As the volume of data is rapidly produced every day, there is a need for the storage media to keep up with the growth rate of digital data created. Despite emerging storage solutions that have been proposed such as Solid State Drive with quad-level cells or penta-level cells, Shingled Magnetic Recording, Linear Tape-Open, and so on, these technologies still fall short of meeting the demand for preserving huge amounts of available data. Moreover, current storage solutions have a limited lifespan, often lasting just a few years. To ensure long-term preservation, data must be continuously migrated to new storage drives. Therefore, there is a need for alternative storage technologies that not only offer high storage capacity but also long persistency. In contrast to existing storage devices, Synthetic Deoxyribonucleic Acid (DNA) storage emerges as a promising candidate for archival data storage, offering both high-density storage capacity and the potential for long-term data preservation. In this article, we will introduce DNA storage, discuss the capabilities of DNA storage based on the current biotechnologies, discuss possible improvements in DNA storage, and explore further improvements with future technologies. Currently, the limitations of DNA storage are due to its weaknesses including high error rates, long access latency, and so on. In this article, we will focus on possible DNA storage research issues based on its relevant bio and computer technologies. Also, we will provide potential solutions and forward-looking predictions about the development and the future of DNA storage. We will discuss DNA storage from the following five perspectives: (1) We will describe the basic background of DNA storage including the basic technologies of read/write DNA storage, data access processes such as Polymerase Chain Reaction-based random access, encoding schemes from digital data to DNA, and required DNA storage format. (2) We will describe the issues of DNA storage based on the current technologies including bio-constraints during the encoding process such as avoiding long homopolymers and containing certain GC contents, different types of errors in synthesis and sequencing processes, low practical capacity with the current technologies, slow read and write performance, and low encoding density for random accesses. (3) Based on the previously mentioned issues, we will summarize the current solutions for each issue, and also give and discuss the potential solutions based on the future technologies. (4) From a system perspective, we will discuss how the DNA storage system will look if the DNA storage becomes commercialized and is widely equipped in archive systems. Some questions will be discussed, including: (i) How do we efficiently index data in DNA storage? (ii) What is a good storage hierarchical storage system with DNA storage? (iii) What will DNA storage be like with the development of technology? (5) Finally, we will provide a comparison with other competitive technologies.

With the rapid increase of available digital data, Deoxyribonucleic Acid (DNA) storage is identified as such a promising candidate due to its long persistency and high areal density, especially for archival storage systems. However, due to biochemical constraints, currently the encoding densities of various DNA storage systems are much less than this upper bound. In this paper, we propose a new Digital Pattern-aware DNA encoding scheme, called DP-DNA, which satisfies the DNA biochemical constraints and efficiently stores digital data in DNA storage with high encoding density. To satisfy the biochemical constraints, our proposed scheme is based on several rotation codes. DP-DNA first analyzes the patterns of each short binary sequence, which will be encoded to a DNA strand, and then selects an appropriate code for encoding the target binary sequence to achieve a high encoding density. An additional encoding field is added to the DNA encoding format, which can distinguish the encoding scheme used for each DNA strand, and thus we can decode DNA data back to its original digital data. Moreover, a new 2bit-code with the highest encoding density (i.e., 2bits/nt) is proposed to add to the pool of code candidates to further increase the encoding density. In addition, a variable-length scheme is applied to increase the feasibility of using 2bit-code scheme. Finally, the experimental results indicate that the proposed DP-DNA achieves 5.9% - 103.5% higher encoding density than the existing encoding schemes with various datasets.

