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Distributed and Parallel Databases
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Performance of RAID5 disk arrays with read and write caching

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Abstract

In this paper, we develop analytical models and evaluate the performance of RAID5 disk arrays in normal mode (all disks operational), in degraded mode (one disk broken, rebuild not started) and in rebuild mode (one disk broken, rebuild started but not finished). Models for estimating rebuild time under the assumption that user requests get priority over rebuild activity have also been developed. Separate models were developed for cached and uncached disk controllers. Particular emphasis is on the performance of cached arrays, where the caches are built of Non-Volatile memory and support write caching in addition to read caching. Using these models, we evaluate the performance of arrayed and unarrayed disk subsystems when driven by a database workload such as those seen on systems running any of several popular database managers. In particular, we assume single-block accesses, flat device skew and little seek affinity. With the above assumptions, we find six significant results. First, in normal mode, we find there is no difference in performance between subsystems built out of either small arrays or large arrays as long as the total number of disks used is the same. Second, we find that if our goal is to minimize the average response time of a subsystem in degraded and rebuild modes, it is better to use small arrays rather than large arrays in the subsystem. Third, we find the counter-intuitive result that if our goal is to minimize the average response time of requests to any one array in the subsystem, it is better to use large arrays than small arrays in the subsystem. We call this the best worst-case phenomenon. Fourth, we find that when no caching is used in the disk controller, subsystems built out of arrays have a normal mode performance that is significantly worse than an equivalent unarrayed subsystem built of the same drives. For the specific drive, controller, workload and system parameters we used for our calculations, we find that, without a cache in the controller and operating at typical I/O rates, the normal mode response time of a subsystem built out of arrays is 50% higher than that of an unarrayed subsystem. In rebuild mode, we find that a subsystem built out of arrays can have anywhere from 100% to 200% higher average response time than an equivalent unarrayed subsystem. Out fifth result is that, with cached controllers, the performance differences between arrayed and equivalent unarrayed subsystems shrink considerably. We find that the normal mode response time in a subsystem built out of arrays is only 4.1% higher than that of an equivalent unarrayed system. In degraded (rebuild) mode, a subsystem built out of small arrays has a response time 11% (13%) higher and a subsystem built out of large arrays has a response time 15% (19%) higher than an unarrayed subsystem. Our sixth and last result is that cached arrays have significantly better response times and throughputs than equivalent uncached arrays. For one workload, a cached array with good hit ratios had 5 times the throughout and 10 to 40 times lower response times than the equivalent uncached array. With poor hit ratios, the cached array is still a factor of 2 better in throughput and a factor of 4 to 10 better in response time for this same workload. We conclude that 3 design decisions are important when designing disk subsystems built out of RAID level 5 arrays. First, it is important that disk subsystems built out of arrays have disk controllers with caches, in particular Non-Volatile caches that cache writes in addition to reads. Second, if one were trying to minimize the worst response time seen by any user, one would choose disk array subsystems built out of large RAID level 5 arrays because of the best worst-case phenomenon. Third, if average subsystem response time is the most important design metric, the subsystem should be built out of small RAID level 5 arrays. © 1994 Kluwer Academic Publishers.

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Distributed and Parallel Databases

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