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- Genre: Doctoral Dissertation
- Creators: Tepedelenlioğlu, Cihan
Description
The research on the topology and dynamics of complex networks is one of the most focused area in complex system science. The goals are to structure our understanding of the real-world social, economical, technological, and biological systems in the aspect of networks consisting a large number of interacting units and to develop corresponding detection, prediction, and control strategies. In this highly interdisciplinary field, my research mainly concentrates on universal estimation schemes, physical controllability, as well as mechanisms behind extreme events and cascading failure for complex networked systems.
Revealing the underlying structure and dynamics of complex networked systems from observed data without of any specific prior information is of fundamental importance to science, engineering, and society. We articulate a Markov network based model, the sparse dynamical Boltzmann machine (SDBM), as a universal network structural estimator and dynamics approximator based on techniques including compressive sensing and K-means algorithm. It recovers the network structure of the original system and predicts its short-term or even long-term dynamical behavior for a large variety of representative dynamical processes on model and real-world complex networks.
One of the most challenging problems in complex dynamical systems is to control complex networks.
Upon finding that the energy required to approach a target state with reasonable precision
is often unbearably large, and the energy of controlling a set of networks with similar structural properties follows a fat-tail distribution, we identify fundamental structural ``short boards'' that play a dominant role in the enormous energy and offer a theoretical interpretation for the fat-tail distribution and simple strategies to significantly reduce the energy.
Extreme events and cascading failure, a type of collective behavior in complex networked systems, often have catastrophic consequences. Utilizing transportation and evolutionary game dynamics as prototypical
settings, we investigate the emergence of extreme events in simplex complex networks, mobile ad-hoc networks and multi-layer interdependent networks. A striking resonance-like phenomenon and the emergence of global-scale cascading breakdown are discovered. We derive analytic theories to understand the mechanism of
control at a quantitative level and articulate cost-effective control schemes to significantly suppress extreme events and the cascading process.
Revealing the underlying structure and dynamics of complex networked systems from observed data without of any specific prior information is of fundamental importance to science, engineering, and society. We articulate a Markov network based model, the sparse dynamical Boltzmann machine (SDBM), as a universal network structural estimator and dynamics approximator based on techniques including compressive sensing and K-means algorithm. It recovers the network structure of the original system and predicts its short-term or even long-term dynamical behavior for a large variety of representative dynamical processes on model and real-world complex networks.
One of the most challenging problems in complex dynamical systems is to control complex networks.
Upon finding that the energy required to approach a target state with reasonable precision
is often unbearably large, and the energy of controlling a set of networks with similar structural properties follows a fat-tail distribution, we identify fundamental structural ``short boards'' that play a dominant role in the enormous energy and offer a theoretical interpretation for the fat-tail distribution and simple strategies to significantly reduce the energy.
Extreme events and cascading failure, a type of collective behavior in complex networked systems, often have catastrophic consequences. Utilizing transportation and evolutionary game dynamics as prototypical
settings, we investigate the emergence of extreme events in simplex complex networks, mobile ad-hoc networks and multi-layer interdependent networks. A striking resonance-like phenomenon and the emergence of global-scale cascading breakdown are discovered. We derive analytic theories to understand the mechanism of
control at a quantitative level and articulate cost-effective control schemes to significantly suppress extreme events and the cascading process.
ContributorsChen, Yuzhong (Author) / Lai, Ying-Cheng (Thesis advisor) / Spanias, Andreas (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Ying, Lei (Committee member) / Arizona State University (Publisher)
Created2016
Description
Access Networks provide the backbone to the Internet connecting the end-users to
the core network thus forming the most important segment for connectivity. Access
Networks have multiple physical layer medium ranging from fiber cables, to DSL links
and Wireless nodes, creating practically-used hybrid access networks. We explore the
hybrid access network at the Medium ACcess (MAC) Layer which receives packets
segregated as data and control packets, thus providing the needed decoupling of data
and control plane. We utilize the Software Defined Networking (SDN) principle of
centralized processing with segregated data and control plane to further extend the
usability of our algorithms. This dissertation introduces novel techniques in Dynamic
Bandwidth allocation, control message scheduling policy, flow control techniques and
Grouping techniques to provide improved performance in Hybrid Passive Optical Networks (PON) such as PON-xDSL, FiWi etc. Finally, we study the different types of
software defined algorithms in access networks and describe the various open challenges and research directions.
