Better than RAID.. (fwd)

Eugene Leitl Eugene.Leitl at lrz.uni-muenchen.de
Thu Jul 19 05:59:31 EDT 2001


---------- Forwarded message ----------
Date: Wed, 18 Jul 2001 21:16:40 -0700
From: Rohit Khare <Rohit at KnowNow.com>
To: FoRK at xent.com
Cc: Ben Sittler <BSittler at KnowNow.com>
Subject: Better than RAID..

http://www.cs.princeton.edu/~rywang/papers/osdi00/MimdRAID/node1.html

1. Introduction

In this paper, we set out to answer a simple question: how do we
systematically increase the performance of a disk array by adding
more disks?

This question is motivated by two phenomena. The first is the
presence of a wide variety of performance-enhancing techniques in the
disk array literature. These include striping[17], mirroring[3], and
replication of data within a track to improve rotational delay[18].
All of these techniques share the common theme of improving
performance by scaling the number of disks. Their performance
impacts, however, are different. Given a fixed budget of disks, an
array designer faces the choice of what combination of these
techniques to use.


The second phenomenon is the increasing cost and performance gap
between disk and memory. This increase is fueled by the explosive
growth of disk areal density, which is at an annual rate of about
60% [8]. On the other hand, the areal density of memory has been
improving at a rate of only 40% per year [8]. The result is a cost
gap of roughly two orders of magnitude today.

As disk latency has been improving at about only 10% per year [8],
disks are becoming increasingly unbalanced in terms of the
relationship between capacity and latency. Although cost per byte and
capacity per drive remain the predominant concerns of a large sector
of the market, a substantial performance-sensitive (and, in
particular, latency-sensitive) market exists. Database vendors today
have already recognized the importance of building a balanced
secondary storage system. For example, in order to achieve high
performance on TPC-C [26], vendors configure systems based on the
number of disk heads instead of capacity. To achieve D times the
bandwidth, the heads form a D-way mirror, a D-way stripe, or a
RAID-10 configuration [4,11,25], which combines mirroring and
striping so that each unit of the striped data is also mirrored. What
is not well understood is how to configure the heads to get the most
out of them.

The key contributions of this paper are:

a flexible strategy for configuring disk arrays and its performance models,
a software-only disk head position prediction mechanism that enables
a range of position-sensitive scheduling algorithms, and
evaluation of a range of alternative strategies that trade capacity
for performance.

More specifically, we present a disk array configuration, called an
SR-Array, that flexibly combines striping with rotational replication
to reduce both seek and rotational delay. The power of this
configuration lies in that it can be flexibly adjusted in a balanced
manner that takes a variety of parameters into consideration. We
present a series of analytical models that show how to configure the
array by considering both disk and workload characteristics.

To evaluate the effectiveness of this approach, we have designed and
implemented a prototype disk array that incorporates the SR-Array
configurations. In the process, we have developed a method for
predicting the disk head location. It works on a wide range of
off-the-shelf hard drives without special hardware support. This
mechanism is not only a crucial ingredient in the success of the
SR-Array configurations, it also enables the implementation of
rotational position sensitive scheduling algorithms, such as Shortest
Access Time First (SATF) [14,23], across the disk array. Because
these algorithms involve inter-disk replicas, without the
head-tracking mechanism, it would have been difficult to choose
replicas intelligently even if the drives themselves perform
sophisticated internal scheduling.

Our experimental results demonstrate that the SR-Array provides an
effective way of trading capacity for improved performance. For
example, under one file system workload, a properly configured
six-disk SR-Array delivers 1.23 to 1.42 times lower latency than that
achieved on highly optimized striping and mirroring systems. The same
SR-Array achieves 1.3 to 2.6 times better sustainable throughput
while maintaining a 15 ms response time on this workload.

The remainder of the paper is organized as follows. Section 2
presents the SR-Array analytical models that guide configuration of
disk arrays. Section 3 describes the integrated simulator and
prototype disk array that implement the SR-Array configuration
models. Section 4 details the experimental results. Section 5
describes some of the related work. Section 6 concludes.


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