Cross-layer design for mission-critical IoT in mobile edge computing systems

In this paper, we establish a cross-layer framework for optimizing user association, packet offloading rates, and bandwidth allocation for mission-critical Internet-of-Things (MC-IoT) services with short packets in mobile edge computing (MEC) systems, where enhanced mobile broadband (eMBB) services with long packets are considered as background services. To reduce communication delay, the fifth generation new radio is adopted in radio access networks. To avoid long queueing delay for short packets from MC-IoT, processor-sharing (PS) servers are deployed at MEC systems, where the service rate of the server is equally allocated to all the packets in the buffer. We derive the distribution of latency experienced by short packets in closed form, and minimize the overall packet loss probability subject to the end-to-end delay requirement. To solve the nonconvex optimization problem, we propose an algorithm that converges to a near optimal solution when the throughput of eMBB services is much higher than MC-IoT services, and extend it into more general scenarios. Furthermore, we derive the optimal solutions in two asymptotic cases: communication or computing is the bottleneck of reliability. The simulation and numerical results validate our analysis and show that the PS server outperforms first-come-first-serve servers

Radio Resource Management for New Application Scenarios in 5G: Optimization and Deep Learning

The fifth-generation (5G) New Radio (NR) systems are expected to support a wide range of emerging applications with diverse Quality-of-Service (QoS) requirements. New application scenarios in 5G NR include enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low-latency communications (URLLC). New wireless architectures, such as full-dimension (FD) massive multiple-input multiple-output (MIMO) and mobile edge computing (MEC) system, and new coding scheme, such as short block-length channel coding, are envisioned as enablers of QoS requirements for 5G NR applications. Resource management in these new wireless architectures is crucial in guaranteeing the QoS requirements of 5G NR systems. The traditional optimization problems, such as subcarriers and user association, are usually non-convex or Non-deterministic Polynomial-time (NP)-hard. It is time-consuming and computing-expensive to find the optimal solution, especially in a large-scale network. To solve these problems, one approach is to design a low-complexity algorithm with near optimal performance. In some cases, the low complexity algorithms are hard to obtain, deep learning can be used as an accurate approximator that maps environment parameters, such as the channel state information and traffic state, to the optimal solutions. In this thesis, we design low-complexity optimization algorithms, and deep learning frameworks in different architectures of 5G NR to resolve optimization problems subject to QoS requirements. First, we propose a low-complexity algorithm for a joint cooperative beamforming and user association problem for eMBB in 5G NR to maximize the network capacity. Next, we propose a deep learning (DL) framework to optimize user association, resource allocation, and offloading probabilities for delay-tolerant services and URLLC in 5G NR. Finally, we address the issue of time-varying traffic and network conditions on resource management in 5G NR

Resource allocation problems in stochastic sequential decision making

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2009.Includes bibliographical references (p. 159-162).In this thesis, we study resource allocation problems that arise in the context of stochastic sequential decision making problems. The practical utility of optimal algorithms for these problems is limited due to their high computational and storage requirements. Also, an increasing number of applications require a decentralized solution. We develop techniques for approximately solving certain class of resource allocation problems that arise in the context of stochastic sequential decision making problems that are computationally efficient with a focus on decentralized algorithms where appropriate. The first resource allocation problem that we study is a stochastic sequential decision making problem with multiple decision makers (agents) with two main features 1) Partial observability Each agent may not have complete information regarding the system 2) Limited Communication - Each agent may not be able to communicate with all other agents at all times. We formulate a Markov Decision Process (MDP) for this problem. The features of partial observability and limited communication impose additional computational constraints on the exact solution of the MDP. We propose a scheme for approximating the optimal Q function and the optimal value function associated with this MDP as a linear combination of preselected basis functions. We show that the proposed approximation scheme leads to decentralization of the agents' decisions thereby enabling their implementation under limited communication. We propose a linear program, ALP, for selecting the parameters for combining the basis functions. We establish bounds relating the approximation error due to the choice of the parameters selected by the ALP with the best possible error given the choice of basis functions.(cont.) Motivated by the need for a decentralized solution to the ALP, which is equivalent to a resource allocation problem with separable, concave objective function, we analyze a general class of resource allocation problems with separable concave objective functions. We propose a distributed algorithm for this class of problems when the objective function is differentiable and establish its convergence and convergence rate properties. We develop a smoothing scheme for non-differentiable objective functions and extend the algorithm for this case. Finally, we build on these results to extend the decentralized algorithm to accommodate non-negativity constraints on the resources. Numerical investigations on the performance of the developed algorithm show that our algorithm is competitive with its centralized counterpart. The second resource allocation problem that we study is the problem of optimally accepting or rejecting arriving orders in a Make-To-Order (MTO) manufacturing firm. We model the production facility of the MTO manufacturing firm as a queue and view the time of the production facility as a resource that needs to be optimally allotted between current and future orders. We formulate the Order Acceptance Problem under two arrival processes - Poisson process (OAP-P), and Bernoulli Process (OAP-B) and formulate both problems as MDPs. We provide insights into the structure of the optimal order acceptance policy for OAP-B under the assumption of First Come First Served (FCFS) scheduling of accepted orders.(cont.) We investigate a class of randomized order acceptance policies for OAP-B called static policies that are practically relevant due to their ease of implementation and develop a procedure for computing the policy gradient for any static policy. Using these results for OAP-B, we propose 4 heuristics for OAP-P. We numerically investigate the performance of the proposed heuristics and compare their performance with other heuristics reported in literature. One of our proposed heuristics, FCFS-ValueFunction outperforms other heuristics under a variety of conditions while also being easy to implement.by Hariharan Lakshmanan.Ph.D

