DeltaFS: Pursuing Zero Update Overhead via Metadata-Enabled Delta Compression for Log-structured File System on Mobile Devices

Data compression has been widely adopted to release mobile devices from intensive write pressure. Delta compression is particularly promising for its high compression efficacy over conventional compression methods. However, this method suffers from non-trivial system overheads incurred by delta maintenance and read penalty, which prevents its applicability on mobile devices. To this end, this paper proposes DeltaFS, a metadata-enabled Delta compression on log-structured File System for mobile devices, to achieve utmost compressing efficiency and zero hardware costs. DeltaFS smartly exploits the out-of-place updating ability of Log-structured File System (LFS) to alleviate the problems of write amplification, which is the key bottleneck for delta compression implementation. Specifically, DeltaFS utilizes the inline area in file inodes for delta maintenance with zero hardware cost, and integrates an inline area manage strategy to improve the utilization of constrained inline area. Moreover, a complimentary delta maintenance strategy is incorporated, which selectively maintains delta chunks in the main data area to break through the limitation of constrained inline area. Experimental results show that DeltaFS substantially reduces write traffics by up to 64.8\%, and improves the I/O performance by up to 37.3\%

스마트폰을 위한 사용자 중심 최적화 기법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 8. 김지홍.Recently, smartphones have become an integral part of everyday life. In addition, as smartphone users are expecting their devices to deliver PC-level user experience, numerous design requirements are rapidly emerging with the technology development. In order to meet the demanding system design requirements, many conventional techniques, whose basic concepts are almost same as those of the traditional computing devices such as PCs, are applied to the smartphones. However, as truly personalized and interaction-oriented devices, the smartphones have distinct characteristics which distinguish the devices from traditional computing devices. Therefore, it is highly required to understand and to analyze the distinctive inherent characteristics of smartphones for a provision of a new novel opportunity for system optimization. In this dissertation, we propose new user-centric optimization techniques to satisfy various design requirements of smartphones such as energy efficiency, effective thermal management and rapid responsiveness without any performance degradation by taking advantage of high-level information from the smartphone users. We first introduce a new definition of the response time, the user-perceived response time, which is known to be a critical metric for the quality of user experience of the smartphone. We also present a user-perceived response time analyzer for Android-based smartphones, which can identify the user-perceived response time of smartphone apps during run time. Based on on-line identification of the user-perceived response time, we propose a novel CPU power management framework, which enables more aggressive low-power techniques to be employed while executing display-insensitive parts of task executions. Our experimental results on a smartphone development board show that the proposed technique can reduce the CPU energy consumption by up to 65.6% over the Android's default ondemand cpufreq governor. Second, we propose a novel dynamic thermal management (DTM) technique for smartphones, which ensures the quality of user experience during the execution of display-sensitive parts without any thermal violations. In the proposed DTM technique, in order to identify that the current execution could affect the visible portion of the display, we develop a user-perceived response time prediction model for each interactive session based on statistical analysis of the user-perceived response times for the past interactive sessions. By exploiting the on-line prediction of the user-perceived response time, the proposed DTM technique carefully makes the DTM decisions for a higher quality of user experience. Our experimental results on an ODROID-XU+E board show that the proposed technique can improve the user-perceived performance by up to 37.96% over the Androids default DTM policy. Third, we present a personalized optimization framework for smartphones which can provide valuable high-level hints for optimizing the smartphone design requirements. The main goal of the proposed framework is collecting an app usage log of a smartphone user and analyzing the collected log so that particular usage patterns, if any, can be effectively identified. In order to identify app usage patterns, a couple of app usage models are also proposed. Based on the app usage models developed, we also propose a launching experience optimization which avoids unnecessary app restarts considering the detrimental effects of the restart on user experience from the perspective of performance, energy, and loss of previous state. Our experimental results on the Nexus S Android reference phones show that our proposed optimization technique can avoid unnecessary application restarts by up to 78.4% over the default LRU-based policy of the Android platform. Based on the evaluation for each technique, we verified that the user-centric optimization techniques improve the quality of user experience in terms of energy efficiency, effective thermal management and rapid responsiveness over previous system-centric techniques.Chapter I. Introduction 13 1.1 Motivation 13 1.1.1 Distinctive Characteristics of Smartphone 13 1.1.2 Existing Optimization Techniques for Smartphones and Their Limitations 16 1.2 Dissertation Goals 19 1.3 Contributions 20 1.4 Dissertation Structure 21 Chapter II. Related Work 23 2.1 Power Management Techniques 23 2.2 User Behavior Characterization 25 2.3 Launching Time Optimization Techniques 26 Chapter III. CPU Power Management Technique Using User-Perceived Response Time Analysis 31 3.1 Motivation 31 3.2 Design and Implementation of URA 36 3.2.1 Overview 36 3.2.2 User-Perceived Resoponse Time Identification 38 3.2.3 URA-based CPU Power Optimization Technique 41 3.3 Experimental Results 42 Chapter IV. SmartDTM: Smart Thermal Management for Smartphones 49 4.1 Overview 49 4.2 Motivation 52 4.3 Design and Implementation of SmartDTM 56 4.3.1 Basic Idea 56 4.3.2 Architectural Overview 59 4.3.3 User-Perceived Response Time Prediction 62 4.3.4 Worst-Case Temperature Estimation Model 64 4.4 Experimental Results 66 4.4.1 Experimental Environment 66 4.4.2 Performance Evaluation 68 4.4.3 Temperature Evaluation 70 Chapter V. Personalized Optimization Framework 77 5.1 Motivation 77 5.2 Design and Implementation of POA 78 5.2.1 Design Overview 78 5.2.2 App Usage Modeling Module 82 5.2.3 Usage Model-Based Optimization Module 83 5.3 App Usage Model Construction 83 5.3.1 P-AUM: Pattern-based App Usage Model 83 5.3.2 C-AUM: Clustering-based App Usage Model 87 Chapter VI. AUM-based Launching Experience Optimization Technique 96 6.1 Motivation 96 6.1.1 Impact of Cold Starts on App Launching Experience 98 6.1.2 Android Task Management Scheme 101 6.1.3 Problem of the LRU-based Task Killer 102 6.2 AUM-based Launching Experience Optimization 106 6.2.1 App Usage (AU)-aware Task Killer 106 6.2.2 App Usage (AU)-aware Prelauncher 107 6.3 Experimental Results 109 6.3.1 Experiment Environment 109 6.3.2 Results of Task Killing Mechanism Optimization 110 6.3.3 Results of Prelaunching Technique 118 Chapter VII. Conclusions 119 7.1 Summary and Conclusions 119 7.2 Future Work 121 7.2.1 Improving Prediction Accuracy of the AUMs Using Context Information 121 7.2.2 Integrated Intra- and Inter-App Approaches for User-Centric Optimizations 123 7.2.3 User-Centric Optimizations for The Other Design Requirements 124 Bibliography 126 국문 초록 132Docto

