Actively Measuring Personal Cloud Storage

Gracia-Tinedo, Raúl; Artigas, Marc Sánchez; Moreno-Martínez, Adrián; Cotes, Cristian; López, Pedro

doi:10.1109/cloud.2013.25

Cited by 57 publications

(40 citation statements)

References 14 publications

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“…To wit, the volume of uploaded GBytes per hour can be up to 10x higher in the central day hours compared to the nights. This observation is aligned with previous works, that detected time-based variability in both the usage and performance of Personal Cloud services [2,7]. This effect is probably related to the working habits of users, since U1 desktop clients are by default initiated automatically when users turn on their machines.…”

Section: Macroscopic Daily Usagesupporting

confidence: 72%

“…Perhaps surprisingly, despite their commercial popularity, only few research works have turned attention to analyze the performance of Personal Cloud storage services [2,3,5,6,7,12,28,29]. We classify them into two categories:…”

Section: Related Workmentioning

confidence: 99%

“…To do so, we resort to the Lorenz curve and the Gini coefficient 7 as indicators of inequality. The Gini coefficient varies between 0, which reflects complete equality, and 1, which indicates complete inequality (i.e., only one user consumes all the traffic).…”

Section: Distinguishing Online From Active Usersmentioning

confidence: 99%

“…Similarly, several Personal Cloud services have been externally probed to infer their operational aspects, such as data reduction and management techniques [3,4,5], or even transfer performance [6,7]. However, from external vantage points, it is impossible to fully understand the operation of these systems without fully reverse-engineering them.…”

Section: Introductionmentioning

confidence: 99%

“…In general, Personal Clouds provide clients with 3 main types of access to their service: Web/mobile access, Representational State Transfer (REST) APIs [7,12] clients. Our measurements in this paper focus on the desktop client interactions with U1.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Dissecting UbuntuOne

Gracia-Tinedo

Tian

Sampé

et al. 2015

Proceedings of the 2015 Internet Measurement Conference

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Personal Cloud services, such as Dropbox or Box, have been widely adopted by users. Unfortunately, very little is known about the internal operation and general characteristics of Personal Clouds since they are proprietary services.In this paper, we focus on understanding the nature of Personal Clouds by presenting the internal structure and a measurement study of UbuntuOne (U1). We first detail the U1 architecture, core components involved in the U1 metadata service hosted in the datacenter of Canonical, as well as the interactions of U1 with Amazon S3 to outsource data storage. To our knowledge, this is the first research work to describe the internals of a large-scale Personal Cloud.Second, by means of tracing the U1 servers, we provide an extensive analysis of its back-end activity for one month. Our analysis includes the study of the storage workload, the user behavior and the performance of the U1 metadata store. Moreover, based on our analysis, we suggest improvements to U1 that can also benefit similar Personal Cloud systems.Finally, we contribute our dataset to the community, which is the first to contain the back-end activity of a large-scale Personal Cloud. We believe that our dataset provides unique opportunities for extending research in the field.

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Section: Macroscopic Daily Usagesupporting

confidence: 72%

Section: Related Workmentioning

confidence: 99%

Section: Distinguishing Online From Active Usersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dissecting UbuntuOne

Gracia-Tinedo

Tian

Sampé

et al. 2015

Proceedings of the 2015 Internet Measurement Conference

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Performance evaluation of a distributed storage service in community network clouds

Selimi

Freitag

Cerdí-Alabern

et al. 2015

Concurrency and Computation

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Community networks are decentralized communication networks built and operated by citizens, for citizens. The consolidation of todays cloud technologies offers now for community networks the possibility to collectively built community clouds, building upon user-provided networks and extending towards cloud services. Cloud storage and in particular secure and reliable cloud storage, could become a key community cloud service to enable end-user applications. In this paper we evaluate in a real deployment the performance of Tahoe-LAFS, a decentralized storage system with provider-independent security that guarantees privacy to the users. We evaluate how the Tahoe-LAFS storage system performs when it is deployed over distributed community cloud nodes in a real community network. Furthermore, we evaluate Tahoe-LAFS in the Microsoft Azure commercial cloud platform, in order to compare and understand the impact of homogeneous network and hardware resources on the performance of the Tahoe-LAFS. We observed that the write operation of Tahoe-LAFS resulted in similar performance when using either the community network cloud or the commercial cloud, but the read operation achieved better performance in the Azure cloud, where the reading from multiple nodes of Tahoe-LAFS benefited from the homogeneity of the network and nodes. Our results suggest that Tahoe-LAFS can run on community network clouds with suitable performance for the needed end-user experience.

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Towards Autonomous Data Sharing Across Personal Clouds

Tornyai

Kertész

2014

Lecture Notes in Computer Science

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Cloud Computing has reached a maturity state and high level of popularity that various Cloud services have become a part of our lives. Mobile devices also benefit from Cloud services: the huge data users produce with these devices are continuously posted to online services, which may require the use of several Cloud providers at the same time to efficiently store these data. Using Cloud-based storage services such as Personal Clouds for these purposes are free for certain amount of data; therefore uniting these separate storages can provide a suitable solution for these user needs. In this paper we propose a novel solution for autonomous data management among Personal Clouds. Our approach applies a continuous monitoring component to track the performance of the managed Cloud providers, and based on this measured historical information it manages user data across the interconnected providers in an autonomous way.

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Actively Measuring Personal Cloud Storage

Cited by 57 publications

References 14 publications

Dissecting UbuntuOne

Dissecting UbuntuOne

Performance evaluation of a distributed storage service in community network clouds

Towards Autonomous Data Sharing Across Personal Clouds

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