Towards all-optical label switching nodes with multicast

Fiber optics has developed so rapidly during the last decades that it has be- come the backbone of our communication systems. Evolved from initially static single-channel point-to-point links, the current advanced optical backbone net- work consists mostly of wavelength-division multiplexed (WDM) networks with optical add/drop multiplexing nodes and optical cross-connects that can switch data in the optical domain. However, the commercially implemented optical net- work nodes are still performing optical circuit switching using wavelength routing. The dedicated use of wavelength and infrequent recon¯guration result in relatively poor bandwidth utilization. The success of electronic packet switching has inspired researchers to improve the °exibility, e±ciency, granularity and network utiliza- tion of optical networks by introducing optical packet switching using short, local optical labels for forwarding decision making at intermediate optical core network nodes, a technique that is referred to as optical label switching (OLS). Various research demonstrations on OLS systems have been reported with transparent optical packet payload forwarding based on electronic packet label processing, taking advantage of the mature technologies of electronic logical cir- cuitry. This approach requires optic-electronic-optic (OEO) conversion of the op- tical labels, a costly and power consuming procedure particularly for high-speed labels. As optical packet payload bit rate increases from gigabit per second (Gb/s) to terabit per second (Tb/s) or higher, the increased speed of the optical labels will eventually face the electronic bottleneck, so that the OEO conversion and the electronic label processing will be no longer e±cient. OLS with label processing in the optical domain, namely, all-optical label switching (AOLS), will become necessary. Di®erent AOLS techniques have been proposed in the last ¯ve years. In this thesis, AOLS node architectures based on optical time-serial label processing are presented for WDM optical packets. The unicast node architecture, where each optical packet is to be sent to only one output port of the node, has been in- vestigated and partially demonstrated in the EU IST-LASAGNE project. This thesis contributes to the multicast aspects of the AOLS nodes, where the optical packets can be forwarded to multiple or all output ports of a node. Multicast capable AOLS nodes are becoming increasingly interesting due to the exponen- tial growth of the emerging multicast Internet and modern data services such as video streaming, high de¯nition TV, multi-party online games, and enterprise ap- plications such as video conferencing and optical storage area networks. Current electronic routers implement multicast in the Internet protocol (IP) layer, which requires not only the OEO conversion of the optical packets, but also exhaus- tive routing table lookup of the globally unique IP addresses. Despite that, there has been no extensive studies on AOLS multicast nodes, technologies and tra±c performance, apart from a few proof-of-principle experimental demonstrations. In this thesis, three aspects of the multicast capable AOLS nodes are addressed: 1. Logical design of the AOLS multicast node architectures, as well as func- tional subsystems and interconnections, based on state-of-the-art literature research of the ¯eld and the subject. 2. Computer simulations of the tra±c performance of di®erent AOLS unicast and multicast node architectures, using a custom-developed AOLS simulator AOLSim. 3. Experimental demonstrations in laboratory and computer simulations using the commercially available simulator VPItransmissionMakerTM, to evaluate the physical layer performance of the required all-optical multicast technolo- gies. A few selected multi-wavelength conversion (MWC) techniques are particularly looked into. MWC is an essential subsystem of the AOLS node for realizing optical packet multicast by making multiple copies of the optical packet all-optically onto di®er- ent wavelengths channels. In this thesis, theMWC techniques based on cross-phase modulation and four-wave mixing are extensively investigated. The former tech- nique o®ers more wavelength °exibility and good conversion e±ciency, but it is only applicable to intensity modulated signals. The latter technique, on the other hand, o®ers strict transparency in data rate and modulation format, but its work- ing wavelengths are limited by the device or component used, and the conversion e±ciency is considerably lower. The proposals and results presented in this thesis show feasibility of all-optical packet switching and multicasting at line speed without any OEO conversion and electronic processing. The scalability and the costly optical components of the AOLS nodes have been so far two of the major obstacles for commercialization of the AOLS concept. This thesis also introduced a novel, scalable optical labeling concept and a label processing scheme for the AOLS multicast nodes. The pro- posed scheme makes use of the spatial positions of each label bit instead of the total absolute value of all the label bits. Thus for an n-bit label, the complexity of the label processor is determined by n instead of 2n