Modelos analíticos para la evaluación de mecanismos de control de tráfico en redes ATM

Se presentan una serie de modelos analíticos que son de utilidad para la evaluación de mecanismos de control de tráfico en redes digitales de servicios integrados de alta velocidad que usan conmutación de paquetes. En concreto se estudia el caso de redes ATM (Asynchronous Transfer Mode).Por un lado se ha desarrollado un modelo para evaluar la perturbación que se introduce al multiplexar un flujo individual de tasa constante que tráfico a ráfagas. Por otro lado, se ha estudiado mecanismos que permiten discriminar entre células ATM que ocupan un buffer de memoria en un mutiplexor o conmutador, estableciendo una prioridad de pérdidas.En el desarrollo de los modelos se ha tenido en cuenta las correlaciones que aparecen en los flujos de tráfico de dichas redes. Para ello se han usado modelos markovianos, en los que el estado de una cadena de Markov permite caracterizar la intensidad del tráfico. Los modelos de colas resultantes se han resuelto usando la metodología de análisis matricial de colas desarrollada principalmente por M. F. Neuts y modelos de aproximación de fluido.We study analytical models for the performance evaluation of traffic control mechanisms in high-speed packet switching digital integrated services networks. In particular, we focus our study on the case of ATM networks.The main contributions of this work are two: (i) An analytical model to evaluate the Cell Delay Variation introduced on a CBR flow, and (ii) several analytical models to study Space Priority mechanism, which introduce a priority among cells in the occupancy of multiplexers and switch buffers.These models take into account the traffic correlation present in this type of networks. We have used markovian models, where each state of the Markov chain characterizes the intensity of the traffic. The obtained queueing models where solved using the Matrix Analysis methodology developed by M. F. Neuts, and a fluid-flow approximation

Sistemas de colas en tiempo discreto con entradas y servicios en bloque: estudio teórico y simulaciones comparativas

Los sistemas de colas se vienen estudiando desde inicios del siglo XX. Suele formarse una cola ante una instalación que proporciona determinado servicio. La teoría de colas pretende estudiar las fluctuaciones que se producen en estas situaciones: el número de clientes, el tiempo que debe esperar cada uno antes de ser atendido, la duración del tiempo de servicio … En este trabajo se plantean algunos modelos de colas con un solo servidor en los que los clientes llegan y son atendidos en grupos, no necesariamente del mismo tamaño. El estudio se hace mediante simulación y mediante análisis probabilístico y se comparan los resultados obtenidos por ambos procedimientos. Se mide la eficiencia de cada modelo en términos de acumulación de clientes y tiempos de espera de acuerdo con los parámetros que los gobiernan. También se comparan las eficiencias de los modelos planteados