the core network thus forming the most important segment for connectivity. Access
Networks have multiple physical layer medium ranging from fiber cables, to DSL links
and Wireless nodes, creating practically-used hybrid access networks. We explore the
hybrid access network at the Medium ACcess (MAC) Layer which receives packets
segregated as data and control packets, thus providing the needed decoupling of data
and control plane. We utilize the Software Defined Networking (SDN) principle of
centralized processing with segregated data and control plane to further extend the
usability of our algorithms. This dissertation introduces novel techniques in Dynamic
Bandwidth allocation, control message scheduling policy, flow control techniques and
Grouping techniques to provide improved performance in Hybrid Passive Optical Networks (PON) such as PON-xDSL, FiWi etc. Finally, we study the different types of
software defined algorithms in access networks and describe the various open challenges and research directions.
ContributorsMercian, Anu (Author) / Reisslein, Martin (Thesis advisor) / McGarry, Michael P (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2015
Description
I propose a new communications scheme where signature signals are used to carry digital data by suitably modulating the signal parameters with information bits. One possible application for the proposed scheme is in underwater acoustic (UWA) communications; with this motivation, I demonstrate how it can be applied in UWA communications. In order to do that, I exploit existing parameterized models for mammalian sounds by using them as signature signals. Digital data is transmitted by mapping vectors of information bits to a carefully designed set of parameters with values obtained from the biomimetic signal models. To complete the overall system design, I develop appropriate receivers taking into account the specific UWA channel models. I present some numerical results from the analysis of data recorded during the Kauai Acomms MURI 2011 (KAM11) UWA communications experiment.
It is shown that the proposed communication scheme results in approximate channel models with amplitude-limited inputs and signal-dependent additive noise. Motivated by this observation, I study capacity of amplitude-limited channels under different transmission scenarios. Specifically, I consider fading channels, signal-dependent additive Gaussian noise channels, multiple-input multiple-output (MIMO) systems and parallel Gaussian channels under peak power constraints.
I also consider practical channel coding problems for channels with signal-dependent noise. I consider two specific models; signal-dependent additive Gaussian noise channels and Z-channels which serve as binary-input binary-output approximations to the Gaussian case. I propose a new upper bound on the probability of error, and utilize it for design of codes. I illustrate the tightness of the derived bounds and the performance of the designed codes via examples.
It is shown that the proposed communication scheme results in approximate channel models with amplitude-limited inputs and signal-dependent additive noise. Motivated by this observation, I study capacity of amplitude-limited channels under different transmission scenarios. Specifically, I consider fading channels, signal-dependent additive Gaussian noise channels, multiple-input multiple-output (MIMO) systems and parallel Gaussian channels under peak power constraints.
I also consider practical channel coding problems for channels with signal-dependent noise. I consider two specific models; signal-dependent additive Gaussian noise channels and Z-channels which serve as binary-input binary-output approximations to the Gaussian case. I propose a new upper bound on the probability of error, and utilize it for design of codes. I illustrate the tightness of the derived bounds and the performance of the designed codes via examples.
ContributorsElMoslimany, Ahmad (Author) / Duman, Tolga M. (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Kosut, Oliver (Committee member) / Arizona State University (Publisher)
Created2015
Description
In many applications, measured sensor data is meaningful only when the location of sensors is accurately known. Therefore, the localization accuracy is crucial. In this dissertation, both location estimation and location detection problems are considered.
In location estimation problems, sensor nodes at known locations, called anchors, transmit signals to sensor nodes at unknown locations, called nodes, and use these transmissions to estimate the location of the nodes. Specifically, the location estimation in the presence of fading channels using time of arrival (TOA) measurements with narrowband communication signals is considered. Meanwhile, the Cramer-Rao lower bound (CRLB) for localization error under different assumptions is derived. Also, maximum likelihood estimators (MLEs) under these assumptions are derived.
