# EC-Kad: An Efficient Data Redundancy Scheme for Cloud Storage
## 1 Introduction  
The swift advancement of cloud computing technology has led to cloud services becoming the predominant service model on the Internet [1, 2, 3, 4]. As an efficient, flexible and scalable computing pattern, cloud services have been widely popularized worldwide, profoundly changing the computing and data management methods of enterprises and individuals. Among them, the cloud storage service, as an important part of cloud services, has become the preferred solution for enterprise and personal data storage because of its convenience, low cost and high scalability. Whether it is for backing up personal photos and videos or the storage and management of massive business data of enterprises, cloud storage provides strong support, greatly simplifying the complexity of data storage and access. However, with the wide application of cloud storage services, data availability and security issues become increasingly prominent. In order to ensure the availability of cloud storage data, especially in the face of potential risks such as unexpected failures, natural disasters or human errors, data disaster tolerance backup has become a crucial means of protection. By saving multiple copies of data in different geographical locations or on different storage media, disaster recovery backup ensures that data can be quickly recovered in case of failure of the primary storage system, thus minimizing the risk of data loss and business interruption. The implementation of data disaster recovery backup is a challenge that cannot be ignored. First, disaster recovery backup requires a large amount of redundant storage space to save data copies, which not only increases the storage cost but also puts forward higher requirements for the management of storage resources. Secondly, with the arrival of the era of big data, the amount of data is exploding, and the storage and management of massivedata has become a huge challenge for cloud storage service providers. Traditional data backup methods, such as simple data replication or triple-backup strategies, can provide a certain degree of disaster tolerance but often lead to a large waste of storage space. For example, for a 1 TB data file, adopting a simple two-copy backup strategy would increase the total storage requirement to 3 TB, with 2 TB being redundant data. This results in a storage utilization rate of only 33.3%, meaning 66.7% of the allocated space is wasted. If a triple-backup strategy is used instead, then 4 TB of storage would be required, further reducing utilization to 25% and increasing the wasted space to 75%. In recent years, network coding technology [5, 6, 7] has been widely applied in various fields to achieve redundant data storage, thereby significantly enhancing the reliability of data storage. Currently, common network coding techniques include random linear network coding (RLC) [8], locally repairable codes (LRCs) [9], regenerating codes [10], and RS erasure codes (RSECs) [11]. Each of these techniques has its own strengths and weaknesses. Random linear network coding has low repair efficiency and high redundancy computation overhead; locally repairable codes offer high repair efficiency but limited fault tolerance; and regenerating codes have strong fault tolerance but also high redundancy computation overhead. In contrast, RS erasure codes achieve a good balance between fault tolerance and redundancy computation overhead, which is why they are widely used [12]. Scholars can select the appropriate network coding technique based on the specific application scenario and requirements. However, in complex cloud storage systems employing network coding redundancy techniques, further enhancing the fault tolerance of encoded data, optimizing the computational overhead of redundant storage and achieving rapid data recovery remain highly challenging issues due to resource heterogeneity and the massive scale of data.