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Blockchain Based Patient Centric Medical Records Sharing With Privacy Preservation

Blockchain Based Patient Centric Medical Records Sharing Wit...

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International conference on Computing and Networking Technology (ICCNT)

作者： M. Sharmitha M P. theeraj P V. Ranjith S. Anitha Department of IT Kongu Engineering College Perundurai Erode Tamilnadu

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Individual health records (IHRs) hold paramount significance for individuals, serving as vital tools for the management and oversight of their medical data. through electronic platforms, IHRs empower patients by granting them authority over their health information, thereby revolutionizing the conventional dynamics of the healthcare provider-patient relationship. Nonetheless, existing systems for managing IHRs face obstacles in providing patients with dependable, trackable, and secure control over their medical records, potentially compromising their authenticity and precision. Furthermore, the prevalent centralized methods of IHR management hinder the seamless exchange of medical data and introduce the risk of a singular point of *** address these challenges, a proposal is presented to utilize Ethereum blockchain-based smart contracts, providing patients with decentralized, immutable, and transparent management of their medical data. this solution also incorporate the decentralized storage feature of interplanetary file systems (IPFS). these integrated components collaborate effectively to securely access, store, and exchange patients’ medical information, with the overarching goal of enhancing the integrity and security of patient-controlled health records.

关键词： Privacy Costs Accuracy Smart contracts Medical services Security InterPlanetary file System Proposals Faces Resilience

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Quiver: An informed storage cache for deep learning 18

Quiver: An informed storage cache for deep learning

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18th usenix conference on file and storage technologies, FAST 2020

作者： Kumar, Abhishek Vijaya Sivathanu, Muthian Microsoft Research India

ISBN: (纸本)9781939133120

We introduce Quiver, an informed storage cache for deep learning training (DLT) jobs in a cluster of GPUs. Quiver employs domain-specific intelligence within the caching layer, to achieve much higher efficiency compared to a generic storage cache. First, Quiver uses a secure hash-based addressing to transparently reuse cached data across multiple jobs and even multiple users operating on the same dataset. Second, by codesigning with the deep learning framework (e.g., PyTorch), Quiver employs a technique of substitutable cache hits to get more value from the existing contents of the cache, thus avoiding cache thrashing when cache capacity is much smaller than the working set. third, Quiver dynamically prioritizes cache allocation to jobs that benefit the most from the caching. With a prototype implementation in PyTorch, we show that Quiver can significantly improve throughput of deep learning workloads. Copyright © Proc. of the 18th usenix conference on file and storage Tech., FAST 2020. All rights reserved.

关键词： Program processors

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GIFT: A coupon based throttle-and-reward mechanism for fair and efficient I/O Bandwidth management on parallel storage systems 18

GIFT: A coupon based throttle-and-reward mechanism for fair ...

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18th usenix conference on file and storage technologies, FAST 2020

作者： Patel, Tirthak Garg, Rohan Tiwari, Devesh Northeastern University Nutanix

ISBN: (纸本)9781939133120

Large-scale parallel applications are highly data-intensive and perform terabytes of I/O routinely. Unfortunately, on a large-scale system where multiple applications run concurrently, I/O contention negatively affects system efficiency and causes unfair bandwidth allocation among applications. To address these challenges, this paper introduces GIFT, a principled dynamic approach to achieve fairness among competing applications and improve system efficiency. Copyright © Proc. of the 18th usenix conference on file and storage Tech., FAST 2020. All rights reserved.

关键词： Large scale systems

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PERSEUS: a fail-slow detection framework for cloud storage systems 23

PERSEUS: a fail-slow detection framework for cloud storage s...

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proceedings of the 21st usenix conference on file and storage technologies

作者： Ruiming Lu Erci Xu Yiming Zhang Fengyi Zhu Zhaosheng Zhu Mengtian Wang Zongpeng Zhu Guangtao Xue Jiwu Shu Minglu Li Jiesheng Wu Shanghai Jiao Tong University Alibaba Inc. Xiamen University Shanghai Jiao Tong University and Zhejiang Normal University

ISBN: (纸本)9781939133328

the newly-emerging "fail-slow" failures plague both software and hardware where the victim components are still functioning yet with degraded performance. To address this problem, this paper presents PERSEUS, a practical fail-slow detection framework for storage devices. PERSEUS leverages a light regression-based model to fast pinpoint and analyze fail-slow failures at the granularity of drives. Within a 10- month close monitoring on 248K drives, PERSEUS managed to find 304 fail-slow cases. Isolating them can reduce the (node-level) 99.99th tail latency by 48%. We assemble a large-scale fail-slow dataset (including 41K normal drives and 315 verified fail-slow drives) from our production traces, based on which we provide root cause analysis on fail-slow drives covering a variety of ill-implemented scheduling, hardware defects, and environmental factors. We have released the dataset to the public for fail-slow study

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GoSeed: Generating an optimal seeding plan for deduplicated storage 18

GoSeed: Generating an optimal seeding plan for deduplicated ...

