DELAY MINIMIZATION IN ENERGY CONSTRAINED WIRELESS COMMUNICATIONS

In wireless communications and networks, especially for many real-time applications, the average delay packets experience is an important quality of service criterion. Therefore, it is imperative to design advanced transmission schemes to jointly address the goals of reliability, high rates and low delay. Achieving these objectives often requires careful allocation of given resources, such as energy, power, rate, among users. It also requires a close collaboration between physical layer, medium access control layer, and upper layers, and yields cross-layer solutions. We first investigate the problem of minimizing the overall transmission delay of packets in a multiple access wireless communication system, where the transmitters have average power constraints. We formulate the problem as a constrained optimization problem, and then transform it into a linear programming problem. We show that the optimal policy has a threshold structure: when the sum of the queue lengths is larger than a threshold, both users should transmit a packet during the current slot; when the sum of the queue lengths is smaller than a threshold, only one of the users, the one with the longer queue, should transmit a packet during the current slot. Then, we study the delay-optimal rate allocation in a multiple access wireless communication system. Our goal is to allocate rates to users, from the multiple access capacity region, based on their current queue lengths, in order to minimize the average delay of the system. We formulate the problem as a Markov decision problem (MDP) with an average cost criterion. We first show that the value function is increasing, symmetric and convex in the queue length vector. Taking advantage of these properties, we show that the optimal rate allocation policy is one which tries to equalize the queue lengths as much as possible in each slot, while working on the dominant face of the capacity region. Next, we extend the delay-optimal rate allocation problem to a communication channel with two transmitters and one receiver, where the underlying rate region is approximated as a general pentagon. We show that the delay-optimal policy has a switch curve structure. For the discounted-cost problem, we prove that the switch curve has a limit along one of the dimensions. The existence of a limit in the switch curve along one of the dimensions implies that, once the queue state is beyond the limit, the system always operates at one of the corner points, implying that the queues can be operated partially distributedly. Next, we shift our focus from the average delay minimization problem to transmission completion time minimization problem in energy harvesting communication systems. We first consider the optimal packet scheduling problem in a single-user energy harvesting wireless communication system. In this system, both the data packets and the harvested energy are modeled to arrive at the source node randomly. Our goal is to adaptively change the transmission rate according to the traffic load and available energy, such that the time by which all packets are delivered is minimized. Under a deterministic system setting, we develop an optimal off-line scheduling policy which minimizes the transmission completion time, under causality constraints on both data and energy arrivals. Then, we investigate the transmission completion time minimization problem in a two-user additive white Gaussian noise (AWGN) broadcast channel, where the transmitter is able to harvest energy from the nature. We first analyze the structural properties of the optimal transmission policy. We prove that the optimal total transmit power has the same structure as the optimal single-user transmit power. We also prove that there exists a cut-off power level for the stronger user. If the optimal total transmit power is lower than this level, all transmit power is allocated to the stronger user, and when the optimal total transmit power is larger than this level, all transmit power above this level is allocated to the weaker user. Based on these structural properties of the optimal policy, we propose an algorithm that yields the globally optimal off-line scheduling policy. Next, we investigate the transmission completion time minimization problem in a two-user AWGN multiple access channel. We first develop a generalized iterative backward waterfilling algorithm to characterize the maximum departure region of the transmitters for any given deadline. Then, based on the sequence of maximum departure regions at energy arrival epochs, we decompose the transmission completion time minimization problem into a convex optimization problem and solve it efficiently. Finally, we investigate the average delay minimization problem in a single-user communication channel with an energy harvesting transmitter. We consider three different cases. In the first case, both the data packets and the energy to be used to transmit them are assumed to be available at the transmitter at the beginning. In the second case, while the energy is available at the transmitter at the beginning, packets arrive during the transmissions. In the third case, the packets are available at the transmitter at the beginning and the energy arrives during the transmissions, as a result of energy harvesting. In each scenario, we find the structural properties of the optimal solution, and develop iterative algorithms to obtain the solution

High speed all optical networks

An inherent problem of conventional point-to-point wide area network (WAN) architectures is that they cannot translate optical transmission bandwidth into comparable user available throughput due to the limiting electronic processing speed of the switching nodes. The first solution to wavelength division multiplexing (WDM) based WAN networks that overcomes this limitation is presented. The proposed Lightnet architecture takes into account the idiosyncrasies of WDM switching/transmission leading to an efficient and pragmatic solution. The Lightnet architecture trades the ample WDM bandwidth for a reduction in the number of processing stages and a simplification of each switching stage, leading to drastically increased effective network throughputs. The principle of the Lightnet architecture is the construction and use of virtual topology networks, embedded in the original network in the wavelength domain. For this construction Lightnets utilize the new concept of lightpaths which constitute the links of the virtual topology. Lightpaths are all-optical, multihop, paths in the network that allow data to be switched through intermediate nodes using high throughput passive optical switches. The use of the virtual topologies and the associated switching design introduce a number of new ideas, which are discussed in detail

