Extension Of Stochastic Petri Nets Research Articles

Modeling and analysis of memory virtualization in cloud computing

Virtualization is a promising approach to consolidating multiple online services onto a smaller number of computing resources. The adoption of virtualization and mappings between physical and virtual resources can further exacerbate challenges. Stochastic reward net (SRN) is an extension of stochastic Petri nets which provides compact modeling facilities for system analysis. In this paper, we address SRN hierarchical modeling of memory virtualization in cloud computing. Memory overcommit techniques such as ballooning, page swapping, and sharing, are applied and modeled for virtualization as a lower-level model. We propose three memory management policies, combination of ballooning and swapping intended for busy virtual memory (VMEM), to increase virtual memory utilization. These policies are handled by using guard functions of transition in SRN. The hypervisor with these policies can relinquish the memory pages of not so busy VMEM and let the busy VMEM get the pages. Memory availability with failure related behavior is modeled and analyzed as a upper-level model. We combine above two level models together and get performability measures. The upper-level model is the structure state model representing the state of the system with regard to failures and repairs of memory pages (banks). There are $$i$$ i active memory pages in progress. Each state of $$i$$ i is assigned a reward rate equal to the memory utilization from the lower-level performance model. The lower-level model captures the performance of the system, especially several memory utilization measures, within a given structure state. Measures of interest are considered as follows: memory utilization for allocated and actually used memory, memory availability with memory failure/repair behavior. These measures are expressed in terms of the expected values of reward rate functions for SRNs.

Reliability Modeling and Evaluation of Electric Vehicle Motor by Using Fault Tree and Extended Stochastic Petri Nets

Performing reliability analysis of electric vehicle motor has an important impact on its safety. To do so, this paper proposes its reliability modeling and evaluation issues of electric vehicle motor by using fault tree (FT) and extended stochastic Petri nets (ESPN). Based on the concepts of FT and ESPN, an FT based ESPN model for reliability analysis is obtained. In addition, the reliability calculation method is introduced and this work designs a hybrid intelligent algorithm integrating stochastic simulation and NN, namely, NN based simulation algorithm, to solve it. Finally, taking an electric vehicle motor as an example, its reliability modeling and evaluation issues are analyzed. The results illustrate the proposed models and the effectiveness of proposed algorithms. Moreover, the results reported in this work could be useful for the designers of electric vehicle motor, particularly, in the process of redesigning the electric vehicle motor and scheduling its reliability growth plan.

클라우드 시스템의 가상 스위치 모델링

Abstract 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.

Analysis Task Scheduling Models based on Hierarchical Timed Marked Graph

Task scheduling is an integrated component of computing with the emergence of grid computing. In this paper, we address two different task scheduling models, which are static Round-Robin (RR) and dynamic Fastest Site First (FSF) task scheduling method, using extended timed marked graphs, which is a special case of Stochastic Petri Nets (SPN). Stochastic reward nets (SRN) is an extension of SPN and provides compact modeling facilities for system analysis. We build hierarchical SRN models to compare two task scheduling methods. The upper level model simulates task scheduling and the lower level model implements task serving process for different sites with multiple servers. We compare these two models and analyze their performances by giving reward measures in SRN.

Performability Analysis of Token Ring Networks using Hierarchical Modeling

It is important for communication networks to possess the capability to overcome failures and provide survivable services. We address modeling and analysis of performability affected by both performance and availability of system components for a token ring network under failure and repair conditions. Stochastic reward nets (SRN) is an extension of stochastic Petri nets and provides compact modeling facilities for system analysis. In this paper, hierarchical SRN modeling techniques are used to overcome state largeness problem. The upper level model is used to compute availability and the lower level model captures the performance. And Normalized Throughput Loss (NTL) is obtained for the composite ring network for each node failures occurrence as a performability measure. One of the key contributions of this paper constitutes the Petri nets modeling techniques instead of complicate numerical analysis of Markov chains and easy way of performability analysis for a token ring network under SRN reward concepts.

재경로 설정을 고려한 통신망의 신뢰도 분석 모델링

본 논문에서는 통신망의 큐잉망 모델을 링크 고장을 고려한 신뢰도 모델과 함께 개발한다. 가상연결을 갖는 통신망에서 링크 고장시의 신뢰도 분석이 수행된다. 확장된 추계적 페트리 네트인 SRN은 시스템 분석을 위한 다양한 모델링 기능을 제공해 주며 적절한 보상율을 모델에 할당해 줌으로써 원하는 성능지표를 쉽게 구할 수 있다. 재경로를 갖는 신뢰도 분석을 위하여 모델 규격을 정하고 자동으로 모델을 생성하고 그 해를 구하는데 SRN 모델링이 적합함을 보여준다. 링크 고장에 따른 다양한 재경로 설정과 신뢰도 분석을 위하여 SRN을 이용한 마르코프 모델이 개발되고 그 해가 구해진다. 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.

