Dissecting Ubuntuone: Autopsy of a Global-Scale Personal Cloud Back-End
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Dissecting UbuntuOne: Autopsy of a Global-scale Personal Cloud Back-end Raúl Gracia-Tinedo Yongchao Tian Josep Sampé Universitat Rovira i Virgili Eurecom Universitat Rovira i Virgili [email protected] [email protected] [email protected] Hamza Harkous John Lenton Pedro García-López EPFL Canonical Ltd. Universitat Rovira i Virgili hamza.harkous@epfl.ch [email protected] [email protected] Marc Sánchez-Artigas Marko Vukolic´ Universitat Rovira i Virgili IBM Research - Zurich [email protected] [email protected] ABSTRACT 1. INTRODUCTION Personal Cloud services, such as Dropbox or Box, have been Today, users require ubiquitous and transparent storage widely adopted by users. Unfortunately, very little is known to help handle, synchronize and manage their personal data. about the internal operation and general characteristics of In a recent report [1], Forrester research forecasts a market Personal Clouds since they are proprietary services. of $12 billion in the US related to personal and user-centric In this paper, we focus on understanding the nature of cloud services by 2016. In response to this demand, Personal Personal Clouds by presenting the internal structure and a Clouds like Dropbox, Box and UbuntuOne (U1) have pro- measurement study of UbuntuOne (U1). We first detail the liferated and become increasingly popular, attracting com- U1 architecture, core components involved in the U1 meta- panies such as Google, Microsoft, Amazon or Apple to offer data service hosted in the datacenter of Canonical, as well their own integrated solutions in this field. as the interactions of U1 with Amazon S3 to outsource data In a nutshell, a Personal Cloud service offers automatic storage. To our knowledge, this is the first research work to backup, file sync, sharing and remote accessibility across a describe the internals of a large-scale Personal Cloud. multitude of devices and operating systems. The popularity Second, by means of tracing the U1 servers, we provide of these services is based on their easy to use Software-as-a- an extensive analysis of its back-end activity for one month. Service (SaaS) storage facade to ubiquitous Infrastructure- Our analysis includes the study of the storage workload, the as-a-Service (IaaS) providers like Amazon S3 and others. user behavior and the performance of the U1 metadata store. Unfortunately, due to the proprietary nature of these sys- Moreover, based on our analysis, we suggest improvements tems, very little is known about their performance and char- to U1 that can also benefit similar Personal Cloud systems. acteristics, including the workload they have to handle daily. Finally, we contribute our dataset to the community, which And indeed, the few available studies have to rely on the so- is the first to contain the back-end activity of a large-scale called “black-box” approach, where traces are collected from Personal Cloud. We believe that our dataset provides unique a single or a limited number of measurement points, in order opportunities for extending research in the field. to infer their properties. This was the approach followed by the most complete analysis of a Personal Cloud to date, the measurement of Dropbox conducted by Drago et al. [2]. Al- Categories and Subject Descriptors though this work describes the overall service architecture, C.4 [Performance of Systems]: Measurement techniques; it provides no insights on the operation and infrastructure of K.6.2 [Management of Computing and Information the Dropbox’s back-end. And also, it has the additional flaw Systems]: Installation management–Performance and us- that it only focuses on small and specific communities, like age measurement university campuses, which may breed false generalizations. Similarly, several Personal Cloud services have been ex- ternally probed to infer their operational aspects, such as Keywords data reduction and management techniques [3, 4, 5], or even transfer performance [6, 7]. However, from external vantage Personal cloud; performance analysis; measurement points, it is impossible to fully understand the operation of Permission to make digital or hard copies of all or part of this work for personal or these systems without fully reverse-engineering them. classroom use is granted without fee provided that copies are not made or distributed In this paper, we present results of our study of U1: the for profit or commercial advantage and that copies bear this notice and the full citation Personal Cloud of Canonical, integrated by default in Linux on the first page. Copyrights for components of this work owned by others than the Ubuntu OS. Despite the shutdown of this service on July author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission 2014, the distinguishing feature of our analysis is that it has and/or a fee. Request permissions from [email protected]. been conducted using data collected by the provider itself. IMC’15, October 28–30, 2015, Tokyo, Japan. U1 provided service to 1.29 million users at the time of the Copyright is held by the owner/author(s). Publication rights licensed to ACM. study on January-February 2014, which constitutes the first ACM 978-1-4503-3848-6/15/10 ...