New Data Protection Abstractions for Emerging Mobile and Big Data Workloads

Two recent shifts in computing are challenging the effectiveness of traditional approaches to data protection. Emerging machine learning workloads have complex access patterns and unique leakage characteristics that are not well supported by existing protection approaches. Second, mobile operating systems do not provide sufficient support for fine grained data protection tools forcing users to rely on individual applications to correctly manage and protect data. My thesis is that these emerging workloads have unique characteristics that we can leverage to build new, more effective data protection abstractions. This dissertation presents two new data protection systems for machine learning work-loads and a new system for fine grained data management and protection on mobile devices. First is Sage, a differentially private machine learning platform addressing the two primary challenges of differential privacy: running out of budget and the privacy utility tradeoff. The second system, Pyramid, is the first selective data system. Pyramid leverages count featurization to reduce the amount of data exposed while training classification models by two orders of magnitude. The final system, Pebbles, provides users with logical data objects as a new fine grained data management and protection primitive allowing data management at a higher level of abstraction. Pebbles, leverages high level storage abstractions in mobile operating systems to discover user recognizable application level data objects in unmodified mobile applications

Securing Arm Platform: From Software-Based To Hardware-Based Approaches

With the rapid proliferation of the ARM architecture on smart mobile phones and Internet of Things (IoT) devices, the security of ARM platform becomes an emerging problem. In recent years, the number of malware identified on ARM platforms, especially on Android, shows explosive growth. Evasion techniques are also used in these malware to escape from being detected by existing analysis systems. In our research, we first present a software-based mechanism to increase the accuracy of existing static analysis tools by reassembleable bytecode extraction. Our solution collects bytecode and data at runtime, and then reassemble them offline to help static analysis tools to reveal the hidden behavior in an application. Further, we implement a hardware-based transparent malware analysis framework for general ARM platforms to defend against the traditional evasion techniques. Our framework leverages hardware debugging features and Trusted Execution Environment (TEE) to achieve transparent tracing and debugging with reasonable overhead. To learn the security of the involved hardware debugging features, we perform a comprehensive study on the ARM debugging features and summarize the security implications. Based on the implications, we design a novel attack scenario that achieves privilege escalation via misusing the debugging features in inter-processor debugging model. The attack has raised our concern on the security of TEEs and Cyber-physical System (CPS). For a better understanding of the security of TEEs, we investigate the security of various TEEs on different architectures and platforms, and state the security challenges. A study of the deploying the TEEs on edge platform is also presented. For the security of the CPS, we conduct an analysis on the real-world traffic signal infrastructure and summarize the security problems

A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection

Towards Modular and Flexible Access Control on Smart Mobile Devices

Smart mobile devices, such as smartphones and tablets, have become an integral part of our daily personal and professional lives. These devices are connected to a wide variety of Internet services and host a vast amount of applications, which access, store and process security- and privacy-sensitive data. A rich set of sensors, ranging from microphones and cameras to location and acceleration sensors, allows these applications and their back end services to reason about user behavior. Further, enterprise administrators integrate smart mobile devices into their IT infrastructures to enable comfortable work on the go. Unsurprisingly, this abundance of available high-quality information has made smart mobile devices an interesting target for attackers, and the number of malicious and privacy-intrusive applications has steadily been rising. Detection and mitigation of such malicious behavior are in focus of mobile security research today. In