Enterprise storage systems depend on disk arrays for their capacity and availability needs. To design and maintain storage systems that efficiently satisfy evolving requirements, it is critical to be able to evaluate configuration alternatives without having to physically implement them. In this paper, we describe an analytical model to predict disk array throughput, based on a hierarchical decomposition of the internal array architecture. We validate the model against a state-of-the-art disk array for a variety of synthetic workloads and array configurations. To our knowledge, no previously published analytical model has either incorporated the combined effects of the complex optimizations present in modern disk arrays, or been validated against a real, commercial array. Our results are quite encouraging for an analytical model: predictions are accurate in most cases within 32% of the observed array performance (15% on the average) for our set of experiments. 1

This paper analyzes various algorithms for scheduling low priority disk drive tasks. The derived closed form solution is applicable to class of greedy algorithms that include a variety of background disk scanning applications. By paying close attention to many characteristics of modern disk drives, the analytical solutions achieve very high accuracy---the difference between the predicted response times and the measurements on two different disks is only 3% for all but one examined workload. This paper also proves a theorem which shows that background tasks implemented by greedy algorithms can be accomplished with very little seek penalty. Using greedy algorithm gives a 10% shorter response time for the foreground application requests and up to a 20% decrease in total background task run time compared to results from previously published techniques.

Abstract—Designing storage systems to provide business continuity in the face of failures requires the use of various data protection techniques, such as backup, remote mirroring, point-in-time copies and vaulting, often in concert. Predicting the dependability provided by such compositions of techniques is difficult, yet necessary for dependable system design. We present a framework for evaluating the dependability of data storage systems, including both individual data protection techniques and their compositions. Our models estimate storage system recovery time, data loss, normal mode system utilization and operational costs under a variety of failure scenarios. We demonstrate the effectiveness of these modeling techniques through a case study using real-world storage system designs and workloads. 1

Abstract—A queueing model is developed for a multi-RAID storage system implemented on modern zoned disks, using fine, accurate access time functions. An extension of a previous analytical model that utilizes Fork-Join composition of M/G/1 queues, it describes zoning directly in terms of the probability distributions or moments of the model’s components, such as seek time, rotational latency and data transfer time. These quantities are calculated directly using the principles of operation of the hardware. This is in contrast to estimating them from simulations and theoretical bounds, as in previous zoned disk models. The resulting multi-RAID model turns out to be accurate, when its performance predictions, characterized here by the mean of queueing and response times, are compared with simulation, and also scalable; not only for the zoned technology but also for alternate ones.

As most computer systems are expected to remain operational 24 hours a day, 7 days a week, they must complete maintenance work while in operation. This work is in addition to the regular tasks of the system and its purpose is to improve system reliability and availability. Nonetheless, additional work in the system, although labeled as best effort or low priority, still affects the performance of foreground tasks, especially if background/foreground work is non-preemptive. In this paper, we propose an analytic model to evaluate the performance trade-offs of the amount of background work that a storage system can sustain. The proposed model results in a quasi-birth-death (QBD) process that is analytically tractable. Detailed experimentation using a variety of workloads shows that under dependent arrivals both foreground and background performance strongly depends on system load. In contrast, if arrivals of foreground jobs are independent, performance sensitivity to load is reduced. The model identifies dependence in the arrivals of foreground jobs as an important characteristic that controls the decision of how much background load the system can accept to maintain high availability and performance gains. Keywords: Foreground/background jobs; storage systems;

A common practice in system design is to treat features intended to enhance performance and reliability as low priority tasks by scheduling them during idle periods, with the goal to keep these features transparent to the user. In this paper, we present an algorithmic framework that determines the schedulability of non-preemptable low priority tasks in storage systems. The framework estimates when and for how long idle times can be utilized by low priority background tasks, without violating pre-defined performance targets of user foreground tasks. The estimation is based on monitored system information that includes the histogram of idle times. This histogram captures accurately important statistical characteristics of the complex demands of the foreground activity. The robustness and the effectiveness of the proposed framework is corroborated via extensive trace driven simulations under a wide range of system conditions and background activities, and via experimentation on a Linux kernel 2.6.22 prototype.

also sponsored through generous grants from the EMC Corporation and the Intel

All server storage environments depend on disk arrays to satisfy their capacity, reliability, and availability requirements. In order to manage these storage systems efficiently, it is necessary to understand the behavior of disk arrays and predict their performance. We develop an analytical model that estimates mean performance measures of disk arrays under a synchronous I/O workload. Synchronous I/O requests are generated by jobs that each block while their request is serviced. Upon I/O service completion, a job may use other computer resources before issuing another I/O request. Our disk array model considers the effect of workload sequentiality, read-ahead caching, write-back caching, and other complex optimizations incorporated into most disk arrays. The model is validated against a mid-range disk-array for a variety of synthetic I/O workloads. The model is computationally simple and scales easily as the number of jobs issuing requests increases, making it potentially useful to performance engineers.

Various activities that intend to enhance performance, reliability, and availability of storage systems are scheduled with low priority and served during storage system idle times. Under such conditions, idleness becomes a valuable “resource ” that needs to be efficiently managed. A common approach in system design is to be non-work-conserving by “idle waiting”, i.e., delay scheduling of background jobs to avoid slowing down upcoming foreground tasks. In this paper, we complement “idle-waiting ” with the “estimation ” of background work to be served in every idle interval to manage the trade-off between the performance of foreground and background tasks. As a result, the storage system is better utilized without compromising foreground performance. Our analysis shows that if idle times have low variability, then it is not necessary to idle wait before starting a background job. Only if idle times are highly variable, then idle waiting is necessary to minimize the impact of background activity on foreground performance. We further show that if there is burstiness in addition to high variability in idle intervals, then it is possible to predict accurately the length of incoming idle times and use that information to serve more background jobs without affecting foreground performance.

Various activities that intend to enhance performance, reliability, and availability of storage systems are scheduled with low priority and served during idle times. Under such conditions, idleness becomes a valuable “resource ” that needs to be efficiently managed. A common approach in system design is to be nonwork conserving by “idle waiting”, that is, delay the scheduling of background jobs to avoid slowing down upcoming foreground tasks. In this article, we complement “idle waiting ” with the “estimation ” of background work to be served in every idle interval to effectively manage the trade-off between the performance of foreground and background tasks. As a result, the storage system is better utilized without compromising foreground performance. Our analysis shows that if idle times have low variability, then idle waiting is not necessary. Only if idle times are highly variable does idle waiting become necessary to minimize the impact of background activity on foreground performance. We further show that if there is burstiness in idle intervals, then it is possible to predict accurately the length of incoming idle intervals and use this information to serve more background jobs without affecting foreground performance.