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 article presents Perseus , a practical fail-slow detection framework for storage devices. Perseus leverages a light regression-based model to quickly 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.
{"title":"From Missteps to Milestones: A Journey to Practical Fail-Slow Detection","authors":"Ruiming Lu, Erci Xu, Yiming Zhang, Fengyi Zhu, Zhaosheng Zhu, Mengtian Wang, Zongpeng Zhu, Guangtao Xue, Jiwu Shu, Minglu Li, Jiesheng Wu","doi":"10.1145/3617690","DOIUrl":"https://doi.org/10.1145/3617690","url":null,"abstract":"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 article presents Perseus , a practical fail-slow detection framework for storage devices. Perseus leverages a light regression-based model to quickly 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.","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":"79 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134957500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Phase Change Memory (PCM), one of the recently proposed non-volatile memory technologies, has been suffering from low write endurance. For example, a single layer PCM cell could only be written approximately 10 8 times. This limits the lifetime of a PCM-based memory to a few days rather than years when memory intensive applications are running. Wear leveling techniques have been proposed to improve the write endurance of a PCM. Among those techniques, the region based start-gap (RBSG) scheme is widely cited as achieving the highest lifetime. Based on our experiments, RBSG can achieve 97% of the ideal lifetime but only for relatively small memory sizes (e.g. 8GB-32GB). As the memory size goes up, RBSG becomes less effective and its expected percentage of the ideal lifetime reduces to less than 57% for a 2TB PCM. In this paper, we propose a table-based wear leveling scheme called block grouping to enhance the write endurance of a PCM with a negligible overhead. Our research results show that with a proper configuration and adoption of partial writes (writing back only 64B subblocks instead of a whole row to the PCM arrays) and internal row shift (shifting the subblocks in a row periodically so no subblock in a row will be written repeatedly), the proposed block grouping scheme could achieve 95% of the ideal lifetime on average for the Rodinia, NPB, and SPEC benchmarks with less than 1.74% performance overhead and up to 0.18% hardware overhead. Moreover, our scheme is scalable and achieves the same percentage of ideal lifetime for PCM of size from 8GB to 2TB. We also show that the proposed scheme can better tolerate memory write attacks than WoLFRAM (Wear Leveling and Fault Tolerance for Resistive Memories) and RBSG for a PCM of size 32GB or higher. Finally, we integrate an error-correcting pointer technique into our proposed block grouping scheme to make the PCM fault tolerant against hard errors.
{"title":"A Scalable Wear Leveling Technique for Phase Change Memory","authors":"Wang Xu, Israel Koren","doi":"10.1145/3631146","DOIUrl":"https://doi.org/10.1145/3631146","url":null,"abstract":"Phase Change Memory (PCM), one of the recently proposed non-volatile memory technologies, has been suffering from low write endurance. For example, a single layer PCM cell could only be written approximately 10 8 times. This limits the lifetime of a PCM-based memory to a few days rather than years when memory intensive applications are running. Wear leveling techniques have been proposed to improve the write endurance of a PCM. Among those techniques, the region based start-gap (RBSG) scheme is widely cited as achieving the highest lifetime. Based on our experiments, RBSG can achieve 97% of the ideal lifetime but only for relatively small memory sizes (e.g. 8GB-32GB). As the memory size goes up, RBSG becomes less effective and its expected percentage of the ideal lifetime reduces to less than 57% for a 2TB PCM. In this paper, we propose a table-based wear leveling scheme called block grouping to enhance the write endurance of a PCM with a negligible overhead. Our research results show that with a proper configuration and adoption of partial writes (writing back only 64B subblocks instead of a whole row to the PCM arrays) and internal row shift (shifting the subblocks in a row periodically so no subblock in a row will be written repeatedly), the proposed block grouping scheme could achieve 95% of the ideal lifetime on average for the Rodinia, NPB, and SPEC benchmarks with less than 1.74% performance overhead and up to 0.18% hardware overhead. Moreover, our scheme is scalable and achieves the same percentage of ideal lifetime for PCM of size from 8GB to 2TB. We also show that the proposed scheme can better tolerate memory write attacks than WoLFRAM (Wear Leveling and Fault Tolerance for Resistive Memories) and RBSG for a PCM of size 32GB or higher. Finally, we integrate an error-correcting pointer technique into our proposed block grouping scheme to make the PCM fault tolerant against hard errors.","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136069911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shucheng Wang, Qiang Cao, Hong Jiang, Ziyi Lu, Jie Yao, Yuxing Chen, Anqun Pan
