Towards Data Optimization in Storages and Networks

Title from PDF of title page, viewed on August 7, 2015Dissertation advisors: Sejun Song and Baek-Young ChoiVitaIncludes bibliographic references (pages 132-140)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2015We are encountering an explosion of data volume, as a study estimates that data will amount to 40 zeta bytes by the end of 2020. This data explosion poses significant burden not only on data storage space but also access latency, manageability, and processing and network bandwidth. However, large portions of the huge data volume contain massive redundancies that are created by users, applications, systems, and communication models. Deduplication is a technique to reduce data volume by removing redundancies. Reliability will be even improved when data is replicated after deduplication. Many deduplication studies such as storage data deduplication and network redundancy elimination have been proposed to reduce storage consumption and network bandwidth consumption. However, existing solutions are not efficient enough to optimize data delivery path from clients to servers through network. Hence we propose a holistic deduplication framework to optimize data in their path. Our deduplication framework consists of three components including data sources or clients, networks, and servers. The client component removes local redundancies in clients, the network component removes redundant transfers coming from different clients, and the server component removes redundancies coming from different networks. We designed and developed components for the proposed deduplication framework. For the server component, we developed the Hybrid Email Deduplication System that achieves a trade-off of space savings and overhead for email systems. For the client component, we developed the Structure Aware File and Email Deduplication for Cloudbased Storage Systems that is very fast as well as having good space savings by using structure-based granularity. For the network component, we developed a system called Software-defined Deduplication as a Network and Storage service that is in-network deduplication, and that chains storage data deduplication and network redundancy elimination functions by using Software Defined Network to achieve both storage space and network bandwidth savings with low processing time and memory size. We also discuss mobile deduplication for image and video files in mobile devices. Through system implementations and experiments, we show that the proposed framework effectively and efficiently optimizes data volume in a holistic manner encompassing the entire data path of clients, networks and storage servers.Introduction -- Deduplication technology -- Existing deduplication approaches -- HEDS: Hybrid Email Deduplication System -- SAFE: Structure-aware File and Email Deduplication for cloud-based storage systems -- SoftDance: Software-defined Deduplication as a Network and Storage Service -- Moblie de-duplication -- Conclusion

Name Disambiguation from link data in a collaboration graph using temporal and topological features

In a social community, multiple persons may share the same name, phone number or some other identifying attributes. This, along with other phenomena, such as name abbreviation, name misspelling, and human error leads to erroneous aggregation of records of multiple persons under a single reference. Such mistakes affect the performance of document retrieval, web search, database integration, and more importantly, improper attribution of credit (or blame). The task of entity disambiguation partitions the records belonging to multiple persons with the objective that each decomposed partition is composed of records of a unique person. Existing solutions to this task use either biographical attributes, or auxiliary features that are collected from external sources, such as Wikipedia. However, for many scenarios, such auxiliary features are not available, or they are costly to obtain. Besides, the attempt of collecting biographical or external data sustains the risk of privacy violation. In this work, we propose a method for solving entity disambiguation task from link information obtained from a collaboration network. Our method is non-intrusive of privacy as it uses only the time-stamped graph topology of an anonymized network. Experimental results on two real-life academic collaboration networks show that the proposed method has satisfactory performance.Comment: The short version of this paper has been accepted to ASONAM 201

Automatic Threshold Selections by exploration and exploitation of optimization algorithm in Record Deduplication

A deduplication process uses similarity function to identify the two entries are duplicate or not by setting the threshold.  This threshold setting is an important issue to achieve more accuracy and it relies more on human intervention. Swarm Intelligence algorithm such as PSO and ABC have been used for automatic detection of threshold to find the duplicate records. Though the algorithms performed well there is still an insufficiency regarding the solution search equation, which is used to generate new candidate solutions based on the information of previous solutions.  The proposed work addressed two problems: first to find the optimal equation using Genetic Algorithm(GA) and next it adopts an modified  Artificial Bee Colony (ABC) to get the optimal threshold to detect the duplicate records more accurately and also it reduces human intervention. CORA dataset is considered to analyze the proposed algorithm

Re-evaluating Retrosynthesis Algorithms with Syntheseus

The planning of how to synthesize molecules, also known as retrosynthesis, has been a growing focus of the machine learning and chemistry communities in recent years. Despite the appearance of steady progress, we argue that imperfect benchmarks and inconsistent comparisons mask systematic shortcomings of existing techniques. To remedy this, we present a benchmarking library called syntheseus which promotes best practice by default, enabling consistent meaningful evaluation of single-step and multi-step retrosynthesis algorithms. We use syntheseus to re-evaluate a number of previous retrosynthesis algorithms, and find that the ranking of state-of-the-art models changes when evaluated carefully. We end with guidance for future works in this area

