Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

This article discusses the challenges in computer systems research posed by the emerging field of pervasive computing. It first examines the relationship of this new field to its predecessors: distributed systems and mobile computing. It then identifies four new research thrusts: effective use of smart spaces, invisibility, localized scalability, and masking uneven conditioning. Next, it sketches a couple of hypothetical pervasive computing scenarios, and uses them to identify key capabilities missing from today's systems. The article closes with a discussion of the research necessary to develop these capabilities.

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Pervasive computing: vision and challenges

This paper presents MAUI, a system that enables fine-grained energy-aware offload of mobile code to the infrastructure. Previous approaches to these problems either relied heavily on programmer support to partition an application, or they were coarse-grained requiring full process (or full VM) migration. MAUI uses the benefits of a managed code environment to offer the best of both worlds: it supports fine-grained code offload to maximize energy savings with minimal burden on the programmer. MAUI decides at run-time which methods should be remotely executed, driven by an optimization engine that achieves the best energy savings possible under the mobile device's current connectivity constrains. In our evaluation, we show that MAUI enables: 1) a resource-intensive face recognition application that consumes an order of magnitude less energy, 2) a latency-sensitive arcade game application that doubles its refresh rate, and 3) a voice-based language translation application that bypasses the limitations of the smartphone environment by executing unsupported components remotely.

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MAUI: making smartphones last longer with code offload

Context-aware computing is a mobile computing paradigm in which applications can discover and take advantage of contextual information (such as user location, time of day, nearby people and devices, and user activity) Since it was proposed about a decade ago, many researchers have studied this topic and built several context-aware applications to demonstrate the usefulness of this new technology Context-aware applications (or the system infrastructure to support them), however, have never been widely available to everyday users In this survey of research on context-aware systems and applications, we looked in depth at the types of context used and models of context information, at systems that support collecting and disseminating context, and at applications that adapt to the changing context Through this survey, it is clear that context-aware research is an old but rich area for research The difficulties and possible solutions we outline serve as guidance for researchers hoping to make context-aware computing a reality

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A Survey of Context-Aware Mobile Computing Research

Together with an explosive growth of the mobile applications and emerging of cloud computing concept, mobile cloud computing (MCC) has been introduced to be a potential technology for mobile services. MCC integrates the cloud computing into the mobile environment and overcomes obstacles related to the performance (e.g., battery life, storage, and bandwidth), environment (e.g., heterogeneity, scalability, and availability), and security (e.g., reliability and privacy) discussed in mobile computing. This paper gives a survey of MCC, which helps general readers have an overview of the MCC including the definition, architecture, and applications. The issues, existing solutions, and approaches are presented. In addition, the future research directions of MCC are discussed. Copyright © 2011 John Wiley & Sons, Ltd.

A survey of mobile cloud computing: architecture, applications, and approaches

In this paper we show that application-aware adaptation, a collaborative partnership between the operating system and applications, offers the most general and effective approach to mobile information access. We describe the design of Odyssey, a prototype implementing this approach, and show how it supports concurrent execution of diverse mobile applications. We identify agility as a key attribute of adaptive systems, and describe how to quantify and measure it. We present the results of our evaluation of Odyssey, indicating performance improvements up to a factor of 5 on a benchmark of three applications concurrently using remote services over a network with highly variable bandwidth.

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Agile application-aware adaptation for mobility

In this paper, we demonstrate that a collaborative relationship between the operating system and applications can be used to meet user-specified goals for battery duration. We first show how applications can dynamically modify their behavior to conserve energy. We then show how the Linux operating system can guide such adaptation to yield a battery-life of desired duration. By monitoring energy supply and demand, it is able to select the correct tradeoff between energy conservation and application quality. Our evaluation shows that this approach can meet goals that extend battery life by as much as 30%.

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Energy-aware adaptation for mobile applications

We describe the design and implementation of PowerScope, a tool for profiling energy usage by applications. PowerScope maps energy consumption to program structure, in much the same way that CPU profilers map processor cycles to specific processes and procedures. Our approach combines hardware instrumentation to measure current level with kernel software support to perform statistical sampling of system activity. Postprocessing software maps the sample data to program structure and produces a profile of energy usage by process and procedure. Using PowerScope, we have been able to reduce the energy consumption of an adaptive video playing application by 46%.

PowerScope: a tool for profiling the energy usage of mobile applications

In this paper, we propose cyber foraging: a mechanism to augment the computational and storage capabilities of mobile devices. Cyber foraging uses opportunistically discovered servers in the environment to improve the performance of interactive applications and distributed file systems on mobile clients. We show how the performance of distributed file systems can be improved by staging data at these servers even though the servers are not trusted. We also show how the performance of interactive applications can be improved via remote execution. Finally, we present VERSUDS: a virtual interface to heteregeneous service discovery protocols that can be used to discover these servers.

https://apollo.smu.edu.sg/papers/cyberforaging_sigops.pdf

The case for cyber foraging

We describe Spectra, a remote execution system for battery-powered clients used in pervasive computing. Spectra enables applications to combine the mobility of small devices with the greater processing power of static compute servers. Spectra is self-tuning: it monitors both application resource usage and the availability of resources in the environment, and dynamically determines how and where to execute application components. In making this determination, Spectra balances the competing goals of performance, energy conservation, and application quality. We have validated Spectra's approach on the Compaq Itsy v2.2 and IBM ThinkPad 560X using a speech recognizer a document preparation system, and a natural language translator. Our results confirm that Spectra almost always selects the best execution plan, and that its few suboptimal choices are very close to optimal.

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Jason Flinn

Papers

Agile application-aware adaptation for mobility

Energy-aware adaptation for mobile applications

PowerScope: a tool for profiling the energy usage of mobile applications

The case for cyber foraging

Balancing performance, energy, and quality in pervasive computing