In large WSNs, distributed location estimation algorithms are more efficient than centralized algorithms. A sequential localization scheme, which is one of distributed location estimation algorithms, is considered. Also, different localization methods, such as TOA, received signal strength (RSS), time difference of arrival (TDOA), direction of arrival (DOA), and large aperture array (LAA) are compared under different signal-to-noise ratio (SNR) conditions. Simulation results show that DOA is the preferred scheme at the low SNR regime and the LAA localization algorithm provides better performance for network discovery at high SNRs. Meanwhile, the CRLB for the localization error using the TOA method is also derived.
A distributed location detection scheme, which allows each anchor to make a decision as to whether a node is active or not is proposed. Once an anchor makes a decision, a bit is transmitted to a fusion center (FC). The fusion center combines all the decisions and uses a design parameter $K$ to make the final decision. Three scenarios are considered in this dissertation. Firstly, location detection at a known location is considered. Secondly, detecting a node in a known region is considered. Thirdly, location detection in the presence of fading is considered. The optimal thresholds are derived and the total probability of false alarm and detection under different scenarios are derived.
In location estimation problems, sensor nodes at known locations, called anchors, transmit signals to sensor nodes at unknown locations, called nodes, and use these transmissions to estimate the location of the nodes. Specifically, the location estimation in the presence of fading channels using time of arrival (TOA) measurements with narrowband communication signals is considered. Meanwhile, the Cramer-Rao lower bound (CRLB) for localization error under different assumptions is derived. Also, maximum likelihood estimators (MLEs) under these assumptions are derived.
In large WSNs, distributed location estimation algorithms are more efficient than centralized algorithms. A sequential localization scheme, which is one of distributed location estimation algorithms, is considered. Also, different localization methods, such as TOA, received signal strength (RSS), time difference of arrival (TDOA), direction of arrival (DOA), and large aperture array (LAA) are compared under different signal-to-noise ratio (SNR) conditions. Simulation results show that DOA is the preferred scheme at the low SNR regime and the LAA localization algorithm provides better performance for network discovery at high SNRs. Meanwhile, the CRLB for the localization error using the TOA method is also derived.
A distributed location detection scheme, which allows each anchor to make a decision as to whether a node is active or not is proposed. Once an anchor makes a decision, a bit is transmitted to a fusion center (FC). The fusion center combines all the decisions and uses a design parameter $K$ to make the final decision. Three scenarios are considered in this dissertation. Firstly, location detection at a known location is considered. Secondly, detecting a node in a known region is considered. Thirdly, location detection in the presence of fading is considered. The optimal thresholds are derived and the total probability of false alarm and detection under different scenarios are derived.
ContributorsZhang, Xue (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2016
Description
The first official history of the Great Patriotic War appeared in the Soviet Union in 1960-1965. It evolved into a six-volume set that elicited both praise and criticism from the reading public. This dissertation examines the creation of the historiographical narrative of the Great Patriotic War in the decade following de-Stalinization in 1956. The debates historians, Party and state representatives engaged in, including the responses they received from reviewers and readers, shed new light on the relationship between the government, those who wrote state-sponsored narratives, and the reading public.
The narrative examined here shows the importance and value placed on the war effort, and explores how aspects of the Stalinist period were retained during the Thaw. By focusing on previously unexplored archival material, which documents debates and editorial decisions, an examination of how officials sought to control the state’s explanation of events, motivations and consequences of the war can be examined in-depth. To date, the periodization, terminology and areas of concentration that define the course of the Great Patriotic War are fixated on topics that Stalin’s war narrative favored, assigning significance to events according to Stalinist preferences rather than objective analysis. My study of the war’s historiography shows how contentious its memory became at every level, making it difficult to clearly discern who represented and opposed the party line throughout Soviet society.
The author argues that the collective memory of the war, as propagated by the state, became so all-encompassing that it was often the preferred version, infiltrating individual memories and displacing or blending with personal recollections and factual documentation. Because the war touched the entire population of the Soviet Union, its story became the foundational myth of the USSR, replacing the October Revolution, and was used as a legitimizing tool by Nikita Khrushchev and Leonid Brezhnev. Most recently, it has experienced a revival in the post-Soviet period by Vladimir Putin as a way to unify Russia and build popular support for his administration. Viewing how the public interacted with representatives of the state over the creation of the official history of the war suggests that like no other event, war compels any state, even a totalitarian state, to reexamine its foundations, historical memory, foreign and domestic policies and views on censorship.