particular, the Android operating system has received special attention by both academia and industry due to its popularity and open-source character. Related work has scrutinized its security architecture, analyzed attack vectors and vulnerabilities and proposed a wide variety of security extensions. While these extensions have diverse goals, many of them constitute modifications of the Android operating system and extend its default permission-based access control model. However, they are not generic and only address specific security and privacy concerns. The goal of this dissertation is to provide generic and extensible system-centric access control architectures, which can serve as a solid foundation for the instantiation of use-case specific security extensions. In doing so, we enable security researchers, enterprise administrators and end users to design, deploy and distribute security extensions without further modification of the underlying operating system. To achieve this goal, we first analyze the mobile device ecosystem and discuss how Android's security architecture aims to address its inherent threats. We proceed to survey related work on Android security, focusing on system-centric security extensions, and derive a set of generic requirements for extensible access control architectures targeting smart mobile devices. We then present two extensible access control architectures, which address these requirements by providing policy-based and programmable interfaces for the instantiation of use-case specific security solutions. By implementing a set of practical use-cases, ranging from context-aware access control, dynamic application behavior analysis to isolation of security domains we demonstrate the advantages of system-centric access control architectures over application-layer approaches. Finally, we conclude this dissertation by discussing an alternative approach, which is based on application-layer deputies and can be deployed whenever practical limitations prohibit the deployment of system-centric solutions

Metafore mobilnih komunikacija ; Метафоры мобильной связи.

Mobilne komunikacije su polje informacione i komunikacione tehnologije koje karakteriše brzi razvoj i u kome se istraživanjem u analitičkim okvirima kognitivne lingvistike, zasnovanom na uzorku od 1005 odrednica, otkriva izrazito prisustvo metafore, metonimije, analogije i pojmovnog objedinjavanja. Analiza uzorka reči i izraza iz oblasti mobilnih medija, mobilnih operativnih sistema, dizajna korisničkih interfejsa, terminologije mobilnih mreža, kao i slenga i tekstizama koje upotrebljavaju korisnici mobilnih naprava ukazuje da pomenuti kognitivni mehanizmi imaju ključnu ulogu u olakšavanju interakcije između ljudi i širokog spektra mobilnih uređaja sa računarskim sposobnostima, od prenosivih računara i ličnih digitalnih asistenata (PDA), do mobilnih telefona, tableta i sprava koje se nose na telu. Ti mehanizmi predstavljaju temelj razumevanja i nalaze se u osnovi principa funkcionisanja grafičkih korisničkih interfejsa i direktne manipulacije u računarskim okruženjima. Takođe je analiziran i poseban uzorak od 660 emotikona i emođija koji pokazuju potencijal za proširenje značenja, imajući u vidu značaj piktograma za tekstualnu komunikaciju u vidu SMS poruka i razmenu tekstualnih sadržaja na društvenim mrežama kojima se redovno pristupa putem mobilnih uređaja...Mobile communications are a fast-developing field of information and communication technology whose exploration within the analytical framework of cognitive linguistics, based on a sample of 1005 entries, reveals the pervasive presence of metaphor, metonymy analogy and conceptual integration. The analysis of the sample consisting of words and phrases related to mobile media, mobile operating systems and interface design, the terminology of mobile networking, as well as the slang and textisms employed by mobile gadget users shows that the above cognitive mechanisms play a key role in facilitating interaction between people and a wide range of mobile computing devices from laptops and PDAs to mobile phones, tablets and wearables. They are the cornerstones of comprehension that are behind the principles of functioning of graphical user interfaces and direct manipulation in computing environments. A separate sample, featuring a selection of 660 emoticons and emoji, exhibiting the potential for semantic expansion was also analyzed, in view of the significance of pictograms for text-based communication in the form of text messages or exchanges on social media sites regularly accessed via mobile devices..

