Jongsuk Ruth Lee

On credibility of simulation studies of telecommunication networks

In telecommunication networks, as in many other areas of science and engineering, the proliferation of computers as research tools has resulted in the adoption of computer simulation as the most commonly used paradigm of scientific investigations. This, together with a plethora of existing simulation languages and packages, has created a popular opinion that simulation is mainly an exercise in computer programming. In new computing environments, programming can be minimized, or even fully replaced, by the manipulation of icons (representing prebuilt programming objects containing basic functional blocks of simulated systems) on a computer monitor. One can say that we have witnessed another success of modern science and technology: the emergence of wonderful and powerful tools for exploring and predicting the behavior of such complex stochastic dynamic systems as telecommunication networks. But this enthusiasm is not shared by all researchers in this area. An opinion is spreading that one cannot rely on the majority of the published results on performance evaluation studies of telecommunication networks based on stochastic simulation, since they lack credibility. Indeed, the spread of this phenomenon is so wide that one can speak about a deep crisis of credibility. In this article this claim is supported by the results of a survey of over 2200 publications on telecommunication networks in proceedings of IEEE INFOCOM and such journals as IEEE Transactions on Communications, IEEE/ACM Transactions on Networking, and Performance Evaluation Journal. The discussion focuses on two important necessary conditions of a credible simulation study: use of appropriate pseudo-random generators of independent uniformly distributed numbers, and appropriate analysis of simulation output data. Having considered their perils and pitfalls, we formulate guidelines that, if observed, could help to ensure a basic level of credibility of simulation studies of telecommunication networks.

Comparison of various estimators in simulated FGN

Abstract The Hurst parameter is the simplest numerical characteristic of self-similar long-range dependent stochastic processes. Such processes have been identified in many natural and man-made systems. In particular, since they were discovered in the Internet and other multimedia telecommunication networks a decade ago, they have been the subject of numerous investigations. Typical quantitative assessment of self-similarity and long-range dependency, begins with the estimation of the Hurst parameter H. There have been a number of techniques proposed for this. This paper reports results of a comparative analysis of the six most frequently used estimators of H. To set up a credible framework for this, the minimal acceptable sample size is first determined. The Hurst parameter estimators are then compared for bias and variance. Our experimental results have confirmed that the Abry–Veitch Daubechies Wavelet-Based (DWB) and the Whittle ML (Maximum Likelihood) estimators of H are the least biased. However, the latter has significantly smaller variance and can be applied to shorter data samples than the Abry–Veitch DWB estimator. On the other hand, the Abry–Veitch DWB estimator is computationally simpler and faster than the Whittle ML estimator.

GAIS: grid advanced information service based on P2P mechanism

In this paper, we propose an information service component of Moredream which we refer to as GAIS, for providing advanced functionalities and more plentiful resource information in order to satisfy the requirements for the various applications in the K/sup */Grid computing environment characterized by both high performance and high throughput computing. In particular, GAIS includes interfaces and functionalities for the dynamic VO management, universal VO discovery, service categorization and efficient information providers. Consequently, we anticipate that our Grid information service be broadly utilized through the improved service qualities.

Study on Big Data Center Traffic Management Based on the Separation of Large-Scale Data Stream

The network of traditional data center has been usually designed and constructed for the provision of users equal access of data centres resource or data. Therefore, network administrators have a strong tendency to manage user traffic from the viewpoint that the traffic has a similar size and characteristics. But, the emersion of big data begins to make data centers have to deal with 1015 byte-data transfer at once. Such a big data transfer can cause problems in network traffic management in the existed data center. And, the tiered network architecture of the legacy data center magnifies the magnitude of the problems. One of the well-known big data in science is from large hadron collider such as LHC in Swiss CERN. CERN LHC generates multi-peta byte data per year. From our experience of CERN data service, this paper showed the impact of network traffic affected by large-scale data stream using NS2 simulation, and then, suggested the evolution direction based on separating of large-scale data stream for the big data centers network architecture.

ATMSim: An anomaly teletraffic detection measurement analysis simulator

Over the last few years, the quantity of teletraffic is rapidly growing because of the explosive increase of Internet users and its applications. The needs of collection, storage, management, analysis, and measurement of the subsequent teletraffic have emerged as some of the very important issues. To this point many studies for detecting anomaly teletraffic have been done. Detection, measurement, and analysis studies for traffic data, however, are not actively being made based on Hadoop. In this paper, some problems and solutions for those systems have been suggested. We have also designed and developed an Anomaly Teletraffic detection Measurement analysis Simulator, called the ATMSim. One strong point of the ATMSim is able to store, measure, and analyze traffic data for detecting anomaly teletraffic. The other strength is to generate sequences of input synthetic anomaly teletraffic with various network attacks for practical network security applications. All simulations were executed under the control of the ATMSim simulator to investigate how input anomaly teletraffic with network attacks can be different from real Ethernet local area network (LAN) traffic. Our numerical results show that the values of the estimated Hurst parameter obtained from the anomaly teletraffic are much higher when compared to real Ethernet LAN traffic.