Bits of Internet traffic control

In this work, we consider four problems in the context of Internet traffic control. The first problem is to understand when and why a sender that implements an equation-based rate control would be TCP-friendly, or not—a sender is said to be TCP-friendly if, under the same operating conditions, its long-term average send rate does not exceed that of a TCP sender. It is an established axiom that some senders in the Internet would need to be TCP-friendly. An equation-based rate control sender plugs-in some on-line estimates of the loss-event rate and an expected round-trip time in a TCP throughput formula, and then at some points in time sets its send rate to such computed values. Conventional wisdom held that if a sender adjusts its send rate as just described, then it would be TCP-friendly. We show exact analysis that tells us when we should expect an equation-based rate control to be TCP-friendly, and in some cases excessively so. We show experimental evidence and identify the causes that, in a realistic scenario, make an equation-based rate control grossly non-TCP-friendly. Our second problem is to understand the throughput achieved by another family of send rate controls—we termed these "increase-decrease controls," with additive-increase/multiplicative-decrease as a special case. One issue that we consider is the allocation of long-term average send rates among senders that adjust their send rates by an additive-increase/multiplicative-decrease control, in a network of links with arbitrary fixed routes, and arbitrary round-trip times. We show what the resulting send rate allocation is. This result advances the state-of-the-art in understanding the fairness of the rate allocation in presence of arbitrary round-trip times. We also consider the design of an increase-decrease control to achieve a given target loss-throughput function. We show that if we design some increase-decrease controls under a commonly used reference loss process—a sequence of constant inter-loss event times—then we know that these controls would overshoot their target loss-throughput function, for some more general loss processes. A reason to study the design problem is to construct an increase-decrease control that would be friendly to some other control, TCP, for instance. The third problem that we consider is how to obtain probabilistic bounds on performance for nodes that conform to the per-hop-behavior of Expedited Forwarding, a service of differentiated services Internet. Under the assumption that the arrival process to a node consists of flows that are individually regulated (as it is commonplace with Expedited Forwarding) and the flows are stochastically independent, we obtained probabilistic bounds on backlog, delay, and loss. We apply our single-node performance bounds to a network of nodes. Having good probabilistic bounds on the performance of nodes that conform to the per-hop-behavior of Expedited Forwarding, would enable a dimensioning of those networks more effectively, than by using some deterministic worst-case performance bounds. Our last problem is on the latency of an input-queued switch that implements a decomposition-based scheduler. With decomposition-based schedulers, we are given a rate demand matrix to be offered by a switch in the long-term between the switch input/output port pairs. A given rate demand matrix is, by some standard techniques, decomposed into a set of permutation matrices that define the connectivity of the input/output port pairs. The problem is how to construct a schedule of the permutation matrices such that the schedule offers a small latency for each input/output port pair of the switch. We obtain bounds on the latency for some schedulers that are in many situations smaller than a best-known bound. It is important to be able to design switches with bounds on their latencies in order to provide guarantees on delay-jitter

From Sleeping to Stockpiling: Energy Conservation via Stochastic Scheduling in Wireless Networks.

Motivated by the need to conserve energy in wireless networks, we study three stochastic dynamic scheduling problems. In the first problem, we consider a wireless sensor node that can turn its radio off for fixed durations of time in order to conserve energy. We formulate finite horizon expected cost and infinite horizon average expected cost problems to model the fundamental tradeoff between packet delay and energy consumption. Through analysis of the dynamic programming equations, we derive structural results on the optimal policies for both formulations. For the infinite horizon problem, we identify a threshold decision rule to determine the optimal control action when the queue is empty. In the second problem, we consider a sensor node with an inaccurate timer in the ultra-low power sleep mode. The loss in timing accuracy in the sleep mode can result in unnecessary energy consumption from two unsynchronized devices trying to communicate. We develop a novel method for the node to calibrate its timer: occasionally waking up to measure the ambient temperature, upon which the timer speed depends. The objective is to dynamically schedule a limited number of temperature measurements in a manner most useful to improving the accuracy of the timer. We formulate optimization problems with both continuous and discrete underlying time scales, and implement a numerical solution to an equivalent reduction of the second formulation. In the third problem, we consider a single source transmitting data to one or more receivers over a shared wireless channel. Each receiver has a buffer to store received packets before they are drained. The transmitter's goal is to minimize total power consumption by exploiting the temporal and spatial variation of the channel, while preventing the receivers' buffers from emptying. In the case of a single receiver, we show that modified base-stock and finite generalized base-stock policies are optimal when the power-rate curves are linear and piecewise-linear convex, respectively. We also present the sequences of critical numbers that complete the characterizations of the optimal policies when additional technical conditions are satisfied. We then analyze the structure of the optimal policy for the case of two receivers.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/77839/1/dishuman_1.pd