this paper, we propose an intelligent channel management technique based on the neural networks. An SRN channel allocation model is developed to generate the learning data for the neural networks and the performance analysis of system. In the proposed technique, the neural network is trained to generate optimal guard channel number g, using backpropagation supervised learning algorithm. The optimal g is computed using the neural network and compared to the g computed by the SRM model. The numerical results show that the difference between the value of 8 by backpropagation and that value by SRM model is ignorable.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.39 no.12
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• pp.529-538
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• 2002

Conventional method to reduce the handoff call blocking probability(PBH) in mobile communication system is to reserve a predetermined number of channels only for handoff calls. To determine the number of reserved channels, an optimization problem, which is generally computationally heavily involved, must be solved. In this Paper, we propose a call admission control (CAC) scheme that can be used to reduce the PBH without reserving channels in advance. For this, we define a new measure, gain, which depends on the state of the system upon the arrival of a new call. The proposed CAC decision rule relies on the gain computed when a new call arrives. SRN, an extended stochastic Petri nets, provides compact modeling facilities for system analysis can be calculated performance index by appropriate reward to the model. In this Paper, we develop SRN models which can perform the CAC with gain. The SRN models are 2 level hierarchical models. The upper layer models are the structure state model representing the CAC and channel allocation methods considering QoS with multimedia traffic The lower layer model Is to compute the gain under the state of the upper layer models.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.115-119
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• 2006

The channel is one of the precious and limited resources in wireless networks. There are many researches on the channel management. Recently, the optimization problem of guard channels has been an important issue. In this paper, we propose an intelligent channel management technique based on the neural networks. An SRN channel alteration model is developed to generate the learning data for the neural networks and the performance analysis of system. In the proposed technique, the neural network is trained to generate optimal guard channel number g, using backpropagation supervised learning algorithm. The optimal g is computed using the neural network and compared to the g computed by the SRN model. The numerical results show that the difference between the value of g by backpropagation and that value by SRN model is ignorable.