The narrative examined here shows the importance and value placed on the war effort, and explores how aspects of the Stalinist period were retained during the Thaw. By focusing on previously unexplored archival material, which documents debates and editorial decisions, an examination of how officials sought to control the state’s explanation of events, motivations and consequences of the war can be examined in-depth. To date, the periodization, terminology and areas of concentration that define the course of the Great Patriotic War are fixated on topics that Stalin’s war narrative favored, assigning significance to events according to Stalinist preferences rather than objective analysis. My study of the war’s historiography shows how contentious its memory became at every level, making it difficult to clearly discern who represented and opposed the party line throughout Soviet society.
The author argues that the collective memory of the war, as propagated by the state, became so all-encompassing that it was often the preferred version, infiltrating individual memories and displacing or blending with personal recollections and factual documentation. Because the war touched the entire population of the Soviet Union, its story became the foundational myth of the USSR, replacing the October Revolution, and was used as a legitimizing tool by Nikita Khrushchev and Leonid Brezhnev. Most recently, it has experienced a revival in the post-Soviet period by Vladimir Putin as a way to unify Russia and build popular support for his administration. Viewing how the public interacted with representatives of the state over the creation of the official history of the war suggests that like no other event, war compels any state, even a totalitarian state, to reexamine its foundations, historical memory, foreign and domestic policies and views on censorship.
ContributorsMann, Yan (Author) / Von Hagen, Mark (Thesis advisor) / Manchester, Laurie (Thesis advisor) / Holian, Anna (Committee member) / Arizona State University (Publisher)
Created2016
Description
This dissertation introduces stochastic ordering of instantaneous channel powers of fading channels as a general method to compare the performance of a communication system over two different channels, even when a closed-form expression for the metric may not be available. Such a comparison is with respect to a variety of performance metrics such as error rates, outage probability and ergodic capacity, which share common mathematical properties such as monotonicity, convexity or complete monotonicity. Complete monotonicity of a metric, such as the symbol error rate, in conjunction with the stochastic Laplace transform order between two fading channels implies the ordering of the two channels with respect to the metric. While it has been established previously that certain modulation schemes have convex symbol error rates, there is no study of the complete monotonicity of the same, which helps in establishing stronger channel ordering results. Toward this goal, the current research proves for the first time, that all 1-dimensional and 2-dimensional modulations have completely monotone symbol error rates. Furthermore, it is shown that the frequently used parametric fading distributions for modeling line of sight exhibit a monotonicity in the line of sight parameter with respect to the Laplace transform order. While the Laplace transform order can also be used to order fading distributions based on the ergodic capacity, there exist several distributions which are not Laplace transform ordered, although they have ordered ergodic capacities. To address this gap, a new stochastic order called the ergodic capacity order has been proposed herein, which can be used to compare channels based on the ergodic capacity. Using stochastic orders, average performance of systems involving multiple random variables are compared over two different channels. These systems include diversity combining schemes, relay networks, and signal detection over fading channels with non-Gaussian additive noise. This research also addresses the problem of unifying fading distributions. This unification is based on infinite divisibility, which subsumes almost all known fading distributions, and provides simplified expressions for performance metrics, in addition to enabling stochastic ordering.
