Distributed, Networked and Mobile Computing

Summary

In this chapter we give an overview of smartphone batteries and their modeling. Battery models are important when the remaining battery capacity needs to be estimated. In this chapter, we give an overview of smartphone Li-Po batteries, their static and dynamic parameters and characteristics, and charging technology, and then consider techniques for assessing and modeling a battery SOC.

Overview

Figure 3.1 presents an overview of the Li-Ion battery charging and discharging process. Current is carried by lithium ions (Li+) that move from negative (anode) to positive (cathode) electrodes during discharging. The lithium ions move in the reverse direction when charging the battery. The ions move through the non-aqueous electrolyte and the separator. Applying a charge places the battery in a closed-circuit voltage, in which the voltage behavior is set by the internal battery resistance. Charging and discharging distorts the battery and it can take up to 24 hours for the battery to stabilize.

Battery life is proportional to the active reaction sites across the cathode. When the discharge current is low, inactive reaction sites are uniformly distributed over the volume of the cathode. When the discharge current is high, the volume of inactive sites at the outer surface of the cathode is large, causing active sites to become unreachable. As a consequence, the battery capacity is reduced at high discharge rates. When a high current is drawn from the battery, the diffusion rate cannot match the rate at which ions are being absorbed at the cathode.

Summary

We now turn our attention from individual optimization techniques to applications. We investigate a few different cases where the principles and techniques we have learned earlier can be applied in mobile applications.

We first look at a specific application, namely video streaming, which is one of the most important internet applications today, from the mobile internet's perspective. We first study the way that video streaming consumes energy and illustrate that through measurement results from real systems. We then cover different strategies that can be used to save energy in video streaming. It turns out that there are a few things that need to be taken into account when applying generic energy-saving techniques to mobile video streaming.

The next two examples are not really specific applications but rather integral parts of many applications and, therefore, they represent extremely important cross-application scenarios. The first of those is sensing. Sensing is a hot research topic at the moment and it is expected to become a very important part of smartphone applications. We study ways to reduce energy consumption with applications that require different kinds of sensing by exploring separately each category of sensors included in modern smartphones. We focus on two well-established techniques: sensor selection and duty cycling.

The second cross-application energy optimization scenario is security. We look at the energy overhead caused by security protocols and algorithms, based on measurement studies. Then, we discuss whether and when it is possible to find a tradeoff between the level of security and energy consumption.

With an ever-increasing number of applications available for mobile devices, battery life is becoming a critical factor in user satisfaction. This practical guide provides you with the key measurement, modeling, and analytical tools needed to optimize battery life by developing energy-aware and energy-efficient systems and applications. As well as the necessary theoretical background and results of the field, this hands-on book also provides real-world examples, practical guidance on assessing and optimizing energy consumption, and details of prototypes and possible future trends. Uniquely, you will learn about energy optimization of both hardware and software in one book, enabling you to get the most from the available battery power. Covering experimental system design and implementation, the book supports assignment-based courses with a laboratory component, making it an ideal textbook for graduate students. It is also a perfect guidebook for software engineers and systems architects working in industry.

Process algebra is a widely accepted and much used technique in the specification and verification of parallel and distributed software systems. This book sets the standard for the field. It assembles the relevant results of most process algebras currently in use, and presents them in a unified framework and notation. The authors describe the theory underlying the development, realization and maintenance of software that occurs in parallel or distributed systems. A system can be specified in the syntax provided, and the axioms can be used to verify that a composed system has the required external behaviour. As examples, two protocols are completely specified and verified in the text: the Alternating-Bit Protocol for Data Communication, and Fischer's Protocol of Mutual Exclusion. The book serves as a reference text for researchers and graduate students in computer science, offering a complete overview of the field and referring to further literature where appropriate.