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18th usenix conference on file and storage technologies, FAST 2020

作者： Nachman, Aviv Yadgar, Gala Sheinvald, Sarai Computer Science Department Technion Braude College of Engineering

ISBN: (纸本)9781939133120

Deduplication decreases the physical occupancy of files in a storage volume by removing duplicate copies of data chunks, but creates data-sharing dependencies that complicate standard storage management tasks. Specifically, data migration plans must consider the dependencies between files that are remapped to new volumes and files that are not. thus far, only greedy approaches have been suggested for constructing such plans, and it is unclear how they compare to one another and how much they can be improved. We set to bridge this gap for seeding-migration in which the target volume is initially empty. We present GoSeed, a formulation of seeding as an integer linear programming (ILP) problem, and three acceleration methods for applying it to realsized storage volumes. Our experimental evaluation shows that, while the greedy approaches perform well on "easy" problem instances, the cost of their solution can be significantly higher than that of GoSeed's solution on "hard" instances, for which they are sometimes unable to find a solution at all. Copyright © Proc. of the 18th usenix conference on file and storage Tech., FAST 2020. All rights reserved.

关键词： storage management

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SMRSTORE: a storage engine for cloud object storage on HM-SMR drives 23

SMRSTORE: a storage engine for cloud object storage on HM-SM...

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proceedings of the 21st usenix conference on file and storage technologies

作者： Su Zhou Erci Xu Hao Wu Yu Du Jiacheng Cui Wanyu Fu Chang Liu Yingni Wang Wenbo Wang Shouqu Sun Xianfei Wang Bo Feng Biyun Zhu Xin Tong Weikang Kong Linyan Liu Zhongjie Wu Jinbo Wu Qingchao Luo Jiesheng Wu Alibaba Group

ISBN: (纸本)9781939133328

Cloud object storage vendors are always in pursuit of better cost efficiency. Emerging Shingled Magnetic Recording (SMR) drives are becoming economically favorable in archival storage systems due to significantly improved areal density. However, for standard-class object storage, previous studies and our preliminary exploration revealed that the existing SMR drive solutions can experience severe throughput variations due to garbage collection (GC).In this paper, we introduce SMRSTORE, an SMR-based storage engine for standard-class object storage without compromising performance or durability. the key features of SMRSTORE include directly implementing chunk store interfaces over SMR drives, using a complete log-structured design, and applying guided data placement to reduce GC for consistent performance. the evaluation shows that SMRSTORE delivers comparable performance as Ext4 on the Conventional Magnetic Recording (CMR) drives, and can be up to 2.16x faster than F2FS on SMR drives. By switching to SMR drives, we have decreased the total cost by up to 15% and provided performance on par with the prior system for customers. Currently, we have deployed SMRSTORE in standard-class Alibaba Cloud Object storage Service (OSS) to store hundreds of PBs of data. We plan to use SMR drives for all classes of OSS in the near future.

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FPGA-accelerated compactions for LSM-based key-value store 18

FPGA-accelerated compactions for LSM-based key-value store

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18th usenix conference on file and storage technologies, FAST 2020

作者： Zhang, Teng Wang, Jianying Cheng, Xuntao Xu, Hao Yu, Nanlong Huang, Gui Zhang, Tieying He, Dengcheng Li, Feifei Cao, Wei Huang, Zhongdong Sun, Jianling Alibaba Group Alibaba-Zhejiang University Joint Institute of Frontier Technologies Zhejiang University

ISBN: (纸本)9781939133120

Log-Structured Merge Tree (LSM-tree) key-value (KV) stores have been widely deployed in the industry due to its high write efficiency and low costs as a tiered storage. To maintain such advantages, LSM-tree relies on a background compaction operation to merge data records or collect garbages for housekeeping purposes. In this work, we identify that slow compactions jeopardize the system performance due to unchecked oversized levels in the LSM-tree, and resource contentions for the CPU and the I/O. We further find that the rising I/O capabilities of the latest disk storage have pushed compactions to be bounded by CPUs when merging short KVs. this causes both query/transaction processing and background compactions to compete for the bottlenecked CPU resources extensively in an LSM-tree KV store. In this paper, we propose to offload compactions to FPGAs aiming at accelerating compactions and reducing the CPU bottleneck for storing short KVs. Evaluations have shown that the proposed FPGA-offloading approach accelerates compactions by 2 to 5 times, improves the system throughput by up to 23%, and increases the energy efficiency (number of transactions per watt) by up to 31.7%, compared with the fine-tuned CPUonly baseline. Without loss of generality, we implement our proposal in X-Engine, a latest LSM-tree storage engine. Copyright © Proc. of the 18th usenix conference on file and storage Tech., FAST 2020. All rights reserved.

