MultiMap: Preserving disk locality for
Minglong Shao, Steven W. Schlosser, Stratos Papadomanolakis, Jiri Schindler,
Anastassia Ailamaki, Christos Faloutsos, Gregory R. Ganger
Parallel Data Laboratory
Carnegie Mellon University
Pittsburgh, PA 15213-3890
MultiMap is a new approach to mapping multidimensional datasets to the linear address space of storage systems.
MultiMap exploits modern disk characteristics to provide full streaming bandwidth for one (primary) dimension and
maximally efficient non-sequential access (i.e., minimal seek and no rotational latency) for the other dimensions.
This is in contrast to existing approaches, which either severely penalize non-primary dimensions or fail to provide
full streaming bandwidth for any dimension. Experimental evaluation of a prototype implementation demonstrates
MultiMap’s superior performance for range and beam queries. On average, MultiMap reduces overall I/O time by over
50% when compared to traditional naive layouts and by over 30% when compared to a Hilbert curve approach. For
scans of the primary dimension, MultiMap and naive both provide almost two orders of magnitude higher throughput
than the Hilbert curve approach.
We thank the members and companies of the PDL Consortium (including EMC, Hewlett-Packard, Hitachi, IBM, Intel, Microsoft, Network
Appliance, Oracle, Panasas, Seagate, Sun, and Veritas) for their interest, insights, feedback, and support. This work is funded in part by NSF grants
CCR-0113660, IIS-0133686, and CCR-0205544, as well as by an IBM faculty partnership award.
Keywords: multidimensional dataset, disk performance, database access, spacial locality
The simple linear abstraction of storage devices offered by standard interfaces such as SCSI is insufficient
for workloads that access out-of-core multidimensional datasets. To illustrate the problem,
consider mapping a relational database table onto the linear address space of a single disk
drive or a logical volume consisting of multiple disks. A naive mapping requires making a choice
between storing a table in row-major or column-major order, trading off access performance along
the two dimensions. While accessing the table in the primary order is efficient thanks to requests to
sequential disk blocks, access in the other order is inefficient. The accesses at regular strides give
random access-like performance and incur short seeks and rotational latencies. Similarly, range
queries are inefficient single dimension. The problem is the same for datasets with higher dimensionality:
sequentiality can only be preserved for a single dimension, and all other dimensions will be scattered across the disk.
The shortcomings of non-sequential disk drive accesses have sparked a healthy research effort
to improve performance when accessing multidimensional datasets. These often start with a problem
statement like this: find a mapping of points in an N-dimensional dataset to a one-dimensional
abstraction of the storage device. Mapping schemes such as Z-ordering , Hilbert curves ,
and Gray-coded curve , can help preserve locality for multidimensional datasets, but they do
not allow accesses along any one dimension to take advantage of streaming bandwidth, disk’s peak
performance. This is a high price to pay, since the difference between streaming bandwidth and
non-sequential accesses is at least two orders of magnitude.
This paper introduces MultiMap, a new mapping model that properly matches N-dimensional
datasets to physical characteristics of storage. Without breaking existing abstractions, MultiMap
offers streaming disk bandwidth for access to one dimension and outperforms all existing schemes
when accessing data blocks for other dimensions. MultiMap maps such blocks on nearby disk
tracks to avoid rotational latency and to incur only the minimum seek time. This access is possible
thanks to high disk track densities, which make the cost of seeking to nearby tracks (typically
within 10-20 cylinders of the current position) nearly equal. MultiMap gives applications the
flexibility to choose a specific block on any of these tracks whereby such blocks, called adjacent
blocks, can be accessed without any rotational latency and for the same positioning cost.
We describe a general model for MultiMap and offer an API that exposes these adjacent disk
blocks. This API hides from applications disk-specific details and maintains the abstractions established
by existing storage interfaces. We evaluate MultiMap analytically and on a prototype
implementation that uses a logical volume of real disk drives with 3-D and 4-D datasets The 4-
D dataset represents an earthquake simulation workload. Our comparison of MultiMap to naive
linearizing mapping scheme and a space-filling Hilbert curve shows that MultiMap speeds up a
variety of spatial range queries on average by 1:3–2.
This remainder of the paper is organized as follows. Section 2 describes related work. Section
3 defines adjacent blocks and describes the underpinnings for enabling efficient accesses to
these blocks on nearby tracks. Section 4 describes an algorithm for mapping multidimensional
datasets. Section 5 evaluates the performance of the system for 3-D, 4-D, and 8-D datasets.