Code offloading in opportunistic computing

With the advent of cloud computing, applications are no longer tied to a single device, but they can be migrated to a high-performance machine located in a distant data center. The key advantage is the enhancement of performance and consequently, the users experience. This activity is commonly referred computational offloading and it has been strenuously investigated in the past years. The natural candidate for computational offloading is the cloud, but recent results point out the hidden costs of cloud reliance in terms of latency and energy; Cuervo et. al. illustrates the limitations on cloud-based computational offloading based on WANs latency times. The dissertation confirms the results of Cuervo et. al. and illustrates more use cases where the cloud may not be the right choice. This dissertation addresses the following question: is it possible to build a novel approach for offloading the computation that overcomes the limitations of the state-of-the-art? In other words, is it possible to create a computational offloading solution that is able to use local resources when the Cloud is not usable, and remove the strong bond with the local infrastructure? To this extent, I propose a novel paradigm for computation offloading named anyrun computing, whose goal is to use any piece of higher-end hardware (locally or remotely accessible) to offloading a portion of the application. With anyrun computing I removed the boundaries that tie the solution to an infrastructure by adding locally available devices to augment the chances to succeed in offloading. To achieve the goals of the dissertation it is fundamental to have a clear view of all the steps that take part in the offloading process. To this extent, I firstly provided a categorization of such activities combined with their interactions and assessed the impact on the system. The outcome of the analysis is the mapping to the problem to a combinatorial optimization problem that is notoriously known to be NP-Hard. There are a set of well-known approaches to solving such kind of problems, but in this scenario, they cannot be used because they require a global view that can be only maintained by a centralized infrastructure. Thus, local solutions are needed. Moving further, to empirically tackle the anyrun computing paradigm, I propose the anyrun computing framework (ARC), a novel software framework whose objective is to decide whether to offload or not to any resource-rich device willing to lend assistance is advantageous compared to local execution with respect to a rich array of performance dimensions. The core of ARC is the nference nodel which receives a rich set of information about the available remote devices from the SCAMPI opportunistic computing framework developed within the European project SCAMPI, and employs the information to profile a given device, in other words, it decides whether offloading is advantageous compared to local execution, i.e. whether it can reduce the local footprint compared to local execution in the dimensions of interest (CPU and RAM usage, execution time, and energy consumption). To empirically evaluate ARC I presented a set of experimental results on the cloud, cloudlet, and opportunistic domain. In the cloud domain, I used the state of the art in cloud solutions over a set of significant benchmark problems and with three WANs access technologies (i.e. 3G, 4G, and high-speed WAN). The main outcome is that the cloud is an appealing solution for a wide variety of problems, but there is a set of circumstances where the cloud performs poorly. Moreover, I have empirically shown the limitations of cloud-based approaches, specifically, In some circumstances, problems with high transmission costs tend to perform poorly, unless they have high computational needs. The second part of the evaluation is done in opportunistic/cloudlet scenarios where I used my custom-made testbed to compare ARC and MAUI, the state of the art in computation offloading. To this extent, I have performed two distinct experiments: the first with a cloudlet environment and the second with an opportunistic environment. The key outcome is that ARC virtually matches the performances of MAUI (in terms of energy savings) in cloudlet environment, but it improves them by a 50% to 60% in the opportunistic domain