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

The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.

Edge Computing: Vision and Challenges

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

Technological evolution of mobile user equipment (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. A suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling it to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: 1) decision on computation offloading; 2) allocation of computing resource within the MEC; and 3) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Technological evolution of mobile user equipments (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. Suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud (CC). Nevertheless, this option introduces significant execution delay consisting in delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such delay is inconvenient and make the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: i) decision on computation offloading, ii) allocation of computing resource within the MEC, and iii) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

Program analysis tools often produce undesirable output due to various approximations. We present an approach and a system EUGENE that allows user feedback to guide such approximations towards producing the desired output. We formulate the problem of user-guided program analysis in terms of solving a combination of hard rules and soft rules: hard rules capture soundness while soft rules capture degrees of approximations and preferences of users. Our technique solves the rules using an off-the-shelf solver in a manner that is sound (satisfies all hard rules), optimal (maximally satisfies soft rules), and scales to real-world analyses and programs. We evaluate EUGENE on two different analyses with labeled output on a suite of seven Java programs of size 131–198 KLOC. We also report upon a user study involving nine users who employ EUGENE to guide an information-flow analysis on three Java micro-benchmarks. In our experiments, EUGENE significantly reduces misclassified reports upon providing limited amounts of feedback.

/pdf/a-user-guided-approach-to-program-analysis-29ob6zdfne.pdf

A user-guided approach to program analysis

Energy efficiency is a primary constraint in modern systems. Approximate computing is a promising approach that trades quality of result for gains in efficiency and performance. State- of-the-art approximate programming models require extensive manual annotations on program data and operations to guarantee safe execution of approximate programs. The need for extensive manual annotations hinders the practical use of approximation techniques. This paper describes FlexJava, a small set of language extensions, that significantly reduces the annotation effort, paving the way for practical approximate programming. These extensions enable programmers to annotate approximation-tolerant method outputs. The FlexJava compiler, which is equipped with an approximation safety analysis, automatically infers the operations and data that affect these outputs and selectively marks them approximable while giving safety guarantees. The automation and the language–compiler codesign relieve programmers from manually and explicitly an- notating data declarations or operations as safe to approximate. FlexJava is designed to support safety, modularity, generality, and scalability in software development. We have implemented FlexJava annotations as a Java library and we demonstrate its practicality using a wide range of Java applications and by con- ducting a user study. Compared to EnerJ, a recent approximate programming system, FlexJava provides the same energy savings with significant reduction (from 2× to 17×) in the number of annotations. In our user study, programmers spend 6× to 12× less time annotating programs using FlexJava than when using EnerJ.

FlexJava: language support for safe and modular approximate programming

API misuse is a well-known source of bugs. Some of them (e.g., incorrect use of SSL API, and integer overflow of memory allocation size) can cause serious security vulnerabilities (e.g., man-in-the-middle (MITM) attack, and privilege escalation). Moreover, modern APIs, which are large, complex, and fast evolving, are error-prone. However, existing techniques to help finding bugs require manual effort by developers (e.g., providing specification or model) or are not scalable to large real-world software comprising millions of lines of code. In this paper, we present APISAN, a tool that automatically infers correct API usages from source code without manual effort. The key idea in APISAN is to extract likely correct usage patterns in four different aspects (e.g., causal relation, and semantic relation on arguments) by considering semantic constraints. APISAN is tailored to check various properties with security implications. We applied APISAN to 92 million lines of code, including Linux Kernel, and OpenSSL, found 76 previously unknown bugs, and provided patches for all the bugs.

https://www.usenix.org/system/files/conference/usenixsecurity16/sec16_paper_yun.pdf

APISan: Sanitizing {API} Usages through Semantic Cross-Checking

We present Mantis, a framework for predicting the performance of Android applications on given inputs automatically, accurately, and efficiently. A key insight underlying Mantis is that program execution runs often contain features that correlate with performance and are automatically computable efficiently. Mantis synergistically combines techniques from program analysis and machine learning. It constructs concise performance models by choosing from many program execution features only a handful that are most correlated with the program's execution time yet can be evaluated efficiently from the program's input. We apply program slicing to accurately estimate the evaluation cost of a feature and automatically generate executable code snippets for efficiently evaluating features. Our evaluation shows that Mantis predicts the execution time of six Android apps with estimation error in the range of 2.2-11.9% by executing predictor code costing at most 1.3% of their execution time on Galaxy Nexus.

/pdf/mantis-automatic-performance-prediction-for-smartphone-3mpg374xpq.pdf

Mantis: automatic performance prediction for smartphone applications

Mobile applications are becoming increasingly ubiquitous and provide ever richer functionality on mobile devices. At the same time, such devices often enjoy strong connectivity with more powerful machines ranging from laptops and desktops to commercial clouds. This paper presents the design and implementation of CloneCloud, a system that automatically transforms mobile applications to benefit from the cloud. The system is a flexible application partitioner and execution runtime that enables unmodified mobile applications running in an application-level virtual machine to seamlessly off-load part of their execution from mobile devices onto device clones operating in a computational cloud. CloneCloud uses a combination of static analysis and dynamic profiling to optimally and automatically partition an application so that it migrates, executes in the cloud, and re-integrates computation in a fine-grained manner that makes efficient use of resources. Our evaluation shows that CloneCloud can achieve up to 21.2x speedup of smartphone applications we tested and it allows different partitioning for different inputs and networks.

Mayur Naik

Papers

A user-guided approach to program analysis

FlexJava: language support for safe and modular approximate programming

APISan: Sanitizing {API} Usages through Semantic Cross-Checking

Mantis: automatic performance prediction for smartphone applications

CloneCloud: Boosting Mobile Device Applications Through Cloud Clone Execution