Counterfeiting is a major problem in many industries, causing hundreds of billions of dollars in damages every year. Digital twins further increase a product's value, making it critical to secure the link between the physical and digital world. An entire industry has sprung up to address this problem with a wide variety of product-authentication technologies, or crypto anchors, offering many different trade-offs between supported product surface, security level, price sensitivity, ease of use, etc. Each crypto-anchor vendor provides their own APIs for commissioning, verification and monitoring which makes it hard for application developers to support many of them. This paper introduces a platform that provides a generic model of an object secured by a crypto anchor, supporting any number of product authentication technologies by means of crypto-anchor plugins. Applications programmed against this generic model can immediately interoperate with any of the plugged-in crypto anchors. We present a blockchain architecture for the platform whose decentralization matches the highly differentiated industry structure. We've implemented the system on Hyperledger Fabric.

Multi-tiered storage, where each tier consists of one type of storage device (e.g., SSD, HDD, or disk arrays), is a commonly used approach to achieve both high performance and cost efficiency in large-scale systems that need to store data with vastly different access characteristics. By aligning the access characteristics of the data, either fixed-sized extents or variable-sized files, to the characteristics of the storage devices, a higher performance can be achieved for any given cost. This article presents ExaPlan, a method to determine both the data-to-tier assignment and the number of devices in each tier that minimize the system’s mean response time for a given budget and workload. In contrast to other methods that constrain or minimize the system load, ExaPlan directly minimizes the system’s mean response time estimated by a queueing model. Minimizing the mean response time is typically intractable as the resulting optimization problem is both nonconvex and combinatorial in nature. ExaPlan circumvents this intractability by introducing a parameterized data placement approach that makes it a highly scalable method that can be easily applied to exascale systems. Through experiments that use parameters from real-world storage systems, such as CERN and LOFAR, it is demonstrated that ExaPlan provides solutions that yield lower mean response times than previous works. It supports standalone SSDs and HDDs as well as disk arrays as storage tiers, and although it uses a static workload representation, we provide empirical evidence that underlying dynamic workloads have invariant properties that can be deemed static for the purpose of provisioning a storage system. ExaPlan is also effective as a load-balancing tool used for placing data across devices within a tier, resulting in an up to 3.6-fold reduction of response time compared with a traditional load-balancing algorithm, such as the Longest Processing Time heuristic.

The explosion of data volumes in enterprise environments and limited budgets have triggered the need for multi-tiered storage systems. With the bulk of the data being extremely infrequently accessed, tape is a natural fit for storing such data. In this paper we present our approach to a file storage system that seamlessly integrates disk and tape, enabling a bottomless and cost-effective storage architecture that can scale to accommodate Big Data requirements. The proposed system offers access to data through a POSIX filesystem interface under a single global namespace, optimizing the placement of data across disk and tape tiers. Using a self-contained, standardized and open filesystem format on the removable tape media, the proposed system avoids dependence on proprietary software and external metadata servers to access the data stored on tape. By internally managing the tape tier resources, such as tape drives and cartridges, the system relieves the user from the burden of dealing with the complexities of tape storage. Our implementation, which is based on the GPFS and LTFS filesystems, demonstrates the applicability of the proposed architecture in real-world environments. Our experimental evaluation has shown that this is a very promising approach in terms scalability, performance and manageability. The proposed system has been productized by IBM as LTFS Enterprise Edition.

Multi-tiered storage, where each tier comprises one type of storage device, e.g., SSD, HDD, is a commonly used approach to achieve both high performance and cost efficiency in large-scale systems that need to store data with vastly different access characteristics. By aligning the access characteristics of the data to the characteristics of the storage devices, higher performance can be achieved for any given cost. This article presents ExaPlan, a method to determine both the data-to-tier assignment and the number of devices in each tier that minimize the system's mean response time for a given budget and workload. In contrast to other methods that constrain or minimize the system load, ExaPlan directly minimizes the system's mean response time estimated by a queueing model. Minimizing the mean response time is typically intractable as the resulting optimization problem is both non-convex and combinatorial in nature. ExaPlan circumvents this intractability by introducing a parameterized data-placement approach that makes it a highly scalable method that can be easily applied to exascale systems. Through experiments that use parameters from real-world storage systems, such as CERN and LOFAR, it is demonstrated that ExaPlan provides solutions that yield lower mean response times than previous works. It is also capable of determining a data-to-tier assignment both at the level of files and at the level of fixed-size extents. For some of the workloads evaluated, file-level placement exhibited a significant performance improvement over extent-level placement.

Frame synchronization is studied for pulse-position modulation-encoded longitudinal position (LPOS) words that are embedded in servo patterns written on tape. After introducing soft-output detection of LPOS symbols, the problem of LPOS frame synchronization using soft outputs from the LPOS detector is considered. An efficient LPOS frame-synchronization algorithm that is robust in the presence of transition noise found in magnetic recording channels is then proposed. Finally, the performance of hard-decision LPOS frame synchronization is compared by simulations with that of the proposed soft-decision algorithm, and a hardware implementation of the overall scheme is illustrated.

A new approach is introduced for symbol synchronization in multi-channel data storage systems called global timing control (GTC). Applying results from optimum filtering theory to the state-space model of the timing process in multichannel data storage systems, a multi-input multi-output PLL solution is derived. Efficient implementations are developed through simplifications of the optimal solution obtained. The performance of the GTC technique is analyzed using actual read- back waveforms captured in commercial tape drive systems. The results demonstrate the impressive robustness of GTC and its excellent resilience against loss-of-lock phenomena.

We present a simple but powerful dynamic link adaptation mechanism for wireless LANs, which are based on the IEEE 802.11a standard. Using this method, a transmitter is able to detect whether the quality of the link is improving or deteriorating, and based on this information to switch to a higher or lower transmission rate, respectively. For determining the link quality, the transmitter employs only information that is available locally. Therefore, the proposed method can be implemented without changes or enhancements to the current IEEE 802.11 standard. The efficiency of our method is investigated and evaluated by means of simulations.