Following a conventional design principle that pays more fast-CPU-cycles for fewer slow-I/Os, popular software storage architecture Linux Multiple-Disk (MD) for parity-based RAID (e.g., RAID5 and RAID6) assigns one or more centralized worker threads to efficiently process all user requests based on multi-stage asynchronous control and global data structures, successfully exploiting characteristics of slow devices, e.g., Hard Disk Drives (HDDs). However, we observe that, with high-performance NVMe-based Solid State Drives (SSDs), even the recently added multi-worker processing mode in MD achieves only limited performance gain because of the severe lock contentions under intensive write workloads. In this paper, we propose a novel stripe-threaded RAID architecture, StRAID, assigning a dedicated worker thread for each stripe-write (one-for-one model) to sufficiently exploit high parallelism inherent among RAID stripes, multi-core processors, and SSDs. For the notoriously performance-punishing partial-stripe writes that induce extra read and write I/Os, StRAID presents a two-stage stripe write mechanism and a two-dimensional multi-log SSD buffer. All writes first are opportunistically batched in memory, and then are written into the primary RAID for aggregated full-stripe writes or conditionally redirected to the buffer for partial-stripe writes. These buffered data are strategically reclaimed to the primary RAID. We evaluate a StRAID prototype with a variety of benchmarks and real-world traces. StRAID is demonstrated to outperform MD by up to 5.8 times in write throughput.
遵循传统的设计原则，即为更少的慢i / o支付更多的快速cpu周期，流行的软件存储架构Linux multi- Disk (MD)用于基于奇偶性的RAID(例如RAID5和RAID6)分配一个或多个集中的工作线程，以有效地处理基于多阶段异步控制和全局数据结构的所有用户请求，成功地利用慢设备(例如硬盘驱动器(hdd))的特性。然而，我们观察到，对于基于nvme的高性能固态驱动器(ssd)，即使是最近在MD中添加的多工作者处理模式也只能获得有限的性能提升，因为在密集的写工作负载下存在严重的锁争用。在本文中，我们提出了一种新的条带线程RAID架构，StRAID，为每个条带写入分配一个专用的工作线程(一对一模型)，以充分利用RAID条带、多核处理器和ssd之间固有的高并行性。众所周知，部分分条写入会导致额外的读/写I/ o，而StRAID提供了一个两阶段的分条写入机制和一个二维多日志SSD缓冲区。所有写操作首先在内存中分批处理，然后写入主RAID以进行聚合的全条带写操作，或者有条件地重定向到缓冲区以进行部分条带写操作。这些缓冲的数据策略性地回收到主RAID。我们使用各种基准测试和真实世界的跟踪来评估StRAID原型。在写入吞吐量方面，StRAID的性能比MD高出5.8倍。
{"title":"Explorations and Exploitation for Parity-based RAIDs with Ultra-fast SSDs","authors":"Shucheng Wang, Qiang Cao, Hong Jiang, Ziyi Lu, Jie Yao, Yuxing Chen, Anqun Pan","doi":"10.1145/3627992","DOIUrl":"https://doi.org/10.1145/3627992","url":null,"abstract":"Following a conventional design principle that pays more fast-CPU-cycles for fewer slow-I/Os, popular software storage architecture Linux Multiple-Disk (MD) for parity-based RAID (e.g., RAID5 and RAID6) assigns one or more centralized worker threads to efficiently process all user requests based on multi-stage asynchronous control and global data structures, successfully exploiting characteristics of slow devices, e.g., Hard Disk Drives (HDDs). However, we observe that, with high-performance NVMe-based Solid State Drives (SSDs), even the recently added multi-worker processing mode in MD achieves only limited performance gain because of the severe lock contentions under intensive write workloads. In this paper, we propose a novel stripe-threaded RAID architecture, StRAID, assigning a dedicated worker thread for each stripe-write (one-for-one model) to sufficiently exploit high parallelism inherent among RAID stripes, multi-core processors, and SSDs. For the notoriously performance-punishing partial-stripe writes that induce extra read and write I/Os, StRAID presents a two-stage stripe write mechanism and a two-dimensional multi-log SSD buffer. All writes first are opportunistically batched in memory, and then are written into the primary RAID for aggregated full-stripe writes or conditionally redirected to the buffer for partial-stripe writes. These buffered data are strategically reclaimed to the primary RAID. We evaluate a StRAID prototype with a variety of benchmarks and real-world traces. StRAID is demonstrated to outperform MD by up to 5.8 times in write throughput.","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136078654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Persistent memory (PM) technologies have inspired a wide range of PM-based system optimizations. However, building correct PM-based systems is difficult due to the unique characteristics of PM hardware. To better understand the challenges as well as the opportunities to address them, this article presents a comprehensive study of PM-related issues in the Linux kernel. By analyzing 1,553 PM-related kernel patches in depth and conducting experiments on reproducibility and tool extension, we derive multiple insights in terms of PM patch categories, PM bug patterns, consequences, fix strategies, triggering conditions, and remedy solutions. We hope our results could contribute to the development of robust PM-based storage systems.
