Many important big data use cases do not require data to be instantly available. Examples are video recordings in TV and film industry, surveillance videos and data from scientific experiments. Archiving such data to high-latency media storage, such as tape and optical disk libraries, results in significant cost savings. In this context, data is accessed by first staging it to low-latency media. However, archiving and staging operations incur additional device and bandwidth costs for both the active and archiving tiers, and might impact user data access performance. For instance, in terms of cost and performance, it is often suboptimal to archive all the data. This paper presents ExaPlan Archive, a scheme to determine the data placement and number of devices required in each tier of a multitiered storage system comprised of archival and active tiers that minimize the latency of the active tiers under budget and staging-time constraints. The efficiency of the proposed optimized archiving scheme is compared with an existing scheme that optimizes multitier storage with only direct-access tiers. The two schemes are evaluated using a staging workload of LOFAR radio telescopes long-term archive for astronomical observation data.

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.

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.

Data with vastly different access characteristics is efficiently stored in multi-tiered storage systems. A cost-effective way to retain large volumes of infrequently accessed data is to store it on tape. Steady developments in tape technology deliver ever increasing storage capacities at low cost. This has established tape as a viable solution to cope with the extreme data growth in the context of Big Data. Assessing the performance of the various tiers is central to achieving appropriate tier dimensioning and storage provisioning. To that end, we develop an analytical model to evaluate the performance of a tape library system that considers various relevant aspects, such as the number of cartridges and tape drives as well as different mount/unmount policies. Closed-form expressions for the corresponding mean waiting times are derived. The validity of the model developed is confirmed by demonstrating that the predicted performance matches well with that obtained by simulation across a wide range of system parameter values.

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.

A recent article on the reliability of RAID-6 storage systems overlooks certain relevant prior work published in the past 20 years and concludes that the widely used mean time to data loss (MTTDL) metric does not provide accurate results. In this note, we refute this position by invoking uncited relevant prior work and demonstrating that the MTTDL remains a useful metric.

Several redundancy and recovery schemes have been developed to enhance the reliability of storage systems. The effectiveness of these schemes has predominately been evaluated based on the mean time to data loss (MTTDL) metric, which has been proven useful for assessing tradeoffs, for comparing schemes, and for estimating the effect of the various parameters on system reliability. In the context of distributed and cloud storage systems, for economical reasons, it is of great importance to also consider the magnitude along with the frequency of data loss. We focus on the following reliability metric: the expected annual fraction of data loss (EAFDL), that is, the fraction of stored data that is expected to be lost by the system annually. We present a general methodology to obtain the EAFDL metric analytically, in conjunction with the MTTDL metric, for various redundancy schemes and for a large class of failure time distributions that also includes real-world distributions like Weibull and gamma. As a demonstration, we subsequently apply this methodology to derive these metrics analytically and to assess the reliability of a replication-based storage system under clustered, declustered, and symmetric data placement schemes. We show that the declustered placement scheme offers superior reliability in terms of both metrics. Previous work has used simulation to evaluate the magnitude of data loss, but this is the first work to analytically assess it, and the first to present a general theoretical framework for this context.

Network bandwidth between sites is typically more scarce than bandwidth within a site in geo-replicated cloud storage systems, and can potentially be a bottleneck for recovery operations. We study the reliability of geo-replicated cloud storage systems taking into account different bandwidths within a site and between sites. We consider a new recovery scheme called staged rebuild and compare it with both a direct scheme and a scheme known as intelligent rebuild. To assess the reliability gains achieved by these schemes, we develop an analytical model that incorporates various relevant aspects of storage systems, such as bandwidths, latent sector errors, and failure distributions. The model applies in the context of Open Stack Swift, a widely deployed cloud storage system. Under certain practical system configurations, we establish that order of magnitude improvements in mean time to data loss (MTTDL) can be achieved using these schemes.