ContributorsRajan, Adithya (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Bliss, Daniel (Committee member) / Kosut, Oliver (Committee member) / Arizona State University (Publisher)
Created2014
Description
Recently, the location of the nodes in wireless networks has been modeled as point processes. In this dissertation, various scenarios of wireless communications in large-scale networks modeled as point processes are considered. The first part of the dissertation considers signal reception and detection problems with symmetric alpha stable noise which is from an interfering network modeled as a Poisson point process. For the signal reception problem, the performance of space-time coding (STC) over fading channels with alpha stable noise is studied. We derive pairwise error probability (PEP) of orthogonal STCs. For general STCs, we propose a maximum-likelihood (ML) receiver, and its approximation. The resulting asymptotically optimal receiver (AOR) does not depend on noise parameters and is computationally simple, and close to the ML performance. Then, signal detection in coexisting wireless sensor networks (WSNs) is considered. We define a binary hypothesis testing problem for the signal detection in coexisting WSNs. For the problem, we introduce the ML detector and simpler alternatives. The proposed mixed-fractional lower order moment (FLOM) detector is computationally simple and close to the ML performance. Stochastic orders are binary relations defined on probability. The second part of the dissertation introduces stochastic ordering of interferences in large-scale networks modeled as point processes. Since closed-form results for the interference distributions for such networks are only available in limited cases, it is of interest to compare network interferences using stochastic. In this dissertation, conditions on the fading distribution and path-loss model are given to establish stochastic ordering between interferences. Moreover, Laplace functional (LF) ordering is defined between point processes and applied for comparing interference. Then, the LF orderings of general classes of point processes are introduced. It is also shown that the LF ordering is preserved when independent operations such as marking, thinning, random translation, and superposition are applied. The LF ordering of point processes is a useful tool for comparing spatial deployments of wireless networks and can be used to establish comparisons of several performance metrics such as coverage probability, achievable rate, and resource allocation even when closed form expressions for such metrics are unavailable.
ContributorsLee, Junghoon (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Committee member) / Reisslein, Martin (Committee member) / Kosut, Oliver (Committee member) / Arizona State University (Publisher)
Created2014
Description
Texture analysis plays an important role in applications like automated pattern inspection, image and video compression, content-based image retrieval, remote-sensing, medical imaging and document processing, to name a few. Texture Structure Analysis is the process of studying the structure present in the textures. This structure can be expressed in terms of perceived regularity. Our human visual system (HVS) uses the perceived regularity as one of the important pre-attentive cues in low-level image understanding. Similar to the HVS, image processing and computer vision systems can make fast and efficient decisions if they can quantify this regularity automatically. In this work, the problem of quantifying the degree of perceived regularity when looking at an arbitrary texture is introduced and addressed. One key contribution of this work is in proposing an objective no-reference perceptual texture regularity metric based on visual saliency. Other key contributions include an adaptive texture synthesis method based on texture regularity, and a low-complexity reduced-reference visual quality metric for assessing the quality of synthesized textures. In order to use the best performing visual attention model on textures, the performance of the most popular visual attention models to predict the visual saliency on textures is evaluated. Since there is no publicly available database with ground-truth saliency maps on images with exclusive texture content, a new eye-tracking database is systematically built. Using the Visual Saliency Map (VSM) generated by the best visual attention model, the proposed texture regularity metric is computed. The proposed metric is based on the observation that VSM characteristics differ between textures of differing regularity. The proposed texture regularity metric is based on two texture regularity scores, namely a textural similarity score and a spatial distribution score. In order to evaluate the performance of the proposed regularity metric, a texture regularity database called RegTEX, is built as a part of this work. It is shown through subjective testing that the proposed metric has a strong correlation with the Mean Opinion Score (MOS) for the perceived regularity of textures. The proposed method is also shown to be robust to geometric and photometric transformations and outperforms some of the popular texture regularity metrics in predicting the perceived regularity. The impact of the proposed metric to improve the performance of many image-processing applications is also presented. The influence of the perceived texture regularity on the perceptual quality of synthesized textures is demonstrated through building a synthesized textures database named SynTEX. It is shown through subjective testing that textures with different degrees of perceived regularities exhibit different degrees of vulnerability to artifacts resulting from different texture synthesis approaches. This work also proposes an algorithm for adaptively selecting the appropriate texture synthesis method based on the perceived regularity of the original texture. A reduced-reference texture quality metric for texture synthesis is also proposed as part of this work. The metric is based on the change in perceived regularity and the change in perceived granularity between the original and the synthesized textures. The perceived granularity is quantified through a new granularity metric that is proposed in this work. It is shown through subjective testing that the proposed quality metric, using just 2 parameters, has a strong correlation with the MOS for the fidelity of synthesized textures and outperforms the state-of-the-art full-reference quality metrics on 3 different texture databases. Finally, the ability of the proposed regularity metric in predicting the perceived degradation of textures due to compression and blur artifacts is also established.
