Distributed Storage Allocation for High Reliability

Leong, Derek; Dimakis, Alexandros G.; Ho, Tracey

doi:10.1109/icc.2010.5502492

Cited by 34 publications

(29 citation statements)

References 10 publications

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“…Another research effort focuses on the design of storage system operational optimization based on existing erasure codes to achieve some specific optimal performance matrices. These operational designs may include storage redundancy overhead optimization, storage allocation and repair, and others [16], [17], [18], [19]. As aforementioned, compared with erasure code based cloud storage systems, LT code based systems simplify content placement and recovery problems at the cost of longer retrieval delay.…”

Section: Related Workmentioning

confidence: 99%

Delay-Optimized File Retrieval under LT-Based Cloud Storage

Foh

Wen

et al. 2017

IEEE Trans. Cloud Comput.

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Abstract-Fountain-code based cloud storage system provides reliable online storage solution through placing unlabeled content blocks into multiple storage nodes. Luby Transform (LT) code is one of the popular fountain codes for storage systems due to its efficient recovery. However, to ensure high success decoding of fountain codes based storage, retrieval of additional fragments is required, and this requirement could introduce additional delay. In this paper, we show that multiple stage retrieval of fragments is effective to reduce the fileretrieval delay. We first develop a delay model for various multiple stage retrieval schemes applicable to our considered system. With the developed model, we study optimal retrieval schemes given requirements on success decodability. Our numerical results suggest a fundamental tradeoff between the file-retrieval delay and the target probability of successful file decoding, and that the file-retrieval delay can be significantly reduced by optimally scheduling packet requests in a multi-stage fashion.

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Section: Related Workmentioning

confidence: 99%

Delay-Optimized File Retrieval under LT-Based Cloud Storage

Foh

Wen

et al. 2017

IEEE Trans. Cloud Comput.

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“…Dimakis et al proposed several erasure coding algorithms for maintaining a distributed storage system in a dynamic network [11]. Leong et al proposed an algorithm for optimal data allocation that maximizes the recovery probability [12]. Aguilera et al…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Fault-Tolerant Data Storage and Processing in Mobile Cloud

Chen

Won

Stoleru

et al. 2015

IEEE Trans. Cloud Comput.

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Abstract-Despite the advances in hardware for hand-held mobile devices, resource-intensive applications (e.g., video and image storage and processing or map-reduce type) still remain off bounds since they require large computation and storage capabilities. Recent research has attempted to address these issues by employing remote servers, such as clouds and peer mobile devices. For mobile devices deployed in dynamic networks (i.e., with frequent topology changes because of node failure/unavailability and mobility as in a mobile cloud), however, challenges of reliability and energy efficiency remain largely unaddressed. To the best of our knowledge, we are the first to address these challenges in an integrated manner for both data storage and processing in mobile cloud, an approach we call k-out-of-n computing. In our solution, mobile devices successfully retrieve or process data, in the most energy-efficient way, as long as k out of n remote servers are accessible. Through a real system implementation we prove the feasibility of our approach. Extensive simulations demonstrate the fault tolerance and energy efficiency performance of our framework in larger scale networks.

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“…Under this setting, the optimal allocation to minimize the delay with respect to users and files has been found both for the coded and uncoded files. On the other hand, for the capture of the bursty and random behavior of the small cell availability, the works [14], [15] have modeled the link between the user and small cell as an erasure channel, i.e. the link is either switched on or switched off with some probability, and the statics is known to the allocation controller.…”

Section: Distributed Storage Allocationmentioning

confidence: 99%

“…the link is either switched on or switched off with some probability, and the statics is known to the allocation controller. More specifically, [14] supposes that only a random set of the small cells are accessible, while [15] models each link as the Bernoulli process with a given erasure probability.…”

Section: Distributed Storage Allocationmentioning

confidence: 99%

Cloud storage for Small Cell Networks

Baştuğ

Guenego

Debbah

2012

2012 IEEE 1st International Conference on Cloud Networking (CLOUDNET)

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Abstract-The Massive dense deployment of Small Cell Networks (SCNs) is a promising way of increasing capacity. Interestingly, by having such a huge amount of user devices as well as small cells deployed in indoor or outdoor areas, one can take benefit of such distributed network for storage purposes. Hence, Depending on the deployed scenario, these storage units can also be used for content caching to relieve the backhaul constraints and increase the peak rate. In this work, by extending concepts of cloud storage to SCNs, we discuss the theoretical challenges in order to embed small cells with storage capabilities. We also briefly introduce Open Cloud Protocol (OCP) as a unified software storage framework.

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Distributed Storage Allocation for High Reliability

Cited by 34 publications

References 10 publications

Delay-Optimized File Retrieval under LT-Based Cloud Storage

Delay-Optimized File Retrieval under LT-Based Cloud Storage

Energy-Efficient Fault-Tolerant Data Storage and Processing in Mobile Cloud

Cloud storage for Small Cell Networks

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