The reliability of data storage systems is adversely affected by the presence of latent sector errors. As the number of occurrences of such errors increases with the storage capacity, latent sector errors have become more prevalent in today's high capacity storage devices. Such errors are typically not detected until an attempt is made to read the affected sectors. When a latent sector error is detected, the redundant data corresponding to the affected sector is used to recover its data. However, if no such redundant data is available, then the data of the affected sector is irrecoverably lost from the storage system. Therefore, the reliability of data storage systems is affected by both the complete failure of storage nodes and the latent sector errors within them. In this article, closed-form expressions for the mean time to data loss (MTTDL) of erasure coded storage systems in the presence of latent errors are derived. The effect of latent errors on systems with various types of redundancy, data placement, and sector error probabilities is studied. For small latent sector error probabilities, it is shown that the MTTDL is reduced by a factor that is independent of the number of parities in the data redundancy scheme as well as the number of nodes in the system. However, for large latent sector error probabilities, the MTTDL is similar to that of a system using a data redundancy scheme with one parity less. The reduction of the MTTDL in the latter case is more pronounced than in the former one.

To improve the reliability of data storage systems, certain data placement schemes spread replicas corresponding to data stored on each node across several other nodes. When node failures occur, this enables parallelizing the rebuild process which in turn results in reducing the rebuild times. However, the underlying assumption is that the parallel rebuild process is facilitated by sufficient availability of network bandwidth to transfer data across nodes at full speed. In a large-scale data storage system where the network bandwidth for rebuild is constrained, such placement schemes will not be as effective. In this paper, it is shown through analysis and simulation how the spread of replicas across nodes affects system reliability under a network bandwidth constraint. Efficient placement schemes that can achieve high reliability in the presence of bandwidth constraints are proposed. Furthermore, in a dynamically changing storage system, in which the number of nodes and the network rebuild bandwidth can change over time, the data placement can be accordingly adapted to maintain a high level of reliability.

The placement of replicas across storage nodes in a replication-based storage system is known to affect rebuild times and therefore system reliability. Earlier work has shown that, for a replication factor of two, the reliability is essentially unaffected by the replica placement scheme because all placement schemes have mean times to data loss (MTTDLs) within a factor of two for practical values of the failure rate, storage capacity, and rebuild bandwidth of a storage node. However, for higher replication factors, simulation results reveal that this no longer holds. Moreover, an analytical derivation of MTTDL becomes intractable for general placement schemes. In this paper, we develop a theoretical model that is applicable for any replication factor and provides a good approximation of the MTTDL for small failure rates. This model characterizes the system behavior by using an analytically tractable measure of reliability: the probability of the shortest path to data loss following the first node failure. It is shown that, for highly reliable systems, this measure approximates well the probability of all paths to data loss after the first node failure and prior to the completion of rebuild, and leads to a rough estimation of the MTTDL. The results obtained are of theoretical and practical importance and are confirmed by means of simulations. As our results show, the declustered placement scheme, contrary to intuition, offers a reliability for replication factors greater than two that does not decrease as the number of nodes in the system increases.

The asymptotic capacity at low input powers of an average-power limited or an average- and peak-power limited discrete-time Poisson channel is considered. For a Poisson channel whose dark current is zero or decays to zero linearly with its average input powerε, capacity scales likeεlog 1/εfor smallε. For a Poisson channel whose dark current is a nonzero constant, capacity scales, to within a constant, likeεlog log 1/ε for smallε.

Replication is a widely used method to protect large-scale data storage systems from data loss when storage nodes fail. It is well known that the placement of replicas of the different data blocks across the nodes affects the time to rebuild. Several systems described in the literature are designed based on the premise that minimizing the rebuild times maximizes the system reliability. Our results however indicate that the reliability is essentially unaffected by the replica placement scheme. We show that, for a replication factor of two, all possible placement schemes have mean times to data loss (MTTDLs) within a factor of two for practical values of the failure rate, storage capacity, and rebuild bandwidth of a storage node. The theoretical results are confirmed by means of event-driven simulation. For higher replication factors, an analytical derivation of MTTDL becomes intractable for a general placement scheme. We therefore use one of the alternate measures of reliability that have been proposed in the literature, namely, the probability of data loss during rebuild in the critical mode of the system. Whereas for a replication factor of two this measure can be directly translated into MTTDL, it is only speculative of the MTTDL behavior for higher replication factors. This measure of reliability is shown to lie within a factor of two for all possible placement schemes and any replication factor. We also show that for any replication factor, the clustered placement scheme has the lowest probability of data loss during rebuild in critical mode among all possible placement schemes, whereas the declustered placement scheme has the highest probability. Simulation results reveal however that these properties do not hold for the corresponding MTTDLs for a replication factor greater than two. This indicates that some alternate measures of reliability may not be appropriate for comparing the MTTDL of different placement schemes.