Fast, reliable, and efficient data transmission across wide-area networks is a predominant bottleneck for data-intensive cloud applications. This paper introduces OneDataShare, which is designed to eliminate the issues plaguing effective cloud-based data transfers of varying file sizes and across incompatible transfer end-points. The vision of OneDataShare is to achieve high-speed data communication, interoperability between multiple transfer protocols, and accurate estimation of delivery time for advance planning, thereby maximizing user-profit through improved and faster data analysis for business intelligence. The paper elaborates on the desirable features of OneDataShare as a cloud-hosted data transfer scheduling and optimization service, and how it is aligned with the vision of harnessing the power of the cloud and distributed computing. Experimental evaluation and comparison with existing real-life file transfer services show that the transfer throughout achieved by OneDataShare is 6.5 times greater.
Server to server data transfers in industry environments have been evaluated in @cite_30 . The proponents have analyzed what benchmarks should be followed when transferring data from one server to another at an industry level. They have evaluated and emphasized the importance of logging and time-stamping the transfer activity at every stage of the transfer for security and auditing purpose. Additionally, the authors provided a mechanism for identifying nearest server when the transfer needs to be speed effective. However, besides security and speed, how to support multi-protocol data transfers and design criteria for such a distributed data delivery system were not discussed. Also the overhead of time-stamping at every stage have been ignored.
@cite_30
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In distributed storage systems, erasure codes have been increasingly deployed to tolerate server failures without loss of data. Traditional erasure codes, such as Reed-Solomon codes, suffer from a high volume of network transfer and disk I O to recover unavailable data at failed storage servers. Typically, unavailable data at different failed storage servers in a distributed storage system are fixed separately. It has been shown that it is possible to reduce the volume of network transfer significantly by reconstructing data from multiple storage servers at the same time. However, there has been no construction of erasure codes to achieve it without imposing strict constraints on system parameters. In this paper, we propose Beehive codes, designed for optimizing the volume of network transfers to fix the data on multiple failed storage servers. Beehive codes can be constructed over a wide range of system parameters at code rate no more than 0.5, while incurring slightly more storage overhead than Reed-Solomon codes. To achieve the optimal storage overhead as Reed-Solomon codes, we further extend vanilla Beehive codes to MDS Beehive codes, which incurs near-optimal volumes of network transfers during reconstruction. We implement both Beehive and MDS Beehive Codes in C++ and evaluate their performance on Amazon EC2. Our evaluation results have clearly shown that the volume of both network transfers and disk I O can be conserved by a substantial margin.