关键词： Compaction

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POLARDB meets computational storage: Efficiently support analytical workloads in cloud-native relational database 18

POLARDB meets computational storage: Efficiently support ana...

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18th usenix conference on file and storage technologies, FAST 2020

作者： Cao, Wei Liu, Yang Cheng, Zhushi Zheng, Ning Li, Wei Wu, Wenjie Ouyang, Linqiang Wang, Peng Wang, Yijing Kuan, Ray Liu, Zhenjun Zhu, Feng Zhang, Tong Alibaba Group Hang Zhou Zhejiang China ScaleFlux Inc. San JoseCA United States

ISBN: (纸本)9781939133120

this paper reports the deployment of computational storage drives in Alibaba Cloud to enable cloud-native relational database cost-effectively support analytical workloads. With its compute-storage decoupled architecture, cloud-native relational database should pushdown data-intensive tasks (e.g., table scan) from front-end database nodes to back-end storage nodes in order to adequately support analytical workloads. this however makes it a challenge to maintain the cost effectiveness of storage nodes. the emerging computational storage opens a new opportunity to address this challenge: By replacing commodity SSDs with computational storage drives, storage nodes can leverage the in-storage computing power to much more efficiently perform table scans. Practical implementation of this simple idea is non-trivial and demands cohesive innovations across the software (i.e., database, filesystem and I/O) and hardware (i.e., computational storage drive) layers. this paper presents such a holistic implementation for Alibaba cloud-native relational database POLARDB. To the best of our knowledge, this is the first real-world deployment of cloud-native databases with computational storage drives ever reported in the open literature. Copyright © Proc. of the 18th usenix conference on file and storage Tech., FAST 2020. All rights reserved.

关键词： Cost effectiveness

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Carver: Finding important parameters for storage system tuning 18

Carver: Finding important parameters for storage system tuni...

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18th usenix conference on file and storage technologies, FAST 2020

作者： Cao, Zhen Kuenning, Geoff Zadok, Erez Stony Brook University Harvey Mudd College

ISBN: (纸本)9781939133120

storage systems usually have many parameters that affect their behavior. Tuning those parameters can provide significant gains in performance. Alas, both manual and automatic tuning methods struggle due to the large number of parameters and exponential number of possible configurations. Since previous research has shown that some parameters have greater performance impact than others, focusing on a smaller number of more important parameters can speed up auto-tuning systems because they would have a smaller state space to explore. In this paper, we propose Carver, which uses (1) a variance-based metric to quantify storage parameters' importance, (2) Latin Hypercube Sampling to sample huge parameter spaces;and (3) a greedy but efficient parameter-selection algorithm that can identify important parameters. We evaluated Carver on datasets consisting of more than 500,000 experiments on 7 file systems, under 4 representative workloads. Carver successfully identified important parameters for all file systems and showed that importance varies with different workloads. We demonstrated that Carver was able to identify a near-optimal set of important parameters in our datasets. We showed Carver's efficiency by testing it with a small fraction of our dataset;it was able to identify the same set of important parameters with as little as 0.4% of the whole dataset. Copyright © Proc. of the 18th usenix conference on file and storage Tech., FAST 2020. All rights reserved.

关键词： Statistical tests

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Hybrid data reliability for emerging key-value storage devices 18

Hybrid data reliability for emerging key-value storage devic...

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18th usenix conference on file and storage technologies, FAST 2020

作者： Pitchumani, Rekha Kee, Yang-Suk Memory Solutions Lab Samsung Semiconductor Inc.

ISBN: (纸本)9781939133120

Rapid growth in data storage technologies created the modern data-driven world. Modern workloads and application have influenced the evolution of storage devices from simple block devices to more intelligent object devices. Emerging, nextgeneration Key-Value (KV) storage devices allow storage and retrieval of variable-length user data directly onto the devices and can be addressed by user-desired variable-length keys. Traditional reliability schemes for multiple block storage devices, such as Redundant Array of Independent Disks (RAID), have been around for a long time and used by most systems with multiple devices. Now, the question arises as to what an equivalent for such emerging object devices would look like, and how it would compare against the traditional mechanism. In this paper, we present Key-Value Multi-Device (KVMD), a hybrid data reliability manager that employs a variety of reliability techniques with different trade-offs, for key-value devices. We present three reliability techniques suitable for variable length values, and evaluate the hybrid data reliability mechanism employing these techniques using KV SSDs from Samsung. Our evaluation shows that, compared to Linux mdadm-based RAID throughput degradation for block devices, data reliability for KV devices can be achieved at a comparable or lower throughput degradation. In addition, the KV API enables much quicker rebuild and recovery of failed devices, and also allows for both hybrid reliability configuration set automatically based on, say, value sizes, and custom per-object reliability configuration for user data. Copyright © Proc. of the 18th usenix conference on file and storage Tech., FAST 2020. All rights reserved.

关键词： Reliability

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