Synthesizing Training Data for Object Detection in Indoor Scenes

Detection of objects in cluttered indoor environments is one of the key enabling functionalities for service robots. The best performing object detection approaches in computer vision exploit deep Convolutional Neural Networks (CNN) to simultaneously detect and categorize the objects of interest in cluttered scenes. Training of such models typically requires large amounts of annotated training data which is time consuming and costly to obtain. In this work we explore the ability of using synthetically generated composite images for training state-of-the-art object detectors, especially for object instance detection. We superimpose 2D images of textured object models into images of real environments at variety of locations and scales. Our experiments evaluate different superimposition strategies ranging from purely image-based blending all the way to depth and semantics informed positioning of the object models into real scenes. We demonstrate the effectiveness of these object detector training strategies on two publicly available datasets, the GMU-Kitchens and the Washington RGB-D Scenes v2. As one observation, augmenting some hand-labeled training data with synthetic examples carefully composed onto scenes yields object detectors with comparable performance to using much more hand-labeled data. Broadly, this work charts new opportunities for training detectors for new objects by exploiting existing object model repositories in either a purely automatic fashion or with only a very small number of human-annotated examples.Comment: Added more experiments and link to project webpag

LSM-tree based Database System Optimization using Application-Driven Flash Management

학위논문(석사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2019. 8. 염헌영.Modern data centers aim to take advantage of high parallelism in storage de- vices for I/O intensive applications such as storage servers, cache systems, and key-value stores. Key-value stores are the most typical applications that should provide a highly reliable service with high-performance. To increase the I/O performance of key-value stores, many data centers have actively adopted next- generation storage devices such as Non-Volatile Memory Express (NVMe) based Solid State Devices (SSDs). NVMe SSDs and its protocol are characterized to provide a high degree of parallelism. However, they may not guarantee pre- dictable performance while providing high performance and parallelism. For example, heavily mixed read and write requests can result in performance degra- dation of throughput and response time due to the interference between the requests and internal operations (e.g., Garbage Collection (GC)). To minimize the interference and provide higher performance, this paper presents IsoKV, an isolation scheme for key-value stores by exploiting internal parallelism in SSDs. IsoKV manages the level of parallelism of SSD directly by running application-driven flash management scheme. By storing data with dif- ferent characteristics in each dedicated internal parallel units of SSD, IsoKV re- duces interference between I/O requests. We implement IsoKV on RocksDB and evaluate it using Open-Channel SSD. Our extensive experiments have shown that IsoKV improves overall throughput and response time on average 1.20× and 43% compared with the existing scheme, respectively.최신 데이터 센터는 스토리지 서버, 캐시 시스템 및 Key-Value stores와 같은 I/O 집약적인 애플리케이션을 위한 스토리지 장치의 높은 병렬성을 활용하는 것을 목표로 한다. Key-value stores는 고성능의 고신뢰 서비스를 제공해야 하는 가장 대표적인 응용프로그램이다. Key-value stores의 I/O 성능을 높이기 위해 많은 데 이터 센터가 비휘발성 메모리 익스프레스(NVMe) 기반 SSD(Solid State Devices) 와 같은 차세대 스토리지 장치를 적극적으로 채택하고 있다. NVMe SSD와 그 프 로토콜은 높은 수준의 병렬성을 제공하는 것이 특징이다. 그러나 NVMe SSD가 병렬성을 제공하면서도 예측 가능한 성능을 보장하지는 못할 수 있다. 예를 들어 읽기 및 쓰기 요청이 많이 혼합되면 요청과 내부 작업(예: GC) 사이의 간섭으로 인해 처리량 및 응답 시간의 성능 저하가 발생할 수 있다. 간섭을 최소화하고 성능을 향상시키기 위해 본 연구에서는 Key-value stores를 위한 격리 방식인 IsoKV를 제시한다. IsoKV는 애플리케이션 중심 플래시 저장장 치 관리 방식을 통해 SSD의 병렬화 수준을 직접 관리한다. IsoKV는 SSD의 각 전용 내부 병렬 장치에 서로 다른 특성을 가진 데이터를 저장함으로써 I/O 요청 간의 간섭을 줄인다. 또한 IsoKV는 SSD의 LSM 트리 로직과 데이터 관리를 동기화하 여 GC를 제거한다. 본 연구에서는 RocksDB를 기반으로 IsoKV를 구현하였으며, Open-Channel SSD를 사용하여 성능평가하였다.. 본 연구의 실험 결과에 따르면 IsoKV는 기존의 데이터 저장 방식과 비교하여 평균 1.20× 빠르고 및 43% 감소된 처리량과 응답시간 성능 개선 결과를 얻었다. 관점에서 43% 감소하였다.Abstract Introduction 1 Background 8 Log-Structured Merge tree based Database 8 Open-Channel SSDs 9 Preliminary Experimental Evaluation using oc bench 10 Design and Implementation 14 Overview of IsoKV 14 GC-free flash storage management synchronized with LSM-tree logic 15 I/O type Isolation through Application-Driven Flash Management 17 Dynamic Arrangement of NAND-Flash Parallelism 19 Implementation 21 Evaluation 23 Experimental Setup 23 Performance Evaluation 25 Related Work 31 Conclusion 34 Bibliography 35 초록 40Maste

DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines

Integrated data analysis (IDA) pipelines—that combine data management (DM) and query processing, high-performance computing (HPC), and machine learning (ML) training and scoring—become increasingly common in practice. Interestingly, systems of these areas share many compilation and runtime techniques, and the used—increasingly heterogeneous—hardware infrastructure converges as well. Yet, the programming paradigms, cluster resource management, data formats and representations, as well as execution strategies differ substantially. DAPHNE is an open and extensible system infrastructure for such IDA pipelines, including language abstractions, compilation and runtime techniques, multi-level scheduling, hardware (HW) accelerators, and computational storage for increasing productivity and eliminating unnecessary overheads. In this paper, we make a case for IDA pipelines, describe the overall DAPHNE system architecture, its key components, and the design of a vectorized execution engine for computational storage, HW accelerators, as well as local and distributed operations. Preliminary experiments that compare DAPHNE with MonetDB, Pandas, DuckDB, and TensorFlow show promising results

Leveraging Non-Volatile Memory in Modern Storage Management Architectures

Non-volatile memory technologies (NVM) introduce a novel class of devices that combine characteristics of both storage and main memory. Like storage, NVM is not only persistent, but also denser and cheaper than DRAM. Like DRAM, NVM is byte-addressable and has lower access latency. In recent years, NVM has gained a lot of attention both in academia and in the data management industry, with views ranging from skepticism to over excitement. Some critics claim that NVM is not cheap enough to replace flash-based SSDs nor is it fast enough to replace DRAM, while others see it simply as a storage device. Supporters of NVM have observed that its low latency and byte-addressability requires radical changes and a complete rewrite of storage management architectures. This thesis takes a moderate stance between these two views. We consider that, while NVM might not replace flash-based SSD or DRAM in the near future, it has the potential to reduce the gap between them. Furthermore, treating NVM as a regular storage media does not fully leverage its byte-addressability and low latency. On the other hand, completely redesigning systems to be NVM-centric is impractical. Proposals that attempt to leverage NVM to simplify storage management result in completely new architectures that face the same challenges that are already well-understood and addressed by the traditional architectures. Therefore, we take three common storage management architectures as a starting point, and propose incremental changes to enable them to better leverage NVM. First, in the context of log-structured merge-trees, we investigate the impact of storing data in NVM, and devise methods to enable small granularity accesses and NVM-aware caching policies. Second, in the context of B+Trees, we propose to extend the buffer pool and describe a technique based on the concept of optimistic consistency to handle corrupted pages in NVM. Third, we employ NVM to enable larger capacity and reduced costs in a index+log key-value store, and combine it with other techniques to build a system that achieves low tail latency. This thesis aims to describe and evaluate these techniques in order to enable storage management architectures to leverage NVM and achieve increased performance and lower costs, without major architectural changes.:1 