Contextual Bandit Modeling for Dynamic Runtime Control in Computer Systems

Modern operating systems and microarchitectures provide a myriad of mechanisms for monitoring and affecting system operation and resource utilization at runtime. Dynamic runtime control of these mechanisms can tailor system operation to the characteristics and behavior of the current workload, resulting in improved performance. However, developing effective models for system control can be challenging. Existing methods often require extensive manual effort, computation time, and domain knowledge to identify relevant low-level performance metrics, relate low-level performance metrics and high-level control decisions to workload performance, and to evaluate the resulting control models. This dissertation develops a general framework, based on the contextual bandit, for describing and learning effective models for runtime system control. Random profiling is used to characterize the relationship between workload behavior, system configuration, and performance. The framework is evaluated in the context of two applications of progressive complexity; first, the selection of paging modes (Shadow Paging, Hardware-Assisted Page) in the Xen virtual machine memory manager; second, the utilization of hardware memory prefetching for multi-core, multi-tenant workloads with cross-core contention for shared memory resources, such as the last-level cache and memory bandwidth. The resulting models for both applications are competitive in comparison to existing runtime control approaches. For paging mode selection, the resulting model provides equivalent performance to the state of the art while substantially reducing the computation requirements of profiling. For hardware memory prefetcher utilization, the resulting models are the first to provide dynamic control for hardware prefetchers using workload statistics. Finally, a correlation-based feature selection method is evaluated for identifying relevant low-level performance metrics related to hardware memory prefetching

Learning Models For Corrupted Multi-Dimensional Data: Fundamental Limits And Algorithms

Developing machine learning models for unstructured multi-dimensional datasets such as datasets with unreliable labels and noisy multi-dimensional signals with or without missing information have becoming a central necessity. We are not always fortunate enough to get noise-free datasets for developing classification and representation models. Though there is a number of techniques available to deal with noisy datasets, these methods do not exploit the multi-dimensional structures of the signals, which could be used to improve the overall classification and representation performance of the model. In this thesis, we develop a Kronecker-structure (K-S) subspace model that exploits the multi-dimensional structure of the signal. First, we study the classification performance of K-S subspace models in two asymptotic regimes when the signal dimensions go to infinity and when the noise power tends to zero. We characterize the misclassification probability in terms of diversity order and we drive an exact expression for the diversity order. We further derive a tighter bound on misclassification probability in terms of pairwise geometry of the subspaces. The proposed scheme is optimal in most of the signal dimension regimes except in one regime where the signal dimension is less than twice the subspace dimension, however, hitting such a signal dimension regime is very rare in practice. We empirically show that the classification performance of K-S subspace models agrees with the diversity order analysis. We also develop an algorithm, Kronecker- Structured Learning of Discriminative Dictionaries (K-SLD2), for fast and compact K-S subspace learning for better classification and representation of multidimensional signals. We show that the K-SLD2 algorithm balances compact signal representation and good classification performance on synthetic and real-world datasets. Next, we develop a scheme to detect whether a given multi-dimensional signal with missing information lies on a given K-S subspace. We find that under some mild incoherence conditions we must observe ��(��1 log ��1) number of rows and ��(��2 log ��2) number of columns in order to detect the K-S subspace. In order to account for unreliable labels in datasets we present Nonlinear, Noise- aware, Quasiclustering (NNAQC), a method for learning deep convolutional networks from datasets corrupted by unknown label noise. We append a nonlinear noise model to a standard convolutional network, which is learned in tandem with the parameters of the network. Further, we train the network using a loss function that encourages the clustering of training images. We argue that the non-linear noise model, while not rigorous as a probabilistic model, results in a more effective denoising operator during backpropagation. We evaluate the performance of NNAQC on artificially injected label noise to MNIST, CIFAR-10, CIFAR-100, and ImageNet datasets and on a large-scale Clothing1M dataset with inherent label noise. We show that on all these datasets, NNAQC provides significantly improved classification performance over the state of the art and is robust to the amount of label noise and the training samples

Markov and Semi-markov Chains, Processes, Systems and Emerging Related Fields

This book covers a broad range of research results in the field of Markov and Semi-Markov chains, processes, systems and related emerging fields. The authors of the included research papers are well-known researchers in their field. The book presents the state-of-the-art and ideas for further research for theorists in the fields. Nonetheless, it also provides straightforwardly applicable results for diverse areas of practitioners

