## 1 Introduction  
Large-scale scientific simulations and experiments [1, 2, 3] generate petabytes of data analyzed by scientists worldwide. Although locating the analysis resources close to the data source may appear logical and efficient, this is not always possible. Therefore, distributed solutions are far more prevalent at this scale, and distributed architectures facilitate these collaborations. However, efficient data transfer tools are needed to move vast volumes of data across geographically distributed data centers to support such an ecosystem. Data transfer involves a number of components, including source and destination host machines, storage systems, networks, and file systems. Fine-tuning individual components to improve performance and reduce latencies between components is vital for achieving higher data transfer rates. However, a mismatch in network and storage impedance [4] can impact the performance of end-to-end data transfer. Therefore, to improve scalability, performance, and reduce the storage and network impedance mismatch, data centers often use parallel file systems (PFSs). PFSs use distinct servers to handle metadata and I/O operations [5, 6]. In a PFS (parallel file system), users share resources, which can lead to competition for those resources. This can cause a significant difference between the estimated and actual I/O performance [7]. Therefore, resources must be prevented from being congested during I/O operations to achieve the expected I/O performance. To achieve this objective, researchers proposed a layout-aware object-based bulk data movement framework (layout-aware data scheduling or LADS), optimizing I/O load imbalance issues in PFS using layout-aware I/O scheduling and object storage target (OST) congestion-aware I/O scheduling algorithms [8; 9; 10]. Object-based big data transfer systems (OBDTS) help achieve higher end-to-end data transfer rates than state-of-the-art data transfer frameworks owing to these enhanced scheduling algorithms. Traditional data transfer frameworks such as GridFTP [11; 12] and BBCP [13] ignore the physical distribution of files within the PFS and transfer file data sequentially. Conversely, the object-based bulk data movement framework views files from a physical perspective rather than a logical one. As a result, object based data transfer tools transfer workload as objects rather than files. Owing to the object-based characteristic of the data transfer and parallel processing, objects belonging to the same file can be transferred in a non-sequential order. Data corruption during a data transfer is a major challenge for big data transfer frameworks. Corruption is possible during the data transfer (while in \"transit\") or when the data are stored on the disk (at \"rest\"). In transit, data corruption can happen due to hardware, network, storage, memory, and software failures. Moreover, when files are being transferred between the network and the disk, or vice versa, faulty memory can cause file corruption errors [14]. Although various in-transit (e.g., the checksum calculation in TCP) [15] data integrity mechanisms can help to prevent data corruption, explicit end-to-end data integrity verification is essential for protecting data from subtle errors that may go unnoticed [16]. Therefore, big data transfer frameworks must implement end-to-end data integrity checks to ensure the accuracy and completeness of transferred data. End-to-end integrity is a fundamental aspect of big data transfer systems that ensures the accuracy and reliability of data during the entire transfer process. State-of-the-art data transfer frameworks, such as GridFTP [11; 12] and BBCP [13], transfer the data in a sequential manner. Therefore, simple checksum aggregation-based data integrity verification schemes can be used to verify data integrity in these frameworks. Conversely, the object-based transfer frameworks transfers the workload as objects. Due to parallel processing and object-oriented nature of data transfer, objects are transferred without any specific order.