Summary

Stationary distributions of queueing systems with general arrivals and service-time distributions are difficult to compute. In this chapter, we will develop techniques to understand the behavior of such systems in certain asymptotic regimes. We consider two such regimes: the heavy-traffic regime and the large-deviations regime. The analysis in the heavy-traffic regime can be thought of as the analog of the central limit theorem for random variables, and the analysis of the large-deviations regime can be thought of as the analog of the Chernoff bound. Traditionally, heavy-traffic analysis is performed by scaling arrival and service processes so that they can be approximated by Brownian motions, which are the stochastic process analogs of Gaussian random variables. Here, we take a different approach: we will show that heavy-traffic results can be obtained by extending the ideas behind the discrete-time Kingman bound presented in Chapter 3. The analysis of the large-deviations regime in this chapter refines the probability of overflow estimates obtained using the Chernoff bound, also in Chapter 3.

Roughly speaking, heavy traffic refers to a regime in which the mean arrival rate to a queueing system is close to the boundary of the capacity region. We will first use a Lyapunov argument to derive bounds on the moments of the scaled queue length ∈q(t) for the discrete-time G/G/1 queue; where ∈ = μ — λ; μ is the service rate and λ is the arrival rate.

Summary

In Chapter 2, we learned about routing algorithms that determine the sequence of links a packet should traverse to get to its destination. But we did not explain how a router actually moves a packet from one link to another. To understand this process, let us first look at the architecture of a router. Generally speaking, a router has four major components: the input and the output ports, which are interfaces connecting the router to input and output links, respectively, a switch fabric, and a routing processor, as shown in Figure 4.1. The routing processor maintains the routing table and makes routing decisions. The switch fabric is the component that moves packets from one link to another link. In this chapter, we will assume that all packets are of equal size. In reality, packets in the Internet have widely variable sizes. In the switch fabric, packets are divided into equal-sized cells and reassembled at the output, hence our assumption holds.

Earlier, we implicitly assumed that this switch fabric operates infinitely fast, so packets are moved from input ports to output ports immediately. This allowed us to focus on the buffers at output ports. So, all our discussions so far on buffer overflow probabilities are for output queues since an output buffer is the place where packets “enter” a link. For example, WFQ, introduced in Chapter 3, may be implemented at output port buffers to provide isolation among flows. However, in reality, the switch fabric does not really operate at infinite speed.

Summary

Why we wrote this book

Traditionally, analytical techniques for communication networks discussed in textbooks fall into two categories: (i) analysis of network protocols, primarily using queueing theoretic tools, and (ii) algorithms for network provisioning which use tools from optimization theory. Since the mid 1990s, a new viewpoint of the architecture of a communication network has emerged. Network architecture and algorithms are now viewed as slow-time-scale, distributed solutions to a large-scale optimization problem. This approach illustrates that the layered architecture of a communication network is a natural by-product of the desire to design a fair and stable system. On the other hand, queueing theory, stochastic processes, and combinatorics play an important role in designing low-complexity and distributed algorithms that are viewed as operating at fast time scales.

Our goal in writing this book is to present this modern point of view of network protocol design and analysis to a wide audience. The book provides readers with a comprehensive view of the design of communication networks using a combination of tools from optimization theory, control theory, and stochastic networks, and introduces mathematical tools needed to analyze the performance of communication network protocols.

Organization of the book

The book has been organized into two major parts. In the first part of the book, with a few exceptions, we present mathematical techniques only as tools to design algorithms implemented at various layers of a communication network. We start with the transport layer, and then consider algorithms at the link layer and the medium access layer, and finally present a unified view of all these layers along with the network layer.

Summary

A communication network is an interconnection of devices designed to carry information from various sources to their respective destinations. To execute this task of carrying information, a number of protocols (algorithms) have to be developed to convert the information to bits and transport these bits reliably over the network. The first part of this book deals with the development of mathematical models which will be used to design the protocols used by communication networks. To understand the scope of the book, it is useful first to understand the architecture of a communication network.

The sources (also called end hosts) that generate information (also called data) first convert the data into bits (0s and 1s) which are then collected into groups called packets. We will not discuss the process of converting data into packets in this book, but simply assume that the data are generated in the form of packets. Let us consider the problem of sending a stream of packets from a source S to destination D, and assume for the moment that there are no other entities (such as other sources or destinations or intermediate nodes) in the network. The source and destination must be connected by some communication medium, such as a coaxial cable, telephone wire, or optical fiber, or they have to communicate in a wireless fashion. In either case, we can imagine that S and D are connected by a communication link, although the link is virtual in the case of wireless communication.

