Multiple access protocols for multichannel communication systems

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (leaves 108-111).System architecture design, evaluation, and optimization are key issues to developing communication systems that meet the requirements of today and expectations of the future. In this thesis, we introduce the concept of multiple access communication and the need to use efficient transmission techniques to expand both present and future wireless communication networks. We will study two areas regarding multiple access on multichannel communication systems. First, we describe fundamental multiplexing techniques that we can build upon and investigate the performance of different candidate architectures for the transmission of messages from bursty sources on multiple channels. We will consider traditional protocols such as Time Division Multiple Access (TDMA) and Slotted ALOHA (S-ALOHA) alongside a channelized architecture, which is based on the idea of multiplexing by dividing total transmission capacity into a fixed number of frequency channels. We develop mathematical models that describe the overall delay for sending large messages of a fixed length arriving from bursty sources and analyze their performances. We will make real-world parameter assumptions in the context of wireless networks and analyze the performance to develop intuition about the effectiveness of the different architectures. Second, we will investigate channel capacity allocation among mixed traffic, i.e., multiple classes of users. We will consider a first-come first-serve (FCFS) access strategy, a non-preemptive priority scheme, a preemptive resume priority scheme, and several channel capacity allocation schemes. We develop models that describe the overall delay for sending messages and analyze their performance. Our focus will concentrate on two classes of users. This scenario is typical of classes of users with small and large messages to transmit. present quantitative results by making real-world parameter assumptions in the context of wireless networks and analyze the performance to develop intuition about the effectiveness of each architecture.by Serena Chan.M.Eng

Discrete-time queueing models with priorities

This PhD-dissertation contains analyses of several discrete-time two-class priority queueing systems. We analyze non-preemptive, preemptive resume as well as preemptive repeat priority queues. The analyses are heavily based on probability generating functions that allow us to calculate moments and tail probabilities of the system contents and packet delays of both classes. The results are applicable in heterogeneous telecommunication networks, when delay-sensitive traffic gets transmission priority over best-effort traffic. Our results predict the influence of priority scheduling on the QoS (Quality-of-Service) of the different types of traffic

Analysis and Computation of the Joint Queue Length Distribution in a FIFO Single-Server Queue with Multiple Batch Markovian Arrival Streams

This paper considers a work-conserving FIFO single-server queue with multiple batch Markovian arrival streams governed by a continuous-time finite-state Markov chain. A particular feature of this queue is that service time distributions of customers may be different for different arrival streams. After briefly discussing the actual waiting time distributions of customers from respective arrival streams, we derive a formula for the vector generating function of the time-average joint queue length distribution in terms of the virtual waiting time distribution. Further assuming the discrete phase-type batch size distributions, we develop a numerically feasible procedure to compute the joint queue length distribution. Some numerical examples are provided also

Control of multiclass queueing systems with abandonments and adversarial customers

This thesis considers the defensive surveillance of multiple public areas which are the open, exposed targets of adversarial attacks. We address the operational problem of identifying a real time decision-making rule for a security team in order to minimise the damage an adversary can inflict within the public areas. We model the surveillance scenario as a multiclass queueing system with customer abandonments, wherein the operational problem translates into developing service policies for a server in order to minimise the expected damage an adversarial customer can inflict on the system. We consider three different surveillance scenarios which may occur in realworld security operations. In each scenario it is only possible to calculate optimal policies in small systems or in special cases, hence we focus on developing heuristic policies which can be computed and demonstrate their effectiveness in numerical experiments. In the random adversary scenario, the adversary attacks the system according to a probability distribution known to the server. This problem is a special case of a more general stochastic scheduling problem. We develop new results which complement the existing literature based on priority policies and an effective approximate policy improvement algorithm. We also consider the scenario of a strategic adversary who chooses where to attack. We model the interaction of the server and adversary as a two-person zero-sum game. We develop an effective heuristic based on an iterative algorithm which populates a small set of service policies to be randomised over. Finally, we consider the scenario of a strategic adversary who chooses both where and when to attack and formulate it as a robust optimisation problem. In this case, we demonstrate the optimality of the last-come first-served policy in single queue systems. In systems with multiple queues, we develop effective heuristic policies based on the last-come first-served policy which incorporates randomisation both within service policies and across service policies

Introduction to Queueing Theory and Stochastic Teletraffic Models

The aim of this textbook is to provide students with basic knowledge of stochastic models that may apply to telecommunications research areas, such as traffic modelling, resource provisioning and traffic management. These study areas are often collectively called teletraffic. This book assumes prior knowledge of a programming language, mathematics, probability and stochastic processes normally taught in an electrical engineering course. For students who have some but not sufficiently strong background in probability and stochastic processes, we provide, in the first few chapters, background on the relevant concepts in these areas.Comment: 298 page

Optimal resource allocation algorithms for cloud computing

Cloud computing is emerging as an important platform for business, personal and mobile computing applications. We consider a stochastic model of a cloud computing cluster, where jobs arrive according to a random process and request virtual machines (VMs), which are specified in terms of resources such as CPU, memory and storage space. The jobs are first routed to one of the servers when they arrive and are queued at the servers. Each server then chooses a set of jobs from its queues so that it has enough resources to serve all of them simultaneously. There are many design issues associated with such systems. One important issue is the resource allocation problem, i.e., the design of algorithms for load balancing among servers, and algorithms for scheduling VM configurations. Given our model of a cloud, we define its capacity, i.e., the maximum rates at which jobs can be processed in such a system. An algorithm is said to be throughput-optimal if it can stabilize the system whenever the load is within the capacity region. We show that the widely-used Best-Fit scheduling algorithm is not throughput-optimal. We first consider the problem where the jobs need to be scheduled nonpreemptively on servers. Under the assumptions that the job sizes are known and bounded, we present algorithms that achieve any arbitrary fraction of the capacity region of the cloud. We then relax these assumptions and present a load balancing and scheduling algorithm that is throughput optimal when job sizes are unknown. In this case, job sizes (durations) are modeled as random variables with possibly unbounded support. Delay is a more important metric then throughput optimality in practice. However, analysis of delay of resource allocation algorithms is difficult, so we study the system in the asymptotic limit as the load approaches the boundary of the capacity region. This limit is called the heavy traffic regime. Assuming that the jobs can be preempted once after several time slots, we present delay optimal resource allocation algorithms in the heavy traffic regime. We study delay performance of our algorithms through simulations