Stochastic Petri Nets Modeling Methods of Channel Allocation in Wireless Networks

To obtain realistic performance measures for wireless networks, one should consider changes in performance due to failure related behavior. In performability analysis, simultaneous consideration is given to both pure performance and performance with failure measures. SRN is an extension of stochastic Petri nets and provides compact modeling facilities for system analysis. In this paper, a new methodology to model and analyze performability based on stochastic reward nets (SRN) is presented. Composite performance and availability SRN models for wireless handoff schemes are developed and then these models are decomposed hierarchically. The SRN models can yield measures of interest such as blocking and dropping probabilities. These measures are expressed in terms of the expected values of reward rate functions for SRNs. Numerical results show the accuracy of the hierarchical model. 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 allocation under SRN reward concepts.

MODELING AND ANALYSIS OF RECONFIGURABLE MANUFACTURING SYSTEM BY EXTENDED STOCHASTIC PETRI NETS

Reconfigurable manufacturing system(RMS)em- phasizes quick reaction to market changes,whose components are reconfignrable structures.The modeling and analysis of such system are extremely difficult to develop.A modular modeling approach based extended stochastic Petri nets(ESPN)is pre- sented,which facilitates the reconfigurable processing model- ing and adapts the arbitrary distributions manufacturing system accurately.The RMS model is described by connecting a set of basic modules that are designed with respect to different re- sources and reconfignrable processing of operations;Their connection method is also provided.Furthermore,the analysis of ESPN model based on behavior expression is presented,and the performance measures of RMC are computed such as pro- duction rates versus the number of standby machines,part type mix ratio,and rump-up time.In addition,the relationship func- tion of performance can be easily obtained without the reach- ability graph of Petri nets.Thus,the analysis process is simple and straightforward.An example ofa DC motor reconfignrable production line is presented to illustrate the effectivity of the methodology.

Comparison of Hybrid Systems and Fluid Stochastic PetriNets

Hybrid Systems are models of interacting digital and continuous devices with applications in the control of aircraft, computers, or modern cars for instance. Concurrently, Fluid Stochastic Petri Nets (FSPNs) have been introduced as an extension of stochastic Petri nets to model physical systems that contain continuous fluid quantities controlled by discrete logic, as well as a means to deal with the possible state explosion of discrete models. Of course, an FSPN is by definition a hybrid system but in this paper, we compare a general hybrid systems formalism with FSPN formalism. The conclusion of this paper, suplemented by some examples, is that fluid stochastic Petri nets and hybrid system formalism can be converted from one to the other. Although there is a price to pay for such a conversion, the conversion can transfer the solution methods from one to the other and thus help the development of both formalisms.

Analysis of the stabilizing effect of Rom on the genetic network controlling ColE1 plasmid replication.

A stochastic model of ColE1 plasmid replication is presented. It is implemented by using UltraSAN, a simulation tool based on an extension of stochastic Petri nets (SPNs). It allows an exploration of the variation in plasmid number per bacterium, which is not possible using a deterministic model. In particular, the rate at which plasmid-free bacteria arise during bacterial division is explored in some detail since spontaneous plasmid loss is a widely observed empirical phenomenon. The rate of spontaneous plasmid loss provides an evolutionary explanation for the maintainance of Rom protein. The presence of Rom acts to reduce variance in plasmid copy number, thereby reducing the rate of plasmid loss at bacterial division. The ability of stochastic models to link biochemical function with evolutionary considerations is discussed.

A moment generating function based approach for evaluating extended stochastic Petri Nets

A moment-generating-function (MGF)-based approach for performance analysis of extended stochastic Petri nets (ESPNs) is presented. The method integrates Petri nets, MGF and stochastic network concepts, and Mason's rule into a tool for evaluating various discrete-event dynamic systems. The ESPNs are modeled, given the specification of a system. Then, the state machine PN is derived, the transfer functions based on the MGFs of the related transitions are found, the network is reduced to a single transition with its transfer function for each performance measure, and system performance is calculated. Firing delays of transitions in ESPNs can be either deterministic or stochastic with an extended distribution. Three fundamental structures that can be reduced into a single transition are discussed. The machine-repairman model with a buffer is given as an example to illustrate the method for evaluating performance parameters. >