Summary

In Chapter 2, we assumed that the transmission rates xr are positive, and we derived fair and stable resource allocation algorithms. In reality, since data are transmitted in the form of packets, the rates xr are converted to discrete window sizes, which results in bursty (non-smooth) arrival rates at the links in the network. In addition, many flows in the Internet are very short (consisting of only a few packets), for which the convergence analysis in the previous chapter does not apply. Further, there may also be flows which are not congestion controlled. Because of these deviations, the number of incoming packets at a link varies over time and may exceed the link capacity occasionally even if the mean arrival rate is less than the link capacity. So buffers are needed to absorb bursty arrivals and to reduce packet losses. To understand the effect of bursty arrivals and the role of buffering in communication networks, in this chapter we model packet arrivals at links as random processes and study the packet level performance at a link using discrete-time queueing theory. This chapter is devoted to answering the following questions.

Summary

So far we have looked at resource allocation algorithms in networks with wireline links. In this chapter, we consider networks with wireless components. The major difference between wireless and wireline networks is that in wireless networks links contend for a common resource, namely the wireless spectrum. As a result, we have to design Medium Access Control (MAC) algorithms to decide which links access the wireless medium at each time instant. As we will see, wireless MAC algorithms have features similar to scheduling algorithms for high-speed switches, which were studied in Chapter 4. However, there are some differences: wireless networks are subject to time-varying link quality due to channel fluctuations, also known as channel fading; and transmissions in wireless networks may interfere with each other, so transmissions have to be scheduled to avoid interference. In addition, some wireless networks do not have a central coordinator to perform scheduling, so scheduling decisions have to be taken independently by each link. In this chapter, we will address the following issues specific to wireless networks.

Summary

We have learned about Discrete-Time Markov Chains (DTMCs) and discrete-time queueing systems in Chapter 3. In this chapter, we will look at continuous-time queueing systems, which were first developed to analyze telephone networks but has broader applications in communication networks, manufacturing, and transportation. The focus of this chapter is on the basics of continuous-time queueing theory and its application to communication networks. As in the case of discrete-time queueing systems, continuous-time queueing systems will be analyzed by relating them to Markov chains. For this purpose, we will first introduce Continuous-Time Markov Chains (CTMCs) and related concepts, such as the global balance equation, the local balance equation, and the Foster–Lyapunov theorem for CTMCs. Then, we will introduce and study simple queueing models including the M/M/1 queue, the M/GI/1 queue, the GI/GI/1 queue, and the Jackson network, as well as important concepts such as reversibility and insensitivity to service time distributions. Finally, we will relate CTMCs and queueing theory to the study of connection-level stability in the Internet, distributed admission control, telephone networks, and P2P file-sharing protocols. The following questions will be answered in this chapter.

Summary

In this chapter, we explore the relationship between the algorithms discussed in the previous chapters and the protocols used in the Internet and wireless networks today. We will first discuss protocols used in the wired Internet, and then discuss protocols used in wireless networks.

It is important to note that Internet congestion control protocols were not designed using the optimization formulation of the resource allocation problem discussed earlier. The predominant concern while designing these protocols was to minimize the risk of congestion collapse, i.e., large-scale buffer overflows, and hence they tended to be rather conservative in their behaviors. Even though the current Internet protocols were not designed with clearly defined fairness and stability ideas in mind, they bear a strong resemblance to the ideas of fair resource allocation that we have discussed so far. In fact, the utility maximization methods presented earlier provide a solid framework that could be for understanding the operation of these congestion control algorithms. Further, going forward, the utility maximization approach seems like a natural candidate framework that could be used to modify existing protocols to adapt to the evolution of the Internet as it continues to grow faster.