2 Related work
There is a large amount of research on access methods for multidimensional datasets. Under the
premise that disks are one-dimensional devices (as suggested by its abstraction of a linear space
of fixed-size blocks), various multidimensional access methods have been proposed in literature.
These methods attempt to order multidimensional data so that spatial locality can be preserved as
much as possible on the one-dimensional disk abstraction. With the premise that objects that are
close in the multidimensional space are also physically close in the disk space, they assume that
fetching them will not incur inefficient random-like accesses. The property of preserving spatial
locality is often called clustering in multidimensional applications.
Multidimensional access methods, which can be roughly classified into point access methods
and spatial access methods, include hash structures (for example, grid files), hierarchical structure
such as various trees, and space-filling curves. These spatial access methods, as surveyed by Gaede
et al. , typically assume that all disk accesses to the tree nodes are random, and thus equally
costly. By contrast, MultiMap deviates from that assumption, and achieves better performance.
Space-filling methods such as Gray-coded curves , Hilbert curve , and Z-ordering 
use linear mapping: they traverse the multidimensional dataset linearly and impose a total order
on the dataset when storing data on disks. Moon et al.  analyze the clustering properties of
the Hilbert curve and indicate that it has better clustering than Z-ordering, and hence better I/O
performance in terms of per-node average access time. Therefore, we compare the performance of
MultiMap against Hilbert curve mapping.
The existing multidimensional data access methods proposed thus far focus are applicable
at the database level and do not pay much attention to the specifics of the underlying storage
devices. They assume a disk drive is a linear storage space with constant (average) access time
per block. Recently, researchers focused on the lower level of the storage system in an attempt to
improve performance of multidimensional queries [10, 19, 22, 23, 29]. Part of the work revisits the
simple disk abstraction and proposes to expand the storage interfaces so that the applications can
be more intelligent. Schindler et al. [19, 21] explored aligning of the access patterns to the disk
drive track-boundary to get rid of the rotational latency. Gorbatenko et al.  and Schindler et
al.  propose a secondary dimension on disks, which has been utilized to create a more flexible
database page layout  for two-dimensional tables. Others have studies the opportunities of
building two dimensional structures to support database applications with new alternative storage
devices, such as MEMS-based storage devices [23, 29]. The work described in this paper further
leverages the underlying characteristics of the storage device to efficiently map an arbitrary number
of dimensions on logical volumes comprised of modern disk drives.
Another body of related research focuses on how to decluster multidimensional datasets [1, 3,
4, 8, 13, 16, 17] to optimize spatial access methods [12, 25] with multiple disks and improve the
throughput parallel system. While all the existing declustering schemes assume that disk drives are
purely one-dimensional, making it impossible to find a total ordering of blocks that preserves spatial
locality for accesses to multidimensional datasets, our paper challenges this assumption. We
exploit two important observations of modern disk drive technologies and show how to map such
datasets for efficient access for all dimensions across disks in a multi-disk storage array. Naturally,
much of the discussion focuses on how to organize multidimensional data so that spatial locality
on a single disk is preserved. We discuss alternative ways for mapping data across multi-disk volumes
and evaluate one such approach. However, exploring the tradeoffs of different declustering
2-techniques and their effects on the performance of specific spatial queries is beyond the scope of
3 Breaking one-dimensionality assumptions
Despite the established view that disk drives can efficiently access only one-dimensional data
mapped to sequential logical blocks, modern disk drives allow for efficient access in more than
one dimension. This fundamental change is based on two observations of technology in modern
1. Short seeks are dominated by the time the head needs to settle on a new track.
2. It is possible to identify and thus access blocks with no rotational latency after a seek.
By combining these two observations, it is possible to construct a mapping and access patterns
for efficient access to multidimensional data sets with the current abstractions of storage systems as
a linear address space of fixed-size blocks. This section details the technological underpinnings that
make this mapping possible. It presents an algorithm for selecting the proper blocks for mapping
and efficient access to multidimensional datasets. Finally, it describes an API abstraction that
allows database storage managers to take advantage of such access without revealing low-level
disk details or breaking current storage interfaces with their established abstractions.