{"title":"Understanding Persistent-memory-related Issues in the Linux Kernel","authors":"Om Rameshwar Gatla, Duo Zhang, Wei Xu, Mai Zheng","doi":"10.1145/3605946","DOIUrl":"https://doi.org/10.1145/3605946","url":null,"abstract":"Persistent memory (PM) technologies have inspired a wide range of PM-based system optimizations. However, building correct PM-based systems is difficult due to the unique characteristics of PM hardware. To better understand the challenges as well as the opportunities to address them, this article presents a comprehensive study of PM-related issues in the Linux kernel. By analyzing 1,553 PM-related kernel patches in depth and conducting experiments on reproducibility and tool extension, we derive multiple insights in terms of PM patch categories, PM bug patterns, consequences, fix strategies, triggering conditions, and remedy solutions. We hope our results could contribute to the development of robust PM-based storage systems.","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135696134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sang-Hoon Kim, Jaehoon Shim, Euidong Lee, Seongyeop Jeong, Ilkueon Kang, Jin-Soo Kim
There have been drastic changes in the storage device landscape recently. At the center of the diverse storage landscape lies the NVMe interface, which allows high-performance and flexible communication models required by these next-generation device types. However, its hardware-oriented definition and specification are bottlenecking the development and evaluation cycle for new revolutionary storage devices. Furthermore, existing emulators lack the capability to support the advanced storage configurations that are currently in the spotlight. In this paper, we present NVMeVirt, a novel approach to facilitate software-defined NVMe devices. A user can define any NVMe device type with custom features, and NVMeVirt allows it to bridge the gap between the host I/O stack and the virtual NVMe device in software. We demonstrate the advantages and features of NVMeVirt by realizing various storage types and configurations, such as conventional SSDs, low-latency high-bandwidth NVM SSDs, zoned namespace SSDs, and key-value SSDs with the support of PCI peer-to-peer DMA and NVMe-oF target offloading. We also make cases for storage research with NVMeVirt, such as studying the performance characteristics of database engines and extending the NVMe specification for the improved key-value SSD performance.
{"title":"Empowering Storage Systems Research with NVMeVirt: A Comprehensive NVMe Device Emulator","authors":"Sang-Hoon Kim, Jaehoon Shim, Euidong Lee, Seongyeop Jeong, Ilkueon Kang, Jin-Soo Kim","doi":"10.1145/3625006","DOIUrl":"https://doi.org/10.1145/3625006","url":null,"abstract":"There have been drastic changes in the storage device landscape recently. At the center of the diverse storage landscape lies the NVMe interface, which allows high-performance and flexible communication models required by these next-generation device types. However, its hardware-oriented definition and specification are bottlenecking the development and evaluation cycle for new revolutionary storage devices. Furthermore, existing emulators lack the capability to support the advanced storage configurations that are currently in the spotlight. In this paper, we present NVMeVirt, a novel approach to facilitate software-defined NVMe devices. A user can define any NVMe device type with custom features, and NVMeVirt allows it to bridge the gap between the host I/O stack and the virtual NVMe device in software. We demonstrate the advantages and features of NVMeVirt by realizing various storage types and configurations, such as conventional SSDs, low-latency high-bandwidth NVM SSDs, zoned namespace SSDs, and key-value SSDs with the support of PCI peer-to-peer DMA and NVMe-oF target offloading. We also make cases for storage research with NVMeVirt, such as studying the performance characteristics of database engines and extending the NVMe specification for the improved key-value SSD performance.","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136152933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Miao Cai, Junru Shen, Bin Tang, Hao Huang, Baoliu Ye
The conventional file system provides a hierarchical namespace by structuring it as a directory tree. Tree-based namespace structure leads to inefficient file path walk and expensive namespace tree traversal, underutilizing ultra-low access latency and superior sequential performance provided by non-volatile memories (NVMs). This paper proposes FlatFS+, an NVM file system that features a flat namespace architecture while providing a compatible hierarchical namespace view. FlatFS+ incorporates three novel techniques: direct file path walk model, range-optimized Br tree, and compressed index key design with scan and write dual optimization, to fully exploit flat namespace to improve file system metadata performance on ultra-fast, byte-addressable NVMs. Evaluation results demonstrate that FlatFS+ achieves significant performance improvements for metadata-intensive benchmarks and real-world applications compared to other file systems.
