Hard Drive Failure Prediction For Large Scale Storage System
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Hard Drive Failure Prediction For Large Scale Storage System
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Author : Xiaohong Huang
language : en
Publisher:
Release Date : 2017
Hard Drive Failure Prediction For Large Scale Storage System written by Xiaohong Huang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Data centers use large numbers of hard drives as data storage devices and it is an increasing challenge to maintain the reliability of the storage system as the number of the hard drives increases exponentially. Manual monitoring does not seem to be efficient for large scale storage systems. Typically, the distributions of healthy hard drives and failed hard drives are highly imbalance. In addition, the size of the training data is large for large scale storage systems. The existence of such challenges makes the hard drive failure prediction problem interesting. In this thesis, several classification models are applied to the hard drive S.M.A.R.T. data from 34,970 hard drives for failure prediction, and the results are compared. Based on the analysis, XGBoost provides the best overall prediction result and it is able to process the data efficiently.
Large Scale Disk Failure Prediction
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Author : Cheng He
language : en
Publisher: Springer Nature
Release Date : 2020-08-05
Large Scale Disk Failure Prediction written by Cheng He and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-05 with Computers categories.
This book constitutes the thoroughly refereed post-competition proceedings of the AI Ops Competition on Large-Scale Disk Failure Prediction, conducted between February 7th and May 15, 2020 on the Alibaba Cloud Tianchi Platform. A dedicated workshop, featuring the best performing teams of the competition, was held at the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, in Singapore, in April 2019. Due to the COVID-19 pandemic, the workshop was hosted online. This book includes 13 selected contributions: an introduction to dataset, selected approaches of the competing teams and the competition summary, describing the competition task, practical challenges, evaluation metrics, etc.
Understanding And Coping With Failures In Large Scale Storage Systems
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Author : Qin Xin
language : en
Publisher:
Release Date : 2005
Understanding And Coping With Failures In Large Scale Storage Systems written by Qin Xin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
An Analysis Of Error Behavior In A Large Storage System
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Author : Nisha Talagala
language : en
Publisher:
Release Date : 1999
An Analysis Of Error Behavior In A Large Storage System written by Nisha Talagala and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Computer storage devices categories.
Achieving High Reliability And Efficiency In Maintaining Large Scale Storage Systems Through Optimal Resource Provisioning And Data Placement
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Author : Lipeng Wan
language : en
Publisher:
Release Date : 2016
Achieving High Reliability And Efficiency In Maintaining Large Scale Storage Systems Through Optimal Resource Provisioning And Data Placement written by Lipeng Wan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Information storage and retrieval systems categories.
With the explosive increase in the amount of data being generated by various applications, large-scale distributed and parallel storage systems have become common data storage solutions and been widely deployed and utilized in both industry and academia. While these high performance storage systems significantly accelerate the data storage and retrieval, they also bring some critical issues in system maintenance and management. In this dissertation, I propose three methodologies to address three of these critical issues. First, I develop an optimal resource management and spare provisioning model to minimize the impact brought by component failures and ensure a highly operational experience in maintaining large-scale storage systems. Second, in order to cost-effectively integrate solid-state drives (SSD) into large-scale storage systems, I design a holistic algorithm which can adaptively predict the popularity of data objects by leveraging the temporal locality in their access patterns and adjust their placement among solid-state drives and regular hard disk drives so that the data access throughput as well as the storage space efficiency of the large-scale heterogeneous storage systems can be improved. Finally, I propose a new checkpoint placement optimization model which can maximize the computation efficiency of large-scale scientific applications while guarantee the endurance requirements of the SSD-based burst buffer in high performance hierarchical storage systems. All these models and algorithms are validated through extensive evaluation using data collected from deployed large-scale storage systems and the evaluation results demonstrate our models and algorithms can significantly improve the reliability and efficiency of large-scale distributed and parallel storage systems.
Characterizing Large Storage Systems
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Author : Nisha Talagala
language : en
Publisher:
Release Date : 1999
Characterizing Large Storage Systems written by Nisha Talagala and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Computer storage devices categories.
Predicting Hard Drive Failures In Computer Clusters
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Author : Robin Wesley Featherstun
language : en
Publisher:
Release Date : 2010
Predicting Hard Drive Failures In Computer Clusters written by Robin Wesley Featherstun and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Mitigating the impact of computer failure is possible if accurate failure predictions are provided. Resources, and services can be scheduled around predicted failure and limit the impact. Such strategies are especially important for multi-computer systems, such as compute clusters, that experience a higher rate of failure due to the large number of components. However providing accurate predictions with sufficient lead time remains a challenging problem. This research uses a new spectrum-kernel Support Vector Machine (SVM) ap- proach to predict failure events based on system log files. These files contain messages that represent a change of system state. While a single message in the file may not be sufficient for predicting failure, a sequence or pattern of messages may be. This approach uses a sliding window (sub-sequence) of messages to predict the likelihood of failure. Then, a frequency representation of the message sub-sequences observed are used as input to the SVM. The SVM associates the messages to a class of failed or non-failed system. Experimental results using actual system log files from a Linux-based compute cluster indicate the proposed spectrum-kernel SVM approach can predict hard disk failure with an accuracy of 80% about one day in advance.
