Magnetic tape provides a cost-effective way to retain the exponentially increasing volumes of data being created in recent years. The low cost per terabyte combined with tape’s low energy consumption make it an appealing option for storing infrequently accessed data and has resulted in a resurgence in use of the technology. Magnetic tape as a digital data storage technology was first commercialized in the early 1950’s and has evolved continuously since then. Despite its long history, tape has significant potential for continued capacity and data rate scaling. This article strives to provide an overview of linear magnetic tape technology, usage, history, and future outlook. After a short introduction, the article delves into the details of how modern tape drives and media operate, including the basic mechanism and physics of magnetic recording, current tape media technology, state-of-the-art tape head technology, tape layout and encoding, data retrieval, timing-based servo and mechatronics of a tape drive, and the capabilities of current drives. This is followed by a discussion of tape libraries, an overview of tape library performance modeling research, operating system-level and application-level tape support, tape use cases, and the future scaling potential and outlook of tape. The article concludes with a history of tape hardware, media, usage and software.

Magnetic tape is a digital data storage technology that has evolved continuously over the last seven decades. It provides a cost-effective way to retain the rapidly increasing volumes of data being created in recent years. The low cost per terabyte combined with tape’s low energy consumption make it an appealing option for storing infrequently accessed data and has resulted in a resurgence in use of the technology. Power and operational failures may damage tapes and lead to data loss. To protect stored data against loss and achieve high data reliability, an erasure coding scheme is employed. A theoretical model capturing the effect of tape failures and latent errors on system reliability is developed. Closed-form expressions are derived for the the Mean Time to Data Loss (MTTDL) and the Expected Annual Fraction of Effective Data Loss (EAFEDL) reliability metric, which assesses losses at the file, object, or block, level. The results obtained demonstrate that, for realistic values of bit error rates, reliability is unaffected by the presence of latent errors. The theoretical results derived can be used to dimension and provision tape libraries to provide desired levels of durability.

Immersive and interactive experiences offered by virtual world simulations (VWS) are becoming increasingly indispensable in today's digital era, particularly in domains such as gaming, engineering, and education. Cloud computing provides a compelling solution for VWS, with its scalability, costeffectiveness, and accessibility, combined with efficient processing capabilities of hardware accelerators such as FPGAs and GPUs. This paper presents APEIRON, an in-progress distributed library built on the Ray framework, that aims to facilitate the effective composition of hardware accelerators on public clouds and emerging disaggregated platforms. Additionally, this work introduces a novel composability metric, analyzing the intricate relationship between resource utilization and workload performance. This study focuses on theoretical composability analyses, laying groundwork for optimized utilization of hardware resources to enhance efficiency, thereby potentially reducing operational costs.

The evolution of cloud applications into loosely-coupled microservices opens new opportunities for hardware accelerators to improve workload performance. Existing accelerator techniques for cloud sacrifice the consolidation benefits of microservices. This paper presents CloudiFi, a framework to deploy and compare accelerators as a cloud service. We evaluate our framework in the context of a financial workload and present early results indicating up to 485 x gains in microservice response time.

Magnetic tape provides a cost-effective way to retain the exponentially increasing volumes of data. The low cost per gigabyte and the low energy consumption render tape a preferred option over hard disk drives and flash for infrequently accessed data. Assessing the performance of tape library systems is central to achieving appropriate storage provisioning and dimensioning. Performance is affected by the number and the operational characteristics of the tape drives and the robotic arms, and the mount and unmount policies deployed. In this paper, we develop a novel analytical model that accurately captures the principal aspects of tape library operation. Several relevant performance measures including the mean waiting time and the mount/unmount rates are derived. The model provides useful insights into the behavior of the tape library mechanisms and yields results, which enable a better understanding of the design tradeoffs. The validity of the model developed is confirmed by demonstrating a good agreement of the predicted performance with that obtained by simulation across various configurations.

New optimum binary shortened cyclic codes with redundancy r = 32 and burst-error correction capability b are presented. The codes are found by performing an exhaustive computer search using the Kasami algorithm, and their performance is compared with analytical bounds by Reiger, Abramson and Campopiano. The true burst-error correction capability of the [2112, 2080] shortened Fire code selected for 10 Gb/s Ethernet is determined to be b = 11 and [2112, 2080] shortened cyclic codes with higher burst-error correction capability b = 13 are given. The double burst-error detection properties of three cyclic redundancy check codes used in standards are compared.

In multi-tier storage systems with large amounts of data, most of the data is stored on inexpensive slower tiers such as cloud or tape to achieve cost savings. This also implies that retrieving the data from the slower storage tiers incurs high latency. Therefore, it would be beneficial to proactively prefetch data from slower tiers to faster tiers by predicting future data accesses. State-of-the-art access prediction methods typically record access history of individual files, data objects, or data segments. However, in systems with large amounts of infrequently accessed (or cold) data, file-level access history is often unavailable for much of the data due to the low frequency of access. In this paper, we extract information from file metadata to predict file accesses in a storage system. The proposed method relies on the hypothesis that users and applications access data stored in the system in a given context and that the context and, therefore, the set of files that are likely to be accessed can be identified by detecting access patterns in file metadata. As an application, we consider the LOFAR radio telescope's long term archive, where the access patterns are learned based on a rich set of metadata, and these patterns are then used to make predictions as to likely future accesses by the astronomers.