دانلود رایگان مقاله انگلیسی شکاف ظرفیت داده ها در ذخیره سازی داده های بزرگ - الزویر 2018

abstract

Big data is aggressive in its production, and with the merger of Cloud computing and IoT, the huge volumes of data generated are increasingly challenging the storage capacity of data centres. This has led to a growing data-capacity gap in big data storage. Unfortunately, the limitations faced by current storage technologies have severely handicapped their potential to meet the storage demand of big data. Consequently, storage technologies with higher storage density, throughput and lifetime have been researched to overcome this gap. In this paper, we first introduce the working principles of three such emerging storage technologies, and justify their inclusion in the study based on the tremendous advances received by them in the recent past. These storage technologies include Optical data storage, DNA data storage & Holographic data storage. We then evaluate the recent advances received in storage density, throughput and lifetime of these emerging storage technologies, and compare them with the trends and advances in prevailing storage technologies. We finally discuss the implications of their adoption, evaluate their prospects, and highlight the challenges faced by them to bridge the data-capacity gap in big data storage.

9. Conclusion

Data-capacity gap in big data storage is eminent and inevitable, and can be bridged with storage technologies with greater storage densities, higher throughputs, and longer lifetime. In this paper, we first discussed the working principles of three emerging storage technologies, i.e., ODS, DDS and HDS, that possess these features. We then evaluated the advances received by them in storage density, throughput and lifetime, and quantitatively compared them with the advances in current storage technologies, i.e., HDD, SSD and LTO. We also investigated the implications of the adoption of these emerging storage technologies, evaluated their prospects and highlighted the challenges.

The study suggests that advances in storage density of ODS and DDS has crossed Pbpsi scale areal density, and thus can easily overcome the data-capacity gap in big data centres. However, due to absence of rewritable storage in ODS, its scope is currently limited to longterm archival of immutable data that is accessed frequently in large volumes and with low latency. Similarly, due to low throughput offered by DDS, its scope is currently limited to longterm archival of mutable data that is accessed less frequently and with high latency. Nevertheless, both technologies offer longer lifetimes. For to be considered for all workloads of a big data centre, ODS and DDS must offer rewritable storage and higher throughput, respectively. In addition, to be economically feasible, ODS and DDS must alleviate the cost of ownership and the cost of reading/writing, respectively.