Queueing networks: solutions and applications

During the pasttwo decades queueing network models have proven to be a versatile tool for computer system and computer communication system performance evaluation. This chapter provides a survey of th field with a particular emphasis on applications. We start with a brief historical retrospective which also servesto introduce the majr issues and application areas. Formal results for product form queuenig networks are reviewed with particular emphasis on the implications for computer systems modeling. Computation algorithms, sensitivity analysis and optimization techniques are among the topics covered. Many of the important applicationsof queueing networks are not amenableto exact analysis and an (often confusing) array of approximation methods have been developed over the years. A taxonomy of approximation methods is given and used as the basis for for surveing the major approximation methods that have been studied. The application of queueing network to a number of areas is surveyed, including computer system cpacity planning, packet switching networks, parallel processing, database systems and availability modeling.Durante as últimas duas décadas modelos de redes de filas provaram ser uma ferramenta versátil para avaliação de desempenho de sistemas de computação e sistemas de comunicação. Este capítulo faz um apanhado geral da área, com ênfase em aplicações. Começamos com uma breve retrospectiva histórica que serve também para introduzir os pontos mais importantes e as áreas de aplicação. Resultados formais para redes de filas em forma de produto são revisados com ênfase na modelagem de sistemas de computação. Algoritmos de computação, análise de sensibilidade e técnicas de otimização estão entre os tópicos revistos. Muitas dentre importantes aplicações de redes de filas não são tratáveis por análise exata e uma série (frequentemente confusa) de métodos de aproximação tem sido desenvolvida. Uma taxonomia de métodos de aproximação é dada e usada como base para revisão dos mais importantes métodos de aproximação propostos. Uma revisão das aplicações de redes de filas em um número de áreas é feita, incluindo planejamento de capacidade de sistemas de computação, redes de comunicação por chaveamento de pacotes, processamento paralelo, sistemas de bancos de dados e modelagem de confiabilidade

Microstructure Analysis of Dynamic Markets: Limit Order Books and Dynamic Matching Markets

This thesis is concerned with addressing operational issues in two types of dynamic markets where queueing plays an important role: limit order books (financial industry), and dynamic matching markets (residential real estate). We first study the smart order routing decisions of investors in fragmented limit order book markets and the implications on the market dynamics. In modern equity markets, participants have a choice of many exchanges at which to trade. Exchanges typically operate as electronic limit order books operating under a “price-time” priority rule and, in turn, can be modeled as multi-class FIFO queueing systems. A market with multiple exchanges can be thought as a decentralized, parallel queueing system. Heterogeneous traders that submit limit orders select the exchange to place their orders by trading off delays until their order may fill against financial considerations. Simultaneously, traders that submit market orders select the exchange to direct their orders. These market orders trigger instantaneous service completions of queued limit orders. Taking into account the effect of investors’ order routing decisions, we find that the equilibrium of this decentralized market exhibits a state space collapse property. The predicted dimension reduction is the result of high-frequency order routing decisions that essentially couple the dynamics across exchanges. Analyzing a TAQ dataset for a sample of stocks over a one month period, we find empirical support for the predicted state space collapse. In the second part of this thesis, we model an electronic limit order book as a multi-class queueing system under fluid dynamics, and formulate and solve a problem of limit and market order placement to optimally buy a block of shares over a short, predetermined time horizon. Using the structure of the optimal execution policy, we identify microstructure variables that affect trading costs over short time horizons and propose a resulting microstructure-based model of market impact costs. We use a proprietary data set to estimate this cost model, and highlight its insightful structure and increased accuracy over conventional (macroscopic) market impact models that estimate the cost of a trade based on its normalized size but disregarding measurements of limit order book variables. In the third part of this thesis, we study the residential real estate markets as dynamic matching systems with an emphasis on their microstructure. We propose a stylized microstructure model and analyze the market dynamics and its equilibrium under the simplifying approximation where buyers and sellers use linear bidding strategies. We motivate and characterize this near closed-form approximation of the market equilibrium, and show that it is asymptotically accurate. We also provide numerical evidence in support of this approximation. Then with the gained tractability, we characterize steady-state properties such as market depth, price dispersion, and anticipated delays in selling or buying a unit. We characterize congestion and matching patterns for sellers and buyers, taking into account market dynamics, heterogeneity, and supply and demand imbalance manifested in the competition among buyers and sellers. Furthermore, we show the effects of market primitives with comparative statics results