{"title":"Exploiting Flat Namespace to Improve File System Metadata Performance on Ultra-fast, Byte-addressable NVMs","authors":"Miao Cai, Junru Shen, Bin Tang, Hao Huang, Baoliu Ye","doi":"10.1145/3620673","DOIUrl":"https://doi.org/10.1145/3620673","url":null,"abstract":"The conventional file system provides a hierarchical namespace by structuring it as a directory tree. Tree-based namespace structure leads to inefficient file path walk and expensive namespace tree traversal, underutilizing ultra-low access latency and superior sequential performance provided by non-volatile memories (NVMs). This paper proposes FlatFS+, an NVM file system that features a flat namespace architecture while providing a compatible hierarchical namespace view. FlatFS+ incorporates three novel techniques: direct file path walk model, range-optimized Br tree, and compressed index key design with scan and write dual optimization, to fully exploit flat namespace to improve file system metadata performance on ultra-fast, byte-addressable NVMs. Evaluation results demonstrate that FlatFS+ achieves significant performance improvements for metadata-intensive benchmarks and real-world applications compared to other file systems.","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43687749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Disaggregated memory systems separate monolithic servers into different components, including compute and memory nodes, to enjoy the benefits of high resource utilization, flexible hardware scalability, and efficient data sharing. By exploiting the high-performance RDMA (Remote Direct Memory Access), the compute nodes directly access the remote memory pool without involving remote CPUs. Hence, the ordered key-value (KV) stores (e.g., B-trees and learned indexes) keep all data sorted to provide rang query service via the high-performance network. However, existing ordered KVs fail to work well on the disaggregated memory systems, due to either consuming multiple network roundtrips to search the remote data or heavily relying on the memory nodes equipped with insufficient computing resources to process data modifications. In this paper, we propose a scalable RDMA-oriented KV store with learned indexes, called ROLEX, to coalesce the ordered KV store in the disaggregated systems for efficient data storage and retrieval. ROLEX leverages a retraining-decoupled learned index scheme to dissociate the model retraining from data modification operations via adding a bias and some data-movement constraints to learned models. Based on the operation decoupling, data modifications are directly executed in compute nodes via one-sided RDMA verbs with high scalability. The model retraining is hence removed from the critical path of data modification and asynchronously executed in memory nodes by using dedicated computing resources. ROLEX efficiently alleviates the fragmentation and garbage collection issues, due to allocating and reclaiming space via fixed-size leaves that are accessed via the atomic-size leaf numbers. Our experimental results on YCSB and real-world workloads demonstrate that ROLEX achieves competitive performance on the static workloads, as well as significantly improving the performance on dynamic workloads by up to 2.2 × than state-of-the-art schemes on the disaggregated memory systems. We have released the open-source codes for public use in GitHub.
{"title":"A High-Performance RDMA-oriented Learned Key-Value Store for Disaggregated Memory Systems","authors":"Pengfei Li, Yu Hua, Pengfei Zuo, Zhangyu Chen, Jiajie Sheng","doi":"10.1145/3620674","DOIUrl":"https://doi.org/10.1145/3620674","url":null,"abstract":"Disaggregated memory systems separate monolithic servers into different components, including compute and memory nodes, to enjoy the benefits of high resource utilization, flexible hardware scalability, and efficient data sharing. By exploiting the high-performance RDMA (Remote Direct Memory Access), the compute nodes directly access the remote memory pool without involving remote CPUs. Hence, the ordered key-value (KV) stores (e.g., B-trees and learned indexes) keep all data sorted to provide rang query service via the high-performance network. However, existing ordered KVs fail to work well on the disaggregated memory systems, due to either consuming multiple network roundtrips to search the remote data or heavily relying on the memory nodes equipped with insufficient computing resources to process data modifications. In this paper, we propose a scalable RDMA-oriented KV store with learned indexes, called ROLEX, to coalesce the ordered KV store in the disaggregated systems for efficient data storage and retrieval. ROLEX leverages a retraining-decoupled learned index scheme to dissociate the model retraining from data modification operations via adding a bias and some data-movement constraints to learned models. Based on the operation decoupling, data modifications are directly executed in compute nodes via one-sided RDMA verbs with high scalability. The model retraining is hence removed from the critical path of data modification and asynchronously executed in memory nodes by using dedicated computing resources. ROLEX efficiently alleviates the fragmentation and garbage collection issues, due to allocating and reclaiming space via fixed-size leaves that are accessed via the atomic-size leaf numbers. Our experimental results on YCSB and real-world workloads demonstrate that ROLEX achieves competitive performance on the static workloads, as well as significantly improving the performance on dynamic workloads by up to 2.2 × than state-of-the-art schemes on the disaggregated memory systems. We have released the open-source codes for public use in GitHub.","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43932825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Huiba Li, Zhihao Zhang, Yifan Yuan, Rui Du, Kai Ma, Lanzheng Liu, Yiming Zhang, Windsor Hsu
Businesses increasingly need agile and elastic computing infrastructure to respond quickly to real world situations. By offering efficient process-based virtualization and a layered image system, containers are designed to enable agile and elastic application deployment. However, creating or updating large container clusters is still slow due to the image downloading and unpacking process. In this paper, we present DADI Image Service, a block-level image service for increased agility and elasticity in deploying applications. DADI replaces the waterfall model of starting containers (downloading image, unpacking image, starting container) with fine-grained on-demand transfer of remote images, realizing instant start of containers. To accelerate the cold start of containers, DADI designs a pull-based prefetching mechanism which allows a host to read necessary image data beforehand at the granularity of image layers. We design a P2P-based decentralized image sharing architecture to balance traffic among all the participating hosts and propose a pull-push collaborative prefetching mechanism to accelerate cold start. DADI efficiently supports various kinds of runtimes including cgroups, QEMU, etc., further realizing “build once, run anywhere”. DADI has been deployed at scale in the production environment of Alibaba, serving one of the world’s largest ecommerce platforms. Performance results show that DADI can cold start 10,000 containers on 1,000 hosts within 4 seconds.
{"title":"Block-Level Image Service for the Cloud","authors":"Huiba Li, Zhihao Zhang, Yifan Yuan, Rui Du, Kai Ma, Lanzheng Liu, Yiming Zhang, Windsor Hsu","doi":"10.1145/3620672","DOIUrl":"https://doi.org/10.1145/3620672","url":null,"abstract":"Businesses increasingly need agile and elastic computing infrastructure to respond quickly to real world situations. By offering efficient process-based virtualization and a layered image system, containers are designed to enable agile and elastic application deployment. However, creating or updating large container clusters is still slow due to the image downloading and unpacking process. In this paper, we present DADI Image Service, a block-level image service for increased agility and elasticity in deploying applications. DADI replaces the waterfall model of starting containers (downloading image, unpacking image, starting container) with fine-grained on-demand transfer of remote images, realizing instant start of containers. To accelerate the cold start of containers, DADI designs a pull-based prefetching mechanism which allows a host to read necessary image data beforehand at the granularity of image layers. We design a P2P-based decentralized image sharing architecture to balance traffic among all the participating hosts and propose a pull-push collaborative prefetching mechanism to accelerate cold start. DADI efficiently supports various kinds of runtimes including cgroups, QEMU, etc., further realizing “build once, run anywhere”. DADI has been deployed at scale in the production environment of Alibaba, serving one of the world’s largest ecommerce platforms. Performance results show that DADI can cold start 10,000 containers on 1,000 hosts within 4 seconds.","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45552135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-17DOI: https://dl.acm.org/doi/10.1145/3606020
Jinghan Sun, Shaobo Li, Jun Xu, Jian Huang
This paper presents a systematic study on the security of modern file systems, following a vulnerability-centric perspective. Specifically, we collected 377 file system vulnerabilities committed to the CVE database in the past 20 years. We characterize them from four dimensions that include why the vulnerabilities appear, how the vulnerabilities can be exploited, what consequences can arise, and how the vulnerabilities are fixed. This way, we build a deep understanding of the attack surfaces faced by file systems, the threats imposed by the attack surfaces, and the good and bad practices in mitigating the attacks in file systems. We envision that our study will bring insights towards the future development of file systems, the enhancement of file system security, and the relevant vulnerability mitigating solutions.