ContributorsVaradarajan, Srenivas (Author) / Karam, Lina J (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Li, Baoxin (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2014
Description
This work considers the problem of multiple detection and tracking in two complex time-varying environments, urban terrain and underwater. Tracking multiple radar targets in urban environments is rst investigated by exploiting multipath signal returns, wideband underwater acoustic (UWA) communications channels are estimated using adaptive learning methods, and multiple UWA communications users are detected by designing the transmit signal to match the environment. For the urban environment, a multi-target tracking algorithm is proposed that integrates multipath-to-measurement association and the probability hypothesis density method implemented using particle filtering. The algorithm is designed to track an unknown time-varying number of targets by extracting information from multiple measurements due to multipath returns in the urban terrain. The path likelihood probability is calculated by considering associations between measurements and multipath returns, and an adaptive clustering algorithm is used to estimate the number of target and their corresponding parameters. The performance of the proposed algorithm is demonstrated for different multiple target scenarios and evaluated using the optimal subpattern assignment metric. The underwater environment provides a very challenging communication channel due to its highly time-varying nature, resulting in large distortions due to multipath and Doppler-scaling, and frequency-dependent path loss. A model-based wideband UWA channel estimation algorithm is first proposed to estimate the channel support and the wideband spreading function coefficients. A nonlinear frequency modulated signaling scheme is proposed that is matched to the wideband characteristics of the underwater environment. Constraints on the signal parameters are derived to optimally reduce multiple access interference and the UWA channel effects. The signaling scheme is compared to a code division multiple access (CDMA) scheme to demonstrate its improved bit error rate performance. The overall multi-user communication system performance is finally analyzed by first estimating the UWA channel and then designing the signaling scheme for multiple communications users.
ContributorsZhou, Meng (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Kovvali, Narayan (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2014
Description
LTE-Advanced networks employ random access based on preambles
transmitted according to multi-channel slotted Aloha principles. The
random access is controlled through a limit W on the number of
transmission attempts and a timeout period for uniform backoff after a
collision. We model the LTE-Advanced random access system by formulating
the equilibrium condition for the ratio of the number of requests
successful within the permitted number of transmission attempts to those
successful in one attempt. We prove that for W≤8 there is only one
equilibrium operating point and for W≥9 there are three operating
points if the request load ρ is between load boundaries ρ1
and ρ2. We analytically identify these load boundaries as well as
the corresponding system operating points. We analyze the throughput and
delay of successful requests at the operating points and validate the
analytical results through simulations. Further, we generalize the
results using a steady-state equilibrium based approach and develop
models for single-channel and multi-channel systems, incorporating the
barring probability PB. Ultimately, we identify the de-correlating
effect of parameters O, PB, and Tomax and introduce the
Poissonization effect due to the backlogged requests in a slot. We
investigate the impact of Poissonization on different traffic and
conclude this thesis.
transmitted according to multi-channel slotted Aloha principles. The
random access is controlled through a limit W on the number of
transmission attempts and a timeout period for uniform backoff after a
collision. We model the LTE-Advanced random access system by formulating
the equilibrium condition for the ratio of the number of requests
successful within the permitted number of transmission attempts to those
successful in one attempt. We prove that for W≤8 there is only one
equilibrium operating point and for W≥9 there are three operating
points if the request load ρ is between load boundaries ρ1
and ρ2. We analytically identify these load boundaries as well as
the corresponding system operating points. We analyze the throughput and
delay of successful requests at the operating points and validate the
analytical results through simulations. Further, we generalize the
results using a steady-state equilibrium based approach and develop
models for single-channel and multi-channel systems, incorporating the
barring probability PB. Ultimately, we identify the de-correlating
effect of parameters O, PB, and Tomax and introduce the
Poissonization effect due to the backlogged requests in a slot. We
investigate the impact of Poissonization on different traffic and
conclude this thesis.
ContributorsTyagi, Revak (Author) / Reisslein, Martin (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / McGarry, Michael (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2014