3.1 Adjacent blocks
It is well understood that disk drives are optimized for access to the next sequential disk block
on the same track and that the time to access any other disk block is a function of that block’s
distance from the current head position. However, recent trends in track density and mechanical
performance change this assumption. They define a set of blocks with respect to a starting location,
here termed adjacent, whose cardinality is D. All of these D adjacent blocks can be accessed with
constant amount of positioning time and without any rotational latency even though they are not
equidistant from that starting location.
In addition to incurring constant positioning cost for accessing non-equidistant adjacent blocks,
such access does not incur any rotational latency and hence results in efficient access. The cost is
equal to the constant overhead of moving the disk head to any of the destination block in the set.
When these adjacent are properly identified and exposed to applications, they can ensure efficient
access to multidimensional data sets. To understand why disk drives can efficiently access adjacent
blocks and what determines the value of D, it is necessary to describe in some detail certain disk
drive characteristics and their trends, which is the topic of the next section.
3.2 Disk technology trends defining adjacent blocks
Figure 1(a) shows a conceptual view of seek time as a function of cylinder distance for modern
disks. In the past the seek time for short distances between one and, say, L cylinders was approximately
a square root of the seek distance . In more recent disks, seek time is almost constant
for distances of up to C cylinders, while it increases for distances between C and L cylinders as
Seek Profile of a Modern Disk Drive
Seek distance [cylinders]
Seek time [ms]
(a) Conceptual seek profile.
0 10 20 30 40 50 60 70 80
Seek time [ms]
Seek distance [cylinders]
Maxtor Atlas 10k III seek time function, first 80 cylinders
(b) Actual seek profile of a modern
Figure 1: Typical seek profile of modern disk drives. The seek profile consists of three distinct regions. For
cylinder distances less that C, the seek time is constant, for distances between C and L, the seek time is approximately
a square root of the seek distance, and for distances greater than L, the seek time is a linear function of seek distance
in cylinders. To illustrate the trends more clearly, the X-axis in Figure 1(a) is not drawn to scale.
Conservatism D Settle time
0 136 1.10 ms
10 180 1.25 ms
20 380 1.45 ms
Table 1: Adding extra conservatism to the base skew of 51 for the Atlas 10k III disk.
before. Thus, seeks of up to C cylinders are dominated by the time it takes a disk head to settle
on the new track. Figure 1(b) shows an example of a seek profile of a disk introduced in 2002 for
which C = 17 and settle time is slightly less than 1 ms.
While settle time has always been a factor in positioning disk heads, the dramatic increase in
areal density over the last decade has brought it to the fore, as shown in Figure 2. At lower track
densities (e.g., in disks introduced before 2000), only a single cylinder can be reached within the
settle period. However, with the large increase in track density since 2000, up to C can now be
The growth of track density has been one of the strongest trends in disk drive technology over
the past decade, while settle time has decreased very little , as shown in Figure 2 for two families
of enterprise-class 10,000 RPM disks from two manufacturers. With such trends, more cylinders
can be accessed as track density continues to grow while settle time improves very little.
Given that each cylinder consists of R tracks, equal to the number of recording surfaces, we
define the disk drive’s depth, D, as the total number of tracks that are accessible within the settle
time, where D = RC. We refer to these D tracks as being adjacent. Each of these tracks then
contains one adjacent block that can be accessed from a starting block within the constant settle
time, without any additional rotational latency. The Maxtor Atlas 10k III disk from our example in
Figure 1(b) hasC =17, and up to 8 surfaces for a total capacity of 73 GB. Thus, it has 136 adjacent
blocks according to our definition and D = 136.
Figure 3 shows a notational drawing of the layout of adjacent blocks on disk. For a given
starting block, there are D adjacent disk blocks, one in each of the D adjacent tracks. During the
settle time, the disk rotates by a fixed number of degrees, W, determined by the ratio of the settle
Atlas 10k II
Atlas 10k IV
Seagate Cheetah 18LP
Seagate Hawk 1LP
One-Cylinder Seek Time [s]
Maxtor Atlas 10k III
Maxtor Settle Time
Figure 2: Disk trends for 10,000 RPM disks. Seagate introduced the Cheetah disk family in 1997 and Quantum/
Maxtor introduced its line of Atlas 10k disks in 1999. Notice the dramatic increase in track density, measured
in Tracks Per Inch (TPI), since 2000. The most recent disks introduced in 2004 Cheetah 10k.7 and Atlas 10k V (not
shown in the graph) have densities of 105,000 and 102,000 TPI respectively, and settle times 0.1 ms shorter than
their previous counterparts.