With the storage's demand for high density and long-term preservation, Deoxyribonucleic Acid (DNA) has become a promising candidate to satisfy the requirement of archival storage for rapidly increased digital volume. However, due to the biochemical constraints, DNA storage faces critical issues of low practical capacity and robustness. In this paper, we target image applications and propose to apply approximation to DNA storage to improve the overall encoding density and robustness of DNA storage by using a hybrid lossy and lossless encoding scheme (called HL-DNA). Several lossy and lossless encoding schemes (lossy and lossless codes) are proposed and used to encode incoming binary sequences. These two types of codes are coordinated to balance the encoding density and errors. The lossless codes are used to limit the errors and the lossy codes are used to improve the encoding density. Moreover, the introduced approximation and newly proposed hybrid encoding schemes in one DNA strand can improve the robustness of DNA storage. Finally, the experimental results indicate that the proposed HL-DNA improves the encoding density of DNA storage and makes it much close to the ideal case. Also, HL-DNA achieves higher robustness to the injected errors than other DNA storage codes.

Deoxyribonucleic Acid (DNA) as a storage medium with high density and long-term preservation properties can satisfy the requirement of archival storage for rapidly increased digital volume. The read and write processes of DNA storage are error-prone. Images widely used in social media have the properties of fault tolerance which are well fitted to the DNA storage. However, prior work simply investigated the feasibility of DNA storage storing different types of data and simply store images in DNA storage, which did not fully investigate the fault-tolerant potential of images in the DNA storage. In this paper, we proposed a new image-based DNA system called IMG-DNA, which can efficiently store images in DNA storage with improved DNA storage robustness. First, a new DNA architecture is proposed to fit JPEG-based images and improve the image's robustness in DNA storage. Moreover, barriers inserted in DNA sequences efficiently prevent error propagation in images of DNA storage. The experimental results indicate that the proposed IMG-DNA achieves much higher fault-tolerant than prior work.

The iSCSI protocol communicates over existing IP infrastructure, making it an intriguing storage alternative for cost-conscious HPC users. Two approaches have emerged for using iSCSI in an HPC context. With the first approach, I/O servers — also called data movers — access iSCSI storage at the block level and export it to the cluster nodes via a distributed file system. With the second approach, cluster nodes connect directly to the storage at the block-level via local iSCSI initiators. This paper describes both architectures and compares their performance and scalability across several workloads at increasing cluster sizes. Our results show that cluster and workload characteristics dictate the suitable approach for each cluster. We conclude with design recommendations based on our measured performance results.

Sequencer, privilege-based, and communication history algorithms are popular approaches to implement total ordering, where communication history algorithms are most suitable for parallel computing systems, because they provide best performance under heavy work load. Unfortunately, post-transmission delay of communication history algorithms is most apparent when a system is idle. In this paper, we propose a fast delivery protocol to reduce the latency of message ordering. The protocol optimizes the total ordering process by waiting for messages only from a subset of the machines in the group, and by fast acknowledging messages on behalf of other machines. Our test results indicate that the fast delivery protocol is suitable for both idle and heavy load systems, while reducing the latency of message ordering.

In this paper we propose a high performance parallel file system over iSCSI (iPVFS) for cluster computing. iPVFS provides a cost-effective solution for heterogeneous cluster environment by dividing a set of I/O servers into two groups, one group with higher performance servers as I/O nodes, while another group with relatively lower performance machines serves as storage target nodes. This combination provides a higher aggregate performance because of the cooperative cache among different target nodes. We have developed a model to analyze iPVFS. Our simulation results show that using same number of total nodes, iPVFS outperforms PVFS for both small requests and large requests under different workloads.

Multi-level cache hierarchies are widely used in high-performance storage systems to improve I/O performance. However, traditional cache management algorithms are not suited well for such cache organizations. Recently proposed multi-level cache replacement algorithms using aggressive exclusive caching work well with single or multiple-client, low-correlated workloads, but suffer serious performance degradation with multiple-client, high-correlated workloads. In this paper, we propose a new cache management algorithm that handles multi-level buffer caches by forming a unified cache (uCache) which uses both exclusive caching in L2 storage caches and cooperative client caching. We also propose a new local replacement algorithm, frequency based eviction-reference (FBER), based on our study of access patterns in exclusive caches. Our simulation results show that uCache increases the cumulative cache hit ratio dramatically. Compared to other popular cache algorithms, like LRU, the I/O response time is improved by up to 46% for low-correlated workloads and 53% for high-correlated workloads.