Design Challenges On Enterprise Scale Storage Systems Employing Hard Drives And Nand Flash Based Solid State Drives
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Author : Youngjae Kim
language : en
Publisher:
Release Date : 2009
Design Challenges On Enterprise Scale Storage Systems Employing Hard Drives And Nand Flash Based Solid State Drives written by Youngjae Kim and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
Flash memory overcomes some key shortcomings of hard disk drives (HDDs), including faster access to non-sequential data and lower power consumption. Economic forces, driven by the desire to introduce flash into the enterprise market without changing existing software based, have resulted in the emergence of solid state drives (SSDs), flash packaged in HDD from factors and capable of working with device drivers and I/O buses designed for HDDs. Unlike the use of DRAM for caching or buffering, however, certain idiosyncrasies of SSDs make their integration into HDD-based systems non-trivial. Flash memory suffers from limits on its reliability, in an order of magnitude more expensive than the disk, and can be sometimes even slower than the HDD (due to excessive GC induced by high intensity of random writes). Given the complementary properties of HDDs and SDDs in terms of cost, performance, and lifetime, the current consensus among several storage experts is to view SSDs not as a replacement for HDD but rather as a complementary device within the storage hierarchy. In my dissertation, I designed and evaluated such a hybrid system called HybridStore to provide (a) improved capacity planning techniques to administrators with the overall goal of operating within cost-budgets and (b) improved performance/lifetime guarantees during episodes of deviations from expected workloads through several novel mechanisms such as fragmentation busting and write regulation. As an illustrative example of HybridStore's efficacy, a combination of 1 SSD and 6 low-speed, cheaper and higher capacity HDDs is recommended the most cost-effective storage configuration in HybridStore for a predominantly random-write dominant I/O trace from an OLTP application running at a large financial institution. Also, HybridStore employing HDD with small SSD is able to reduce the average response time for Financial trace by about 71% as compared to a HDD-based system. In addition to HybridStore project, I developed a novel design technique of the Flash Translation Layer (FTL) in the SSD. It provides improved performance, reduced garbage collection overhead, and better overloaded behavior compared to state-of- the art FTL schemes. For example, the Financial trace shows a 78% improvement in average response time (due to a 3-fold reduction in operations of the garbage collector), compared to a state-of-the-art FTL scheme. Finally, I also developed and validated flash simulation framework call FlashSim. While a number of well-regarded simulation environments exist for HDDs, the same is not yet true for SSDs. This is due to SSDs having been in the storage market for relatively less time as well as the lack of information (hardware configuration and software methods) about state-of-the-art SSDs that is publicly available. FlashSim aimed at filling this void in performance evaluation of emerging storage systems that employ SSDs.
Flash Storage Management Algorithm For Large Scale Hybrid Storage Systems
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Author : Abdullah Hasan A. Aldhamin
language : en
Publisher:
Release Date : 2014
Flash Storage Management Algorithm For Large Scale Hybrid Storage Systems written by Abdullah Hasan A. Aldhamin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
As the computing platforms have evolved over the years, the associated storage requirements have also followed a rapid change in terms of performance, cost, availability and scalability. In addition, computing elements, mainly the CPU, are continuing to scale and develop at a higher pace compared to storage systems. Flash-based solid-state drives (SSDs) have led to significant innovations in storage systems architecture. However, due to their special design and architecture characteristics, they are not considered as cost-effective and immediate replacement of traditional hard-disk drives for large-scale storage systems. Thus, how we can best utilize this technology to build an efficient hybrid storage system remains a research challenge. We propose a real-time dynamic programming algorithm, called Flash Storage Management (FSM) algorithm, to address this challenge. The FSM algorithm can run in two modes: online and offline modes. We implement the proposed FSM algorithm in an event-driven simulator. To compare the FSM algorithm, we implement a simulator for the closest algorithms in the literature, which is Hystor. Our evaluation results indicate that the proposed algorithm outperforms Hystor, especially for read-intensive workloads. For example, the online FSM algorithm achieves a hit ratio of 75% when using SSD size of 30% of the workload, which outperforms Hystor by more than 20%.
Advanced Informatics For Computing Research
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Author : Ashish Kumar Luhach
language : en
Publisher: Springer
Release Date : 2018-12-12
Advanced Informatics For Computing Research written by Ashish Kumar Luhach and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-12 with Computers categories.
This two-volume set (CCIS 955 and CCIS 956) constitutes the refereed proceedings of the Second International Conference on Advanced Informatics for Computing Research, ICAICR 2018, held in Shimla, India, in July 2018. The 122 revised full papers presented were carefully reviewed and selected from 427 submissions. The papers are organized in topical sections on computing methodologies; hardware; information systems; networks; security and privacy; computing methodologies.