Personalized Expert Recommendation: Models and Algorithms

Many large-scale information sharing systems including social media systems, questionanswering sites and rating and reviewing applications have been growing rapidly, allowing millions of human participants to generate and consume information on an unprecedented scale. To manage the sheer growth of information generation, there comes the need to enable personalization of information resources for users — to surface high-quality content and feeds, to provide personally relevant suggestions, and so on. A fundamental task in creating and supporting user-centered personalization systems is to build rich user profile to aid recommendation for better user experience. Therefore, in this dissertation research, we propose models and algorithms to facilitate the creation of new crowd-powered personalized information sharing systems. Specifically, we first give a principled framework to enable personalization of resources so that information seekers can be matched with customized knowledgeable users based on their previous historical actions and contextual information; We then focus on creating rich user models that allows accurate and comprehensive modeling of user profiles for long tail users, including discovering user’s known-for profile, user’s opinion bias and user’s geo-topic profile. In particular, this dissertation research makes two unique contributions: First, we introduce the problem of personalized expert recommendation and propose the first principled framework for addressing this problem. To overcome the sparsity issue, we investigate the use of user’s contextual information that can be exploited to build robust models of personal expertise, study how spatial preference for personally-valuable expertise varies across regions, across topics and based on different underlying social communities, and integrate these different forms of preferences into a matrix factorization-based personalized expert recommender. Second, to support the personalized recommendation on experts, we focus on modeling and inferring user profiles in online information sharing systems. In order to tap the knowledge of most majority of users, we provide frameworks and algorithms to accurately and comprehensively create user models by discovering user’s known-for profile, user’s opinion bias and user’s geo-topic profile, with each described shortly as follows: —We develop a probabilistic model called Bayesian Contextual Poisson Factorization to discover what users are known for by others. Our model considers as input a small fraction of users whose known-for profiles are already known and the vast majority of users for whom we have little (or no) information, learns the implicit relationships between user?s known-for profiles and their contextual signals, and finally predict known-for profiles for those majority of users. —We explore user’s topic-sensitive opinion bias, propose a lightweight semi-supervised system called “BiasWatch” to semi-automatically infer the opinion bias of long-tail users, and demonstrate how user’s opinion bias can be exploited to recommend other users with similar opinion in social networks. — We study how a user’s topical profile varies geo-spatially and how we can model a user’s geo-spatial known-for profile as the last step in our dissertation for creation of rich user profile. We propose a multi-layered Bayesian hierarchical user factorization to overcome user heterogeneity and an enhanced model to alleviate the sparsity issue by integrating user contexts into the two-layered hierarchical user model for better representation of user’s geo-topic preference by others

Adaptive Resource Allocation Strategies for Dynamic Heterogeneous Traffic in Td-cdma/Tdd Systems

The purpose of this study was to investigate the co-channel interference present in TD-CDMA/TDD systems and TDMA/TDD systems and propose methods to avoid the co-channel interference. Time Slot Opposing algorithm which avoids co-channel interference in TD-CDMA/D-TDD system is reviewed as part of background study. The interference scenarios in TDMA/D-TDD systems are then studied and methods to avoid co-channel interference are proposed. The algorithms are then tested using real Internet data traffic to obtain a realistic analysis. Based on the background research, an extended Max {SIR} algorithm is proposed to avoid co-channel interference in TDMA/D-TDD systems. This algorithm is a centralized dynamic channel allocation algorithm that uses information from all the cells in the system to avoid co-channel interference and increase the signal power-to-interference power outage probability ratio. The proposed algorithm is then applied to a TDMA/D-TDD system that have subscribers grouped based on priority. As a last step of the research, traffic in TDMA/D-TDD systems is modeled using the ON-OFF traffic modeling and the Max {SIR} algorithm is applied. The results obtained using ON-OFF traffic modeling matched with the results obtained using analytical simulations.School of Electrical & Computer Engineerin

Analysis of Embedded Controllers Subject to Computational Overruns

Microcontrollers have become an integral part of modern everyday embedded systems, such as smart bikes, cars, and drones. Typically, microcontrollers operate under real-time constraints, which require the timely execution of programs on the resource-constrained hardware. As embedded systems are becoming increasingly more complex, microcontrollers run the risk of violating their timing constraints, i.e., overrunning the program deadlines. Breaking these constraints can cause severe damage to both the embedded system and the humans interacting with the device. Therefore, it is crucial to analyse embedded systems properly to ensure that they do not pose any significant danger if the microcontroller overruns a few deadlines.However, there are very few tools available for assessing the safety and performance of embedded control systems when considering the implementation of the microcontroller. This thesis aims to fill this gap in the literature by presenting five papers on the analysis of embedded controllers subject to computational overruns. Details about the real-time operating system's implementation are included into the analysis, such as what happens to the controller's internal state representation when the timing constraints are violated. The contribution includes theoretical and computational tools for analysing the embedded system's stability, performance, and real-time properties.The embedded controller is analysed under three different types of timing violations: blackout events (when no control computation is completed during long periods), weakly-hard constraints (when the number of deadline overruns is constrained over a window), and stochastic overruns (when violations of timing constraints are governed by a probabilistic process). These scenarios are combined with different implementation policies to reduce the gap between the analysis and its practical applicability. The analyses are further validated with a comprehensive experimental campaign performed on both a set of physical processes and multiple simulations.In conclusion, the findings of this thesis reveal that the effect deadline overruns have on the embedded system heavily depends the implementation details and the system's dynamics. Additionally, the stability analysis of embedded controllers subject to deadline overruns is typically conservative, implying that additional insights can be gained by also analysing the system's performance