Distributed steady-state simulation of telecommunication networks with self-similar teletraffic

Abstract Recent measurement studies of teletraffic data in modern telecommunication networks have shown that self-similar processes may provide better models of teletraffic than Poisson processes. If this is not taken into account, it can lead to inaccurate conclusions about performance of telecommunication networks. We show how arrival processes with self-similar input influences the run-length of a distributed steady-state simulation of queueing systems in telecommunication networks. For this purpose, the simulation run-length of SSM/M/1/∞ queueing systems in the method based on the batch means, conducted for estimating steady-state mean waiting times is compared with the results obtained from simulations of M/M/1/∞ queueing systems when a single processor and multiple processors are used. We also investigate speedup conducted stochastic simulation of SSM/M/1/∞ queueing systems on multiple processors under a scenario of distributed stochastic simulation known as MRIP (Multiple Replications In Parallel) in a local area network (LAN) environment on Solaris operating system. We show that, assuming self-similar inter-event processes (i.e., SSM/M/1/∞ queueing systems), many more observations are required to obtain the final simulation results with a required precision, as the value of the Hurst parameter H increases, than when assuming Poisson models, exhibiting short-range dependence (i.e., M/M/1/∞ queueing systems) on a single processor and multiple processors. Our results show that the time for collecting many numbers of observations under the MRIP scenario is clearly reduced as traffic intensity and the value of the Hurst parameter increase, and as the engaged processor increases one to four. In particular, the value of H influences much more the speedup than traffic intensity and the engaged processor.

Self-Similar Properties of Spam

We often receive unwanted information from a variety of electronic systems mainly through emails, electronic boards and messengers, called spam. Spam is the use of electronic messaging systems to send unsolicited bulk messages indiscriminately. Widely varying estimates of the cost associated with spam are available in the literature. However, a stochastic and quantitative analysis of the determinant characteristics of spam traffic is still an open problem. This work fills this gap. A 4-year data sample of real-time inbound traffic between May 2005 and July 2009 was collected to investigate and analyze characteristics of spam traffic through JIRANSOFTs Spam Sniper on the network at Korean Bible University. Our major findings of a statistical analysis of spam traffic are that (i) real-time inbound spam traffic is statistically more correlated (self-similar) when compared to normal traffic, and (ii) the degree of self-similarity measured in terms of the Hurst parameter H and obtained from different estimation techniques is very high.

GRASP: a grid resource allocation system based on OGSA

In this paper, we describe GRASP, a grid resource allocation system based on OGSA. In order to submit job to the grid resources in more efficient and convenient manner, we support some features for user-friendly resource allocation such as resource brokering, scheduling, monitoring, and so forth. GRASP supports any scientific applications with the high performance computing features such as MPI and applications with high throughput computing features such as parameter studies.

Detecting Anomaly Teletraffic Using Stochastic Self-Similarity Based on Hadoop

In recent years, the quantity of teletraffic is rapidly growing because of the explosive increase of Internet users and its applications. The needs of collection, storage, management, analysis, and measurement of the subsequent teletraffic have been emerged as one of very important issues. So far many studies for detecting anomaly teletraffic have been done. However, measurement and analysis studies for big data in cloud computing environments are not actively being made based on Hadoop. Thus, this paper presents for detecting anomaly teletraffic using stochastic self-similarity based on Hadoop. All simulations are conducted under control of our proposed platform, called ATM tool, for anomaly teletraffic intrusion detection system on Hadoop. Our numerical results show that the values of the estimated Hurst parameter obtained from the anomaly teletraffic are much higher when compared to ordinary local area network traffic.

Suggestions of efficient self-similar generators

The growth of Grid computing and the Internet has been exponential in recent years. These high-speed communication networks have had a tremendous impact on our civilisation. High-speed communication networks offer a wide range of applications, such as multimedia and data intensive applications, which differ significantly in their traffic characteristics and performance requirements. Many analytical studies have shown that self-similar network traffic can have a detrimental impact on network performance, including amplified queueing delays and packet loss rates in broadband wide area networks. Thus, full understanding of the self-similar nature in teletraffic engineering is an important issue. This paper presents a detailed survey of self-similar generators proposed for generating sequential and fixed-length selfsimilar pseudo-random sequences for simulation in communication networks. We evaluate and compare the operational properties of the fixed-length and sequential generators of self-similar pseudo-random sequences. The statistical accuracy and time required to produce long sequences are discussed theoretically and studied experimentally. The evaluation of the generators concentrated on two aspects: (i) how accurately self-similar processes can be generated (assuming a given mean, variance and self-similarity parameter H), and (ii) how quickly the generators can generate long self-similar sequences. Overall, our results have revealed that the fastest and most accurate generators of the six sequential and five fixed-length sequence generators considered are the SRP-FGN, FFT and FGN-DW methods. � 2006 Elsevier B.V. All rights reserved.