Resource Allocation for Green Cloud Networks under Uncertainty: Stochastic, Robust and Big Data-driven Approaches

University of Minnesota M.S. thesis. September 2016. Major: Electrical/Computer Engineering. Advisor: Georgios Giannakis. 1 computer file (PDF); viii, 139 pages.Major improvements have propelled the development of worldwide Internet systems during the past decade. To meet the growing demand in massive data processing, a large number of geographically-distributed data centers begin to surge in the era of data deluge and information explosion. Along with their remarkable expansion, contemporary cloud networks are being challenged by the growing concerns about global warming, due to their substantial energy consumption. Hence, the infrastructure of future data centers must be energy-efficient and sustainable. Fortunately, supporting technologies of smart grids, big data analytics and machine learning, are also developing rapidly. These considerations motivate well the present thesis, which mainly focuses on developing interdisciplinary approaches to offer sustainable resource allocation for future cloud networks, by leveraging three intertwining research subjects. The modern smart grid has many new features and advanced capabilities including e.g., high penetration of renewable energy sources, and dynamic pricing based demand-side management. Clearly, by integrating these features into the cloud network infrastructure, it becomes feasible to realize its desiderata of reliability, energy-efficiency and sustainability. Yet, full benefits of the renewable energy (e.g., wind and solar) can only be harnessed by properly mitigating its intrinsically stochastic nature, which is still a challenging task. This prompts leveraging the huge volume of historical data to reduce the stochasticity of online decision making. Specifically, valuable insights from big data analytics can enable a markedly improved resource allocation policy by learning historical user and environmental patterns. Relevant machine learning approaches can further uncover “hidden insights” from historical relationships and trends in massive datasets. Targeting this goal, the present thesis systematically studies resource allocation tasks for future sustainable cloud networks under uncertainty. With an eye towards realistic scenarios, the thesis progressively adapts elegant mathematical models, optimization frameworks, and develops low complexity algorithms from three different aspects: stochastic (Chapters 2 and 3), robust (Chapter 4), and big data-driven approaches (Chapter 5). The resultant algorithms are all numerically efficient with optimality guarantees, and most of them are also amenable to a distributed implementation

Non-Bayesian Quickest Detection with Stochastic Sample Right Constraints

In this paper, we study the design and analysis of optimal detection scheme for sensors that are deployed to monitor the change in the environment and are powered by the energy harvested from the environment. In this type of applications, detection delay is of paramount importance. We model this problem as quickest change detection problem with a stochastic energy constraint. In particular, a wireless sensor powered by renewable energy takes observations from a random sequence, whose distribution will change at a certain unknown time. Such a change implies events of interest. The energy in the sensor is consumed by taking observations and is replenished randomly. The sensor cannot take observations if there is no energy left in the battery. Our goal is to design a power allocation scheme and a detection strategy to minimize the worst case detection delay, which is the difference between the time when an alarm is raised and the time when the change occurs. Two types of average run length (ARL) constraint, namely an algorithm level ARL constraint and an system level ARL constraint, are considered. We propose a low complexity scheme in which the energy allocation rule is to spend energy to take observations as long as the battery is not empty and the detection scheme is the Cumulative Sum test. We show that this scheme is optimal for the formulation with the algorithm level ARL constraint and is asymptotically optimal for the formulations with the system level ARL constraint.Comment: 30 pages, 5 figure

Isochronets: a High-Speed Network Switching Architecture

Traditional switching techniques need hundred- or thousand-MIPS processing power within switches to support Gbit/s transmission rates available today. These techniques anchor their decision-making on control information within transmitted frames and thus must resolve routes at the speed in which frames are being pumped into switches. Isochronets can potentially switch at any transmission rate by making switching decisions independent of frame contents. Isochronets divide network bandwidth among routing trees, a technique called Route Division Multiple Access (RDMA). Frames access network resources through the appropriate routing tree to the destination. Frame structures are irrelevant for switching decisions. Consequently, Isochronets can support multiple framing protocols without adaptation layers and are strong candidates for all-optical implementations. All network-layer functions are reduced to an admission control mechanism designed to provide quality of service (QOS) guarantees for multiple classes of traffic. The main results of this work are: (1) A new network architecture suitable for high-speed transmissions; (2) An implementation of Isochronets using cheap off-theshelf components; (3) A comparison of RDMA with more traditional switching techniques, such as Packet Switching and Circuit Switching; (4) New protocols necessary for Isochronet operations; and (5) Use of Isochronet techniques at higher layers of the protocol stack (in particular, we show how Isochronet techniques may solve routing problems in ATM networks)

Traffic Optimization in Data Center and Software-Defined Programmable Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen