Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis

Even with impressive advances in automated formal methods, certain problems in system verification and synthesis remain challenging. Examples include the verification of quantitative properties of software involving constraints on timing and energy consumption, and the automatic synthesis of systems from specifications. The major challenges include environment modeling, incompleteness in specifications, and the complexity of underlying decision problems. This position paper proposes sciduction, an approach to tackle these challenges by integrating inductive inference, deductive reasoning, and structure hypotheses. Deductive reasoning, which leads from general rules or concepts to conclusions about specific problem instances, includes techniques such as logical inference and constraint solving. Inductive inference, which generalizes from specific instances to yield a concept, includes algorithmic learning from examples. Structure hypotheses are used to define the class of artifacts, such as invariants or program fragments, generated during verification or synthesis. Sciduction constrains inductive and deductive reasoning using structure hypotheses, and actively combines inductive and deductive reasoning: for instance, deductive techniques generate examples for learning, and inductive reasoning is used to guide the deductive engines. We illustrate this approach with three applications: (i) timing analysis of software; (ii) synthesis of loop-free programs, and (iii) controller synthesis for hybrid systems. Some future applications are also discussed

Transiently Powered Computers

Demand for compact, easily deployable, energy-efficient computers has driven the development of general-purpose transiently powered computers (TPCs) that lack both batteries and wired power, operating exclusively on energy harvested from their surroundings. TPCs\u27 dependence solely on transient, harvested power offers several important design-time benefits. For example, omitting batteries saves board space and weight while obviating the need to make devices physically accessible for maintenance. However, transient power may provide an unpredictable supply of energy that makes operation difficult. A predictable energy supply is a key abstraction underlying most electronic designs. TPCs discard this abstraction in favor of opportunistic computation that takes advantage of available resources. A crucial question is how should a software-controlled computing device operate if it depends completely on external entities for power and other resources? The question poses challenges for computation, communication, storage, and other aspects of TPC design. The main idea of this work is that software techniques can make energy harvesting a practicable form of power supply for electronic devices. Its overarching goal is to facilitate the design and operation of usable TPCs. This thesis poses a set of challenges that are fundamental to TPCs, then pairs these challenges with approaches that use software techniques to address them. To address the challenge of computing steadily on harvested power, it describes Mementos, an energy-aware state-checkpointing system for TPCs. To address the dependence of opportunistic RF-harvesting TPCs on potentially untrustworthy RFID readers, it describes CCCP, a protocol and system for safely outsourcing data storage to RFID readers that may attempt to tamper with data. Additionally, it describes a simulator that facilitates experimentation with the TPC model, and a prototype computational RFID that implements the TPC model. To show that TPCs can improve existing electronic devices, this thesis describes applications of TPCs to implantable medical devices (IMDs), a challenging design space in which some battery-constrained devices completely lack protection against radio-based attacks. TPCs can provide security and privacy benefits to IMDs by, for instance, cryptographically authenticating other devices that want to communicate with the IMD before allowing the IMD to use any of its battery power. This thesis describes a simplified IMD that lacks its own radio, saving precious battery energy and therefore size. The simplified IMD instead depends on an RFID-scale TPC for all of its communication functions. TPCs are a natural area of exploration for future electronic design, given the parallel trends of energy harvesting and miniaturization. This work aims to establish and evaluate basic principles by which TPCs can operate

Automatic prediction of computational resource consumption for efficient task migration in cloud