{"title":"The Security War in File Systems: An Empirical Study from A Vulnerability-Centric Perspective","authors":"Jinghan Sun, Shaobo Li, Jun Xu, Jian Huang","doi":"https://dl.acm.org/doi/10.1145/3606020","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3606020","url":null,"abstract":"<p>This paper presents a systematic study on the security of modern file systems, following a vulnerability-centric perspective. Specifically, we collected 377 file system vulnerabilities committed to the CVE database in the past 20 years. We characterize them from four dimensions that include why the vulnerabilities appear, how the vulnerabilities can be exploited, what consequences can arise, and how the vulnerabilities are fixed. This way, we build a deep understanding of the attack surfaces faced by file systems, the threats imposed by the attack surfaces, and the good and bad practices in mitigating the attacks in file systems. We envision that our study will bring insights towards the future development of file systems, the enhancement of file system security, and the relevant vulnerability mitigating solutions.</p>","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":"46 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138512819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Suzhen Wu, Zhanhong Tu, Yuxuan Zhou, Zuocheng Wang, Zhirong Shen, Wei Chen, Wen Wang, Weichun Wang, Bo Mao
More and more data are stored in cloud storage which brings two major challenges. First, the modified files in the cloud should be quickly synchronized to ensure data consistency, e.g., delta synchronization (sync) achieves efficient cloud sync by synchronizing only the updated part of the file. Second, the huge data in the cloud needs to be deduplicated and encrypted, e.g., Message-Locked Encryption (MLE) implements data deduplication by encrypting the content among different users. However, when combined, a few updates in the content can cause large sync traffic amplification for both keys and ciphertext in the MLE-based cloud storage, significantly degrading the cloud sync efficiency. A feature-based encryption sync scheme, FeatureSync, is proposed to address the delta amplification problem. However, with further improvement of the network bandwidth, the performance of FeatureSync stagnates. In our preliminary experimental evaluations, we find that the bottleneck of the computational overhead in the high-bandwidth network environments is the main bottleneck in FeatureSync. In this paper, we propose an enhanced feature-based encryption sync scheme FASTSync to optimize the performance of FeatureSync in high-bandwidth network environments. The performance evaluations on a lightweight prototype implementation of FASTSync show that FASTSync reduces the cloud sync time by 70.3% and the encryption time by 37.3% on average, compared with FeatureSync.
{"title":"FASTSync: a FAST Delta Sync Scheme for Encrypted Cloud Storage in High-Bandwidth Network Environments","authors":"Suzhen Wu, Zhanhong Tu, Yuxuan Zhou, Zuocheng Wang, Zhirong Shen, Wei Chen, Wen Wang, Weichun Wang, Bo Mao","doi":"10.1145/3607536","DOIUrl":"https://doi.org/10.1145/3607536","url":null,"abstract":"More and more data are stored in cloud storage which brings two major challenges. First, the modified files in the cloud should be quickly synchronized to ensure data consistency, e.g., delta synchronization (sync) achieves efficient cloud sync by synchronizing only the updated part of the file. Second, the huge data in the cloud needs to be deduplicated and encrypted, e.g., Message-Locked Encryption (MLE) implements data deduplication by encrypting the content among different users. However, when combined, a few updates in the content can cause large sync traffic amplification for both keys and ciphertext in the MLE-based cloud storage, significantly degrading the cloud sync efficiency. A feature-based encryption sync scheme, FeatureSync, is proposed to address the delta amplification problem. However, with further improvement of the network bandwidth, the performance of FeatureSync stagnates. In our preliminary experimental evaluations, we find that the bottleneck of the computational overhead in the high-bandwidth network environments is the main bottleneck in FeatureSync. In this paper, we propose an enhanced feature-based encryption sync scheme FASTSync to optimize the performance of FeatureSync in high-bandwidth network environments. The performance evaluations on a lightweight prototype implementation of FASTSync show that FASTSync reduces the cloud sync time by 70.3% and the encryption time by 37.3% on average, compared with FeatureSync.","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48634042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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