Effects of Data Duplication in Pretraining

학위논문(석사) -- 서울대학교대학원 : 데이터사이언스대학원 데이터사이언스학과, 2023. 2. 이재진.This paper studies the effect of deduplication in training data on language models, such as BERT (the encoder-based model) and GPT-2 (the decoder-based model). Previous studies focus on memorizing duplicates in the training dataset whereas we perform several experiments with data deduplication. The pretraining data is first clustered by MinhashLSH, a stochastic method for finding near-duplicate documents in large corpus data, and then deduplicated by Jaccard similarity with various threshold values. Then, the models are finetuned with different downstream tasks. The experimental result indicates that GPT-2 works better with the deduplication, whereas BERT works differently depending on the tasks. It is due to the difference in self-supervised learning methods between BERT and GPT-2. The duplicated data may work on BERT as data augmentation through random masking in its data preprocessing stage. Data duplication may introduce biases and lead to overfitting, but the effect depends on the amount of duplicated data. To improve performance, data deduplication with proper granularity is essential in language model training.이 연구는 BERT(인코더 기반 모델) 및 GPT-2(디코더 기반 모델)와 같은 언어 모델에 대한 훈련 데이터의 중복 제거 효과를 제시하는 데 목적이 있다. 기존 연구에서는 생성 모델에 한하여 중복 제거의 이점을 밝혔으며, 모델이 암기된 텍스트를 덜 생성하고 모델의 훈련 단계가 더 적게 필요하다는 것을 발견하였다. 이에 덧붙여 현 연구에서는 데이터 중복 제거에 대해 몇 가지 추가적인 실험을 수행한다. 사전 학습 데이터는 우선 MinhashLSH(대규모 말뭉치 데이터에서 유사한 문서를 찾기 위한 확률론적 방법)로 클러스터링 한 다음, 다양한 임계값의 Jaccard 유사성으로 중복 document를 제거하는 전처리 과정을 거친다. 구성된 데이터셋을 기반으로 사전 학습을 진행하고, 이후 다양한 downstream 작업에 finetuning한다. GPT-2는 중복 제거된 모델에서 더 높은 성능을 내는 반면, BERT는 downstream 작업에 따라 다른 성능을 보인다. 이는 BERT와 GPT-2의 self-supervised learning 방식의 차이 때문이다. BERT에서는 데이터 전처리 단계에서 랜덤 마스킹 방식을 통해 중복된 데이터가 오히려 데이터 augmentation으로 작용할 수 있다. 그렇지만 결과적으로 데이터 중복은 편향을 도입하고 과적합으로 이어질 수 있으며, 그 효과는 중복 데이터의 양에 따라 다를 수 있다. 따라서 성능을 향상시키기 위해선 언어 모델 훈련에서 적절한 임계값의 데이터 중복 제거가 필수적이다.Chapter 1. Introduction １ 1.1. Study Background １ 1.2. Purpose of Research ３ 1.3. Related Work ４ Chapter 2. Approach ６ 2.1. Pretraining Models ６ 2.2. Pretraining Dataset ７ 2.3. Near Deduplication ７ 2.4. Injection of Exact Document Duplication １０ Chapter 3. Experiments １２ 3.1. Near Deduplication Results １２ 3.2. Duplication Injection Results １４ Chapter 4. Conclusion １６ 4.1. Discussion and Future work １６석

낸드 플래시 저장장치의 성능 및 수명 향상을 위한 프로그램 컨텍스트 기반 최적화 기법

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. 김지홍.컴퓨팅 시스템의 성능 향상을 위해, 기존의 느린 하드디스크(HDD)를 빠른 낸드 플래시 메모리 기반 저장장치(SSD)로 대체하고자 하는 연구가 최근 활발히 진행 되고 있다. 그러나 지속적인 반도체 공정 스케일링 및 멀티 레벨링 기술로 SSD 가격을 동급 HDD 수준으로 낮아졌지만, 최근의 첨단 디바이스 기술의 부작용으 로 NAND 플래시 메모리의 수명이 짧아지는 것은 고성능 컴퓨팅 시스템에서의 SSD의 광범위한 채택을 막는 주요 장벽 중 하나이다. 본 논문에서는 최근의 고밀도 낸드 플래시 메모리의 수명 및 성능 문제를 해결하기 위한 시스템 레벨의 개선 기술을 제안한다. 제안 된 기법은 응용 프로 그램의 쓰기 문맥을 활용하여 기존에는 얻을 수 없었던 데이터 수명 패턴 및 중복 데이터 패턴을 분석하였다. 이에 기반하여, 단일 계층의 단순한 정보만을 활용했 던 기존 기법의 한계를 극복함으로써 효과적으로 NAND 플래시 메모리의 성능 및 수명을 향상시키는 최적화 방법론을 제시한다. 먼저, 응용 프로그램의 I/O 작업에는 문맥에 따라 고유한 데이터 수명과 중 복 데이터의 패턴이 존재한다는 점을 분석을 통해 확인하였다. 문맥 정보를 효과 적으로 활용하기 위해 프로그램 컨텍스트 (쓰기 문맥) 추출 방법을 구현 하였다. 프로그램 컨텍스트 정보를 통해 가비지 컬렉션 부하와 제한된 수명의 NAND 플 래시 메모리 개선을 위한 기존 기술의 한계를 효과적으로 극복할 수 있다. 둘째, 멀티 스트림 SSD에서 WAF를 줄이기 위해 데이터 수명 예측의 정확 성을 높이는 기법을 제안하였다. 이를 위해 애플리케이션의 I/O 컨텍스트를 활용 하는 시스템 수준의 접근 방식을 제안하였다. 제안된 기법의 핵심 동기는 데이터 수명이 LBA보다 높은 추상화 수준에서 평가 되어야 한다는 것이다. 따라서 프 로그램 컨텍스트를 기반으로 데이터의 수명을 보다 정확히 예측함으로써, 기존 기법에서 LBA를 기반으로 데이터 수명을 관리하는 한계를 극복한다. 결론적으 로 따라서 가비지 컬렉션의 효율을 높이기 위해 수명이 짧은 데이터를 수명이 긴 데이터와 효과적으로 분리 할 수 있다. 마지막으로, 쓰기 프로그램 컨텍스트의 중복 데이터 패턴 분석을 기반으로 불필요한 중복 제거 작업을 피할 수있는 선택적 중복 제거를 제안한다. 중복 데 이터를 생성하지 않는 프로그램 컨텍스트가 존재함을 분석적으로 보이고 이들을 제외함으로써, 중복제거 동작의 효율성을 높일 수 있다. 또한 중복 데이터가 발생 하는 패턴에 기반하여 기록된 데이터를 관리하는 자료구조 유지 정책을 새롭게 제안하였다. 추가적으로, 서브 페이지 청크를 도입하여 중복 데이터를 제거 할 가능성을 높이는 세분화 된 중복 제거를 제안한다. 제안 된 기술의 효과를 평가하기 위해 다양한 실제 시스템에서 수집 된 I/O 트레이스에 기반한 시뮬레이션 평가 뿐만 아니라 에뮬레이터 구현을 통해 실제 응용을 동작하면서 일련의 평가를 수행했다. 더 나아가 멀티 스트림 디바이스의 내부 펌웨어를 수정하여 실제와 가장 비슷하게 설정된 환경에서 실험을 수행하 였다. 실험 결과를 통해 제안된 시스템 수준 최적화 기법이 성능 및 수명 개선 측면에서 기존 최적화 기법보다 더 효과적이었음을 확인하였다. 향후 제안된 기 법들이 보다 더 발전된다면, 낸드 플래시 메모리가 초고속 컴퓨팅 시스템의 주 저장장치로 널리 사용되는 데에 긍정적인 기여를 할 수 있을 것으로 기대된다.Replacing HDDs with NAND flash-based storage devices (SSDs) has been one of the major challenges in modern computing systems especially in regards to better performance and higher mobility. Although the continuous semiconductor process scaling and multi-leveling techniques lower the price of SSDs to the comparable level of HDDs, the decreasing lifetime of NAND flash memory, as a side effect of recent advanced device technologies, is emerging as one of the major barriers to the wide adoption of SSDs in highperformance computing systems. In this dissertation, system-level lifetime improvement techniques for recent high-density NAND flash memory are proposed. Unlike existing techniques, the proposed techniques resolve the problems of decreasing performance and lifetime of NAND flash memory by exploiting the I/O context of an application to analyze data lifetime patterns or duplicate data contents patterns. We first present that I/O activities of an application have distinct data lifetime and duplicate data patterns. In order to effectively utilize the context information, we implemented the program context extraction method. With the program context, we can overcome the limitations of existing techniques for improving the garbage collection overhead and limited lifetime of NAND flash memory. Second, we propose a system-level approach to reduce WAF that exploits the I/O context of an application to increase the data lifetime prediction for the multi-streamed SSDs. The key motivation behind the proposed technique was that data lifetimes should be estimated at a higher abstraction level than LBAs, so we employ a write program context as a stream management unit. Thus, it can effectively separate data with short lifetimes from data with long lifetimes to improve the efficiency of garbage collection. Lastly, we propose a selective deduplication that can avoid unnecessary deduplication work based on the duplicate data pattern analysis of write program context. With the help of selective deduplication, we also propose fine-grained deduplication which improves the likelihood of eliminating redundant data by introducing sub-page chunk. It also resolves technical difficulties caused by its finer granularity, i.e., increased memory requirement and read response time. In order to evaluate the effectiveness of the proposed techniques, we performed a series of evaluations using both a trace-driven simulator and emulator with I/O traces which were collected from various real-world systems. To understand the feasibility of the proposed techniques, we also implemented them in Linux kernel on top of our in-house flash storage prototype and then evaluated their effects on the lifetime while running real-world applications. Our experimental results show that system-level optimization techniques are more effective over existing optimization techniques.I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Garbage Collection Problem . . . . . . . . . . . . . 