Introduction 1.1 Non-Volatile Memory 1.2 Challenges 1.3 Non-Volatile Memory & Database Systems 1.4 Contributions and Outline 2 Background 2.1 Non-Volatile Memory 2.1.1 Types of NVM 2.1.2 Access Modes 2.1.3 Byte-addressability and Persistency 2.1.4 Performance 2.2 Related Work 2.3 Case Study: Persistent Tree Structures 2.3.1 Persistent Trees 2.3.2 Evaluation 3 Log-Structured Merge-Trees 3.1 LSM and NVM 3.2 LSM Architecture 3.2.1 LevelDB 3.3 Persistent Memory Environment 3.4 2Q Cache Policy for NVM 3.5 Evaluation 3.5.1 Write Performance 3.5.2 Read Performance 3.5.3 Mixed Workloads 3.6 Additional Case Study: RocksDB 3.6.1 Evaluation 4 B+Trees 4.1 B+Tree and NVM 4.1.1 Category #1: Buffer Extension 4.1.2 Category #2: DRAM Buffered Access 4.1.3 Category #3: Persistent Trees 4.2 Persistent Buffer Pool with Optimistic Consistency 4.2.1 Architecture and Assumptions 4.2.2 Embracing Corruption 4.3 Detecting Corruption 4.3.1 Embracing Corruption 4.4 Repairing Corruptions 4.5 Performance Evaluation and Expectations 4.5.1 Checksums Overhead 4.5.2 Runtime and Recovery 4.6 Discussion 5 Index+Log Key-Value Stores 5.1 The Case for Tail Latency 5.2 Goals and Overview 5.3 Execution Model 5.3.1 Reactive Systems and Actor Model 5.3.2 Message-Passing Communication 5.3.3 Cooperative Multitasking 5.4 Log-Structured Storage 5.5 Networking 5.6 Implementation Details 5.6.1 NVM Allocation on RStore 5.6.2 Log-Structured Storage and Indexing 5.6.3 Garbage Collection 5.6.4 Logging and Recovery 5.7 Systems Operations 5.8 Evaluation 5.8.1 Methodology 5.8.2 Environment 5.8.3 Other Systems 5.8.4 Throughput Scalability 5.8.5 Tail Latency 5.8.6 Scans 5.8.7 Memory Consumption 5.9 Related Work 6 Conclusion Bibliography A PiBenc

Evaluation of Storage Systems for Big Data Analytics

abstract: Recent trends in big data storage systems show a shift from disk centric models to memory centric models. The primary challenges faced by these systems are speed, scalability, and fault tolerance. It is interesting to investigate the performance of these two models with respect to some big data applications. This thesis studies the performance of Ceph (a disk centric model) and Alluxio (a memory centric model) and evaluates whether a hybrid model provides any performance benefits with respect to big data applications. To this end, an application TechTalk is created that uses Ceph to store data and Alluxio to perform data analytics. The functionalities of the application include offline lecture storage, live recording of classes, content analysis and reference generation. The knowledge base of videos is constructed by analyzing the offline data using machine learning techniques. This training dataset provides knowledge to construct the index of an online stream. The indexed metadata enables the students to search, view and access the relevant content. The performance of the application is benchmarked in different use cases to demonstrate the benefits of the hybrid model.Dissertation/ThesisMasters Thesis Computer Science 201

Computing server power modeling in a data center: survey,taxonomy and performance evaluation

Data centers are large scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT) and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power measurement techniques and error calculation formula on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power measurement technique and error formula, with the aim of achieving an objective comparison. We use different servers architectures to assess the impact of heterogeneity on the models' comparison. The performance analysis of these models is elaborated in the paper