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 2. 백윤흥.In order to accommodate the high demand for performance in smartphones, mobile cloud computing techniques, which aim to enhance a smartphone's performance through utilizing powerful cloud servers, were suggested. Among such techniques, execution offloading, which migrates a thread between a mobile device and a server, is often employed. In such execution offloading techniques, it is typical to dynamically decide what code part is to be offloaded through decision making algorithms. In order to achieve optimal offloading performance, however, the gain and cost of offloading must be predicted accurately for such algorithms. Previous works did not try hard to do this because it is usually expensive to make an accurate prediction. Moreover, existing schemes completely ignore the costs of cloud resources by assuming that idle servers are always available for free of charge. These unrealistic assumptions make each server run only a small load to achieve the guaranteed high offload performance. Therefore, these schemes cannot be applied to real-world commercial clouds which aim to minimize the operation costs by maximizing the server throughput, and then charge users for their resource usage. Thus in this dissertation, I present Mantis, a framework for predicting the Computational Resource Consumption(CRC) of Android applications on given inputs accurately, and efficiently. CRC synergistically combines techniques from program analysis and machine learning. It constructs concise CRC models by choosing from many program execution features only a handful that are most correlated with the program's CRC metric yet can be evaluated efficiently from the program's input. I apply program slicing to reduce evaluation time of a feature and automatically generate executable code snippets for efficiently evaluating features. Using the techniques, I empirically show they enhance the performance of offloading. Lately, I propose CMcloud, a novel cost-effective mobile-to-cloud offloading platform, which works nicely under the real-world cloud environments. CMcloud minimizes both the server costs and the user service fee by offloading as many mobile applications to a single server as possible, while satisfying the target performance of all applications.Abstract i Chapter 1 Introduction 1 1.1 Mobile Execution Offloading . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Dynamic Code Partitioning . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Cost-effectivity of Mobile Execution Offloading . . . . . . . . . . . . 3 1.4 Dissertation Contributions and Outline . . . . . . . . . . . . . . . . . 4 Chapter 2 Mantis: Efficient Predictions of Execution Time, Energy Usage, Memory Usage and Network Usage on Smart Mobile Devices 6 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 Feature Instrumentation . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.4 CRC Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.5 Predictor Code Generation . . . . . . . . . . . . . . . . . . . . . . . 15 2.5.1 Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.5.2 Slicer Challenges . . . . . . . . . . . . . . . . . . . . . . . . 17 2.5.3 Slicer Design . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.6 Implementations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.7 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.7.1 Evaluation Environment . . . . . . . . . . . . . . . . . . . . 24 2.7.2 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . 26 2.8 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Chapter 3 Precise Execution Offloading for Applications with Dynamic Behavior in Mobile Cloud Computing 40 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.2 Background & Motivation . . . . . . . . . . . . . . . . . . . . . . . 41 3.2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.2.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.3 f Mantis : Automatically generation of accurate and efficient performance predictor for mobile execution offloading . . . . . . . . . . . . 48 3.3.1 Performance predictor generation overview . . . . . . . . . . 49 3.3.2 Profiler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.3.3 Predictor Generator . . . . . . . . . . . . . . . . . . . . . . 50 3.4 Dynamic code partitioning with predictor generated by f Mantis . . . 52 3.4.1 Architecture for our solver . . . . . . . . . . . . . . . . . . . 52 3.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.5.1 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.5.2 Evaluation Environment . . . . . . . . . . . . . . . . . . . . 55 3.5.3 Experimental results . . . . . . . . . . . . . . . . . . . . . . 56 3.6 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 Chapter 4 CMcloud: Cloud Platform for Cost-Effective Offloading of Mobile Applications 73 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.2 Backgrounds and Limitations . . . . . . . . . . . . . . . . . . . . . . 75 4.2.1 Basic Offload Mechanisms . . . . . . . . . . . . . . . . . . . 76 4.2.2 Limitations of Existing schemes . . . . . . . . . . . . . . . . 77 4.3 CMcloud offloading . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.3.1 Design Goals . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.3.2 Operation Model . . . . . . . . . . . . . . . . . . . . . . . . 80 4.3.3 Architecture Model . . . . . . . . . . . . . . . . . . . . . . . 82 4.4 CMcloud mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 84 4.4.1 Reference-model Server Profiling . . . . . . . . . . . . . . . 84 4.4.2 Performance Estimation . . . . . . . . . . . . . . . . . . . . 85 4.4.3 Performance Monitoring . . . . . . . . . . . . . . . . . . . . 92 4.4.4 Migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 4.4.5 Cost-aware Application Scheduling in Cloud . . . . . . . . . 94 4.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.5.1 Estimating Target CPI stack . . . . . . . . . . . . . . . . . . 96 4.5.2 Predicting Instruction Count . . . . . . . . . . . . . . . . . . 98 4.5.3 Cost Effectiveness with QoS requirements . . . . . . . . . . . 98 4.5.4 Offloading/migration Overhead . . . . . . . . . . . . . . . . 102 4.6 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 4.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 Chapter 5 Conculsion 105 초록 119 vDocto

Computer Aided Verification

This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency