Data centers have become center of big data processing. Most programs running in a data center processes big data. The storage requirements of such programs cannot be fulfilled by a single node in the data center, and hence a distributed file system is used where the the storage resource are pooled together from more than one node and presents a unified view of it to outside world. Optimum performance of these distributed file-systems given a workload is of paramount important as disk being the slowest component in the framework. Owning to this fact, many big data processing frameworks implement their own file-system to get the optimal performance by fine tuning it for their specific workloads. However, fine-tuning a file system for a particular workload results in poor performance for workloads that do not match the profile of desired workload. Hence, these file systems cannot be used for general purpose usage, where the workload characteristics shows high variation. In this paper we model the performance of a general purpose file-system and analyse the impact of tuning the file-system on its performance. Performance of these parallel file-systems are not easy to model because the performance depends on a lot of configuration parameters, like the network, disk, under lying file system, number of servers, number of clients, parallel file-system configuration etc. We present a Multiple Linear regression model that can capture the relationship between the configuration parameters of a file system, hardware configuration, workload configuration (collectively called features) and the performance metrics. We use this to rank the features according to their importance in deciding the performance of the file-system.
Technische Universität Berlin（柏林工业大学）；University of Glasgow（格拉斯哥大学）；HPI, University of Potsdam（波茨坦大学哈索·普拉特纳研究所）
Distributed file systems are widely used nowadays, yet using their default configurations is often not optimal. At the same time, tuning configuration parameters is typically challenging and time-consuming. It demands expertise and tuning operations can also be expensive. This is especially the case for static parameters, where changes take effect only after a restart of the system or workloads. We propose a novel approach, Magpie, which utilizes deep reinforcement learning to tune static parameters by strategically exploring and exploiting configuration parameter spaces. To boost the tuning o…
Assise: Performance and Availability via NVM Colocation in a Distributed File System
极低延迟持久内存模块（PMM）的采用颠覆了长期建立的分散式文件系统访问模型。相反，通过将计算和PMM存储放在同一位置，我们可以为应用程序提供更高的I/O性能、亚秒级的应用程序故障转移和强一致性。为了证明这一点，我们构建了基于持久性、复制一致性协议的Assise分布式文件系统，用于管理一组服务器托管的PMM，作为应用程序和较慢的分散存储（如SSD）之间的快速、崩溃可恢复的缓存。与分散式文件系统不同，Assise通过尽可能在共置的PMM上执行IO来最大限度地提高所有文件IO的局部性，并通过在IO操作粒度（而不是固定的块大小）上保持一致性来最大限度地减少一致性开销。我们将Assise与Ceph/Bluestore、NFS和Octopus在具有英特尔Optane DC PMM和SSD的群集上进行了比较，用于常见的云应用程序和基准测试，例如LevelDB、Postfix和FileBench。我们发现，Assise将写入延迟提高了22倍，吞吐量提高了56倍，故障转移时间提高了103倍，并且比同类产品的扩展能力提高了6倍，同时提供了更强的一致性语义。Assise承诺将以1.5倍的速度打破MinuteSort世界纪录。
DIAMOnDS - DIstributed Agents for MObile & Dynamic Services
分布式服务架构支持服务之间的移动代理，提供显着改善的通信和计算的灵活性。代理的使用允许执行复杂的操作，这些操作涉及使用分布式资源有效地处理大量数据。移动和动态服务分布式代理原型系统DIAMOnDS（Distributed Agents for Mobile and Dynamic Services）允许一个服务代表它向其他服务发送代理，以执行数据操作和处理。代理已经被实现为移动服务，使用Jini代理机制发现并由其他服务用于任务管理和通信。代理提供用于与其他服务交互的代理以及用于监视和控制代理活动的特定GUI。因此，代理代表一个服务与其他服务合作执行一项工作，以半自治的方式提供松耦合服务的互操作。远程文件系统访问功能已被代理框架合并，并允许服务动态共享和浏览运行服务的主机的文件系统资源。通用的数据库访问功能已实现在移动代理框架，允许执行复杂的数据采掘和处理操作有效地在分布式系统中。还实现了在文件系统中执行基于查询的搜索的基本数据搜索代理。该框架的测试是在广域网上进行的，移动连接测试代理之间的代理站在欧洲核子研究中心，瑞士和NUST，巴基斯坦。
Redundant storage maintains the performance of distributed systems under various forms of uncertainty. This paper considers the uncertainty in node access and download service. We consider two access models under two download service models. In one access model, a user can access each node with a fixed probability, and in the other, a user can access a random fixed-size subset of nodes. We consider two download service models. In the first (small file) model, the randomness associated with the file size is negligible. In the second (large file) model, randomness is associated with both the fil…
The XRootD system is used to transfer, store, and cache large datasets from high-energy physics (HEP). In this study we focus on its capability as distributed on-demand storage cache. Through exploring a large set of daily log files between 2020 and 2021, we seek to understand the data access patterns that might inform future cache design. Our study begins with a set of summary statistics regarding file read operations, file lifetimes, and file transfers. We observe that the number of read operations on each file remains nearly constant, while the average size of a read operation grows over ti…
This work examines strategies to handle large shared data objects in distributed storage systems (DSS), while boosting the number of concurrent accesses, maintaining strong consistency guarantees, and ensuring good operation performance. To this respect, we define the notion of fragmented objects:con-current objects composed of a list of fragments (or blocks) that allow operations to manipulate each of their fragments individually. As the fragments belong to the same object, it is not enough that each fragment is linearizable to have useful consistency guarantees in the composed object. Hence,…
A peer-to-peer system is a distributed system in which equal nodes (in terms of role and usage) exchange information and services directly. This paper describes a distributed peer-to-peer protocol that allows wifi-enabled smart devices (especially Android smartphones) to exchange data using only wifi. The protocol is designed to allow the automatic establishment of a distributed peer-to-peer network of any size without geography constraint. It is applied to file sharing between devices, but can easily be adapted to support any data sharing. The protocol defines two layers: (1) a kernel layer r…
High-performance object stores are an emerging technology which offers an alternative solution in the field of HPC storage, with potential to address long-standing scalability issues in traditional distributed POSIX file systems due to excessive consistency assurance and metadata prescriptiveness. In this paper we assess the performance of storing object-like data within a standard file system, where the configuration and access mechanisms have not been optimised for object access behaviour, and compare with and investigate the benefits of using an object storage system. Whilst this approach i…
As the capacity of Solid-State Drives (SSDs) is constantly being optimised and boosted with gradually reduced cost, the SSD cluster is now widely deployed as part of the hybrid storage system in various scenarios such as cloud computing and big data processing. However, despite its rapid developments, the performance of the SSD cluster remains largely under-investigated, leaving its sub-optimal applications in reality. To address this issue, in this paper we conduct extensive empirical studies for a comprehensive understanding of the SSD cluster in diverse settings. To this end, we configure a…
