Cloud Uptime Archive: Open-Access Availability Data of Web, Cloud, and Gaming Services

IF 5.9 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2026-03-01 Epub Date: 2026-03-12 DOI:10.1109/TPDS.2026.3673519

Sacheendra Talluri;Dante Niewenhuis;Xiaoyu Chu;Jakob Kyselica;Mehmet Cetin;Alexander Balgavy;Alexandru Iosup

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引用次数: 0

Abstract

Cloud services are critical to society. However, their reliability is poorly understood. Towards solving the problem, we propose a standard repository for cloud uptime data. We populate this repository with the data we collect containing failure reports from users and operators of cloud services, web services, and online games. The multiple vantage points help reduce bias from individual users and operators. We compare our new data to existing failure data from the Failure Trace Archive and the Google cluster trace. We analyze the MTBF and MTTR, time patterns, failure severity, user-reported symptoms, and operator-reported symptoms of failures in the data we collect. We observe that high-level user facing services fail less often than low-level infrastructure services, likely due to them using fault-tolerance techniques. We use simulation-based experiments to demonstrate the impact of different failure traces on the performance of checkpointing and retry mechanisms.

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云正常运行时间存档：Web，云和游戏服务的开放访问可用性数据

云服务对社会至关重要。然而，人们对它们的可靠性知之甚少。为了解决这个问题，我们提出了一个标准的云正常运行时间数据存储库。我们用收集的数据填充这个存储库，这些数据包含来自云服务、web服务和在线游戏的用户和运营商的故障报告。多个有利位置有助于减少来自个人用户和运营商的偏见。我们将新数据与来自故障跟踪归档和谷歌集群跟踪的现有故障数据进行比较。我们在收集的数据中分析MTBF和MTTR、时间模式、故障严重程度、用户报告的故障症状和操作员报告的故障症状。我们观察到，高级用户服务比低级基础设施服务更少发生故障，这可能是由于它们使用了容错技术。我们使用基于仿真的实验来演示不同的故障跟踪对检查点和重试机制性能的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Parallel and Distributed Systems 工程技术-工程：电子与电气

CiteScore

11.00

自引率

9.40%

发文量

281

审稿时长

5.6 months

期刊介绍： IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.