2 1.1.2 Limited Endurance Problem . . . . . . . . . . . . . 4 1.2 Dissertation Goals . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4 Dissertation Structure . . . . . . . . . . . . . . . . . . . . . 7 II. Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1 NAND Flash Memory System Software . . . . . . . . . . . 9 2.2 NAND Flash-Based Storage Devices . . . . . . . . . . . . . 10 2.3 Multi-stream Interface . . . . . . . . . . . . . . . . . . . . 11 2.4 Inline Data Deduplication Technique . . . . . . . . . . . . . 12 2.5 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.5.1 Data Separation Techniques for Multi-streamed SSDs 13 2.5.2 Write Traffic Reduction Techniques . . . . . . . . . 15 2.5.3 Program Context based Optimization Techniques for Operating Systems . . . . . . . . 18 III. Program Context-based Analysis . . . . . . . . . . . . . . . . 21 3.1 Definition and Extraction of Program Context . . . . . . . . 21 3.2 Data Lifetime Patterns of I/O Activities . . . . . . . . . . . 24 3.3 Duplicate Data Patterns of I/O Activities . . . . . . . . . . . 26 IV. Fully Automatic Stream Management For Multi-Streamed SSDs Using Program Contexts . . 29 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.2.1 No Automatic Stream Management for General I/O Workloads . . . . . . . . . 33 4.2.2 Limited Number of Supported Streams . . . . . . . 36 4.3 Automatic I/O Activity Management . . . . . . . . . . . . . 38 4.3.1 PC as a Unit of Lifetime Classification for General I/O Workloads . . . . . . . . . . . 39 4.4 Support for Large Number of Streams . . . . . . . . . . . . 41 4.4.1 PCs with Large Lifetime Variances . . . . . . . . . 42 4.4.2 Implementation of Internal Streams . . . . . . . . . 44 4.5 Design and Implementation of PCStream . . . . . . . . . . 46 4.5.1 PC Lifetime Management . . . . . . . . . . . . . . 46 4.5.2 Mapping PCs to SSD streams . . . . . . . . . . . . 49 4.5.3 Internal Stream Management . . . . . . . . . . . . . 50 4.5.4 PC Extraction for Indirect Writes . . . . . . . . . . 51 4.6 Experimental Results . . . . . . . . . . . . . . . . . . . . . 53 4.6.1 Experimental Settings . . . . . . . . . . . . . . . . 53 4.6.2 Performance Evaluation . . . . . . . . . . . . . . . 55 4.6.3 WAF Comparison . . . . . . . . . . . . . . . . . . . 56 4.6.4 Per-stream Lifetime Distribution Analysis . . . . . . 57 4.6.5 Impact of Internal Streams . . . . . . . . . . . . . . 58 4.6.6 Impact of the PC Attribute Table . . . . . . . . . . . 60 V. Deduplication Technique using Program Contexts . . . . . . 62 5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.2 Selective Deduplication using Program Contexts . . . . . . . 63 5.2.1 PCDedup: Improving SSD Deduplication Efficiency using Selective Hash Cache Management . . . . . . 63 5.2.2 2-level LRU Eviction Policy . . . . . . . . . . . . . 68 5.3 Exploiting Small Chunk Size . . . . . . . . . . . . . . . . . 70 5.3.1 Fine-Grained Deduplication . . . . . . . . . . . . . 70 5.3.2 Read Overhead Management . . . . . . . . . . . . . 76 5.3.3 Memory Overhead Management . . . . . . . . . . . 80 5.3.4 Experimental Results . . . . . . . . . . . . . . . . . 82 VI. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 6.1 Summary and Conclusions . . . . . . . . . . . . . . . . . . 88 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . 89 6.2.1 Supporting applications that have unusal program contexts . . . . . . . . . . . . . 89 6.2.2 Optimizing read request based on the I/O context . . 90 6.2.3 Exploiting context information to improve fingerprint lookups . . . . .. . . . . . 91 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92Docto