In our earlier discussion on network utility maximization for the Internet, we assumed that routes are Axed. In this chapter, we discuss how the Internet chooses a route for each source-destination pair. Further, we also discuss the addressing protocol used in the Internet which allows one to implement routing protocols.

Summary

As we have seen so far, the Internet is a collection of physical links and routers that communicate with each other using a common set of protocols. Many applications run on top of this physical network. Some of these applications involve a collection of computers (called peers), which form a logical overlay network on top of the physical Internet. When communication takes place between the peers, the underlying physical network is transparent to them, and so the communication appears as though it takes place between the peers without the participation of any intermediate nodes. Hence, these types of networks are called Peer-to-Peer (P2P) networks.

Typically, P2P networks are used for high bandwidth data transfer due to its scalability. In a traditional client-server system, the capacity of the system is limited by the bandwidth of the server. In a P2P network, peers upload data to other peers while receiving the data, so the network capacity increases with the number of peers. In this chapter, we will introduce three P2P applications: Distributed Hash Tables (DHTs), file-sharing applications, and streaming systems. A DHT protocol is a distributed database which stores (key, value) pairs at participating peers and facilitates the retrieval of content location. In filesharing applications, peers who are interested in the same content (such as a movie or a music file) exchange content pieces among themselves.

Summary

In earlier chapters, we considered distributed resource allocation algorithms for ad hoc wireless networks. We showed that there exist algorithms, called throughput-optimal algorithms, that maximize the network throughput. However, we do not yet have a simple expression for the total amount of information that can be transferred in an ad hoc wireless network. The problem of computing such an expression is challenging due to interference caused by simultaneous wireless transmissions. This chapter focuses on understanding the limitations on network throughput imposed by wireless interference. We will introduce geometric random graph models of wireless networks, in which n wireless nodes are distributed over a geographical area. Two nodes can communicate if the distance between them is smaller than a threshold and if no nodes near the receiver are transmitting at the same time. The capacity region of such a network is difficult to characterize exactly. Therefore, we will study the network capacity when the number of wireless nodes becomes large. We will show that, in such an asymptotic regime, one can obtain expressions for the network capacity that are asymptotically correct in a sense that will be made precise. A key insight of this chapter is that the throughput per source diminishes as the number of wireless nodes (in a fixed geographical area) increases because of wireless interference. The following questions will be addressed in this chapter.

Summary

In this chapter, we will develop a mathematical formulation of the problem of resource allocation in the Internet. A large communication network such as the Internet can be viewed as a collection of communication links shared by many sources. Congestion control algorithms are protocols that allocate the available network resources in a fair, distributed, and stable manner among the sources. In this chapter, we will introduce the network utility maximization formulation for resource allocation in the Internet, where each source is associated with a utility function Ur(xr), and xr is the transmission rate allocated to source r. The goal of fair resource allocation is to maximize the net utility Σr Ur (xr) subject to resource constraints. We will derive distributed, congestion control algorithms that solve the network utility maximization problem. In a later chapter, we will discuss the relationship between the mathematical models developed in this chapter to transport layer protocols used in the Internet. Optimality and stability of the congestion control algorithms will be established using convex optimization and control theory. We will also introduce a game-theoretical view of network utility maximization and study the impact of strategic users on the efficiency of network utility maximization. Finally, routing and IP addressing will be discussed. The following key questions will be answered in this chapter.

Distributed, Networked and Mobile Computing

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Frontmatter

3 - Smartphone batteries

Summary

15 - Example scenarios for energy optimization

Summary

Smartphone Energy Consumption

Process Algebra: Equational Theories of Communicating Processes

10 - Asymptotic analysis of queues

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I - Network architecture and algorithms

4 - Scheduling in packet switches

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Frontmatter

Contents

Preface

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1 - Introduction

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3 - Links: statistical multiplexing and queues

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5 - Scheduling in wireless networks

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9 - Queueing theory in continuous time

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Index

7 - Network protocols

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8 - Peer-to-peer networks

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11 - Geometric random graph models of wireless networks

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2 - Mathematics of Internet architecture

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