Adjacent disk block
Figure 3: An example of adjacent tracks.
time to the rotational period of the disk. For example, with settle time of 1 ms and the rotational
period of 6 ms (i.e., for a 10,000 RPM disk), W = 60 degrees (1=6360). Therefore adjacent
blocks are always physically offset from the starting block by the same amount.
As settle time is not deterministic (i.e., due to external vibrations, thermal expansion etc.), it
is useful to add some extra conservatism to W to avoid rotational misses and suffer full revolution
delay. Adding conservatism to the value of W increases the number of tracks, D, that can be
accessed within the settle time at the cost of added rotational latency, as shown in Table 1 for the
Atlas 10k III disk.
Accessing successive adjacent disk blocks enables semi-sequential disk access [10, 22], which
is the second most efficient disk access method after pure sequential access. The delay between
each access is equal to a disk head settle time, which is the minimum mechanical positioning delay
the disk can offer. However, the semi-sequential access in previous work utilizes only one of the
adjacent blocks for efficiently accessing 2-D data structures. This work shows how to use up to D
adjacent blocks to improve performance for multidimensional datasets.
= Find an LBN adjacent to lbn and step tracks away =
L := GETADJACENT(lbn; step) :
= Find the required skew of target LBN =
target skew := (GETSKEW(lbn) +W) mod 360
= Find the first LBN in target track =
base lbn := lbn + (step T)
flow; highg := GETTRACKBOUNDARIES(base lbn)
= Find the minimum skew of target track =
low skew := GETSKEW(low)
= Find the offset of target LBN from the start of target track =
if (target skew > low skew) then
offset skew := target skewlow skew
offset skew := target skewlow skew+360
= Convert the offset skew into LBNs =
offset lbns := (offset skew=360) T
= Find the physical skew of lbn, measured in degrees =
A := GETSKEW(lbn)
= Find the boundaries of the track containing lbn =
fL; Hg := GETTRACKBOUNDARIES(lbn)
T: The track length
W: Skew to add between adjacent blocks, measured in degrees
Figure 4: Algorithm for the GETADJACENT function.
3.3 An algorithm for identifying adjacent blocks
The current disk interface based on the abstraction of a storage device as a linear array of fixedsized
blocks does not expose adjacent blocks, even though such information is readily available
inside modern disk drives. In particular, the firmware functions for mapping logical blocks to
physical sectors use the skew W and the logic of functions for scheduling requests can identify
blocks that can be accessed without incurring any rotational latency.
This section describes an algorithm for identifying the logical block numbers (LBN) of adjacent
disk blocks for a given block in the absence of the proper storage interface functions. The
algorithm uses a detailed model of the low-level disk layout taken from a storage system simulator
called DiskSim . The parameters can be extracted from SCSI disks by previously published
methods [20, 28]. The algorithm uses two functions that abstract the disk-specific details of the
disk model: GETSKEW(lbn), which returns the physical angle between the physical location of an
LBN on the disk and a “zero” position, and GETTRACKBOUNDARIES(lbn), which returns the first
and the last LBN at the ends of the track containing lbn.
Figure 5: Adjacency graph forLBN 0 of Atlas 10k III. Only two levels adjacent blocks are shown. The LBNs are
shown inside nodes.
For convenience, the algorithm also defines two parameters. First, the parameter T is the number
of disk blocks per track and can be found by calling GETTRACKBOUNDARIES, and subtracting
the low LBN from the high LBN. Second, the parameter W defines the angle between a starting
block and its adjacent blocks, as described in Section 3.2. This angle can be found by calling the
GETSKEW function twice for two consecutive LBNs mapped to two different tracks and computing
the difference; disks skew the mapping of LBNs on consecutive tracks byW degrees to account for
settle time and to optimize sequential access performance.
The GETADJACENT algorithm shown in Figure 4 takes as input a starting LBN (lbn) and finds
the adjacent LBN that is W degrees ahead and step tracks away. Every disk block has an adjacent
block within the D closest tracks, so the entire set of adjacent blocks is found by calling
GETADJACENT for increasing values of step from 1 to D.
3.4 Hiding low-level details from software
While this paper advocates exposing to applications adjacent LBNs, it is unrealistic to burden
application developers with such low-level details as settle time, physical skews, and data layout
to implement the algorithm described above. The application need not know the reasons why
disk blocks are adjacent, it just needs to be able to identify them through a