$15.00. complete analysis of the performance of a Personal Cloud in DOI: http://dx.doi.org/10.1145/2815675.2815677. 155 UbuntuOne Analisys Finding Implications and Opportunities 90% of files are smaller than 1MByte (P). Object storage services normally used as a cloud service are not optimized for managing small files [8]. Storage Workload (§ 5) 18 .5% of the upload traffic is caused by file updates Changes in file metadata cause high overhead since the (C). U1 client does not support delta updates (e.g. .mp3 tags). We detected a deduplication ratio of 17% in one File-based cross-user deduplication provides an attrac- month (C). tive trade-off between complexity and performance [5]. DDoS attacks against U1 are frequent (N). Further research is needed regarding secure protocols and automatic countermeasures for Personal Clouds. 1% of users generate 65% of the traffic (P). Very active users may be treated in an optimized manner User Behavior (§ 6) to reduce storage costs. Data management operations (e.g., uploads, file dele- This correlated behavior can be exploited by caching and tions) are normally executed in long sequences (C). prefetching mechanisms in the server-side. User operations are bursty; users transition between User behavior combined with the user per-shard data long, idle periods and short, very active ones (N). model impacts the metadata back-end load balancing. A 20-node database cluster provided service to 1.29M The user-centric data model of a Personal Cloud makes users without symptoms of congestion (N). relational database clusters a simple yet effective ap- Back-end Performance (§ 7) proach to scale out metadata storage. RPCs service time distributions accessing the meta- Several factors at hardware, OS and application-level are data store exhibit long tails (N). responsible for poor tail latency in RPC servers [9]. In short time windows, load values of API servers/DB Further research is needed to achieve better load balanc- shards are very far from the mean value (N). ing under this type of workload. C: Confirms previous results, P: Partially aligned with previous observations, N: New observation Table 1: Summary of some of our most important findings and their implications. the wild. Such a unique data set has allowed us to reconfirm in one month, which calls for further research in automatic results from prior studies, like that of Drago et al. [2], which attack countermeasures in secure and dependable storage paves the way for a general characterization of these systems. protocols. Although our observations may not apply to all But it has also permitted us to expand the knowledge base existing services, we believe that our analysis can help to on these services, which now represent a considerable volume improve the next generation of Personal Clouds [10, 4]. of the Internet traffic. According to Drago et al. [2], the total volume of Dropbox traffic accounted for a volume equivalent Publicly available dataset. We contribute our dataset to around one third of the YouTube traffic on a campus (758GB) to the community and it is available at http:// network. We believe that the results of our study can be cloudspaces.eu/results/datasets. To our knowledge, this useful for both researchers, ISPs and data center designers, is the first dataset that contains the back-end activity of giving hints on how to anticipate the impact of the growing a large-scale Personal Cloud. We hope that our dataset adoption of these services. In summary, our contributions provides new opportunities to researchers in further under- are the following: standing the internal operation of Personal Clouds, promot- ing research and experimentation in this field. Back-end architecture and operation of U1. This Roadmap: The rest of the paper is organized as follows. 2 work provides a comprehensive description of the U1 ar- § chitecture, being the first one to also describe the back-end provides basic background on Personal Clouds. We describe infrastructure of a real-world vendor. Similarly to Dropbox in 3 the details of the U1 Personal Cloud. In 4 we explain§ the trace collection methodology. In 5, §6 and [2], U1 decouples the storage of file contents (data) and their § § logical representation (metadata). Canonical only owns the 7 we analyze the storage workload, user activity and back- end§ performance of U1, respectively. 8 discusses related infrastructure for the metadata service, whereas the actual § file contents are stored separately in Amazon S3. Among work. We discuss the implications of our insights and draw conclusions in 9.