Outsourcing data on cloud storage services has already attracted great attention due to the prospect of rapid data growth and storing efficiencies for customers. The coding-based cloud storage approach can offer a more reliable and faster solution with less storage space in comparison with replication-based cloud storage. LT codes are the famous member of the rateless code family that can improve performance of storage systems utilizing good degree distributions. Since degree distribution plays a key role in LT codes performance, recently introduced Poisson robust soliton distribution (PRSD) and combined Poisson robust soliton distribution (CPRSD) motivate us to investigate LT code-based cloud storage system. So, we exploit LT codes with new degree distributions to provide lower average degree and higher decoding efficiency, specifically when receiving fewer encoding symbols, compared with popular degree distribution, robust soliton distribution (RSD). In this paper, we show that proposed cloud storage outperforms traditional ones in terms of storage space and robustness encountering unavailability of encoding symbols, due to compatible properties of PRSD and CPRSD with cloud storage essence. Furthermore, a modified decoding process is presented based on required encoding symbols behavior to reduce data retrieval time. Numerical results confirm improvement of cloud storage performance.

由于数据快速增长和客户存储效率的前景，云存储服务的外包数据已经引起了极大的关注。与基于复制的云存储相比，基于编码的云存储方法可以以更少的存储空间提供更可靠、更快速的解决方案。LT 代码是无率代码家族的著名成员，它可以利用良好的度分布来提高存储系统的性能。由于度分布在 LT 码性能中起着关键作用，最近引入的泊松鲁棒孤子分布 (PRSD) 和组合的泊松鲁棒孤子分布 (CPRSD) 促使我们研究基于 LT 码的云存储系统。因此，我们利用具有新度分布的 LT 码来提供更低的平均度和更高的解码效率，特别是在接收较少的编码符号时，与流行度分布相比，鲁棒孤子分布（RSD）。在本文中，我们表明，由于 PRSD 和 CPRSD 与云存储本质的兼容特性，所提出的云存储在存储空间和遇到编码符号​​不可用的鲁棒性方面优于传统云存储。此外，基于所需的编码符号行为，提出了一种改进的解码过程，以减少数据检索时间。数值结果证实了云存储性能的改进。由于 PRSD 和 CPRSD 与云存储本质的兼容特性。此外，基于所需的编码符号行为，提出了一种改进的解码过程，以减少数据检索时间。数值结果证实了云存储性能的改进。由于 PRSD 和 CPRSD 与云存储本质的兼容特性。此外，基于所需的编码符号行为，提出了一种改进的解码过程，以减少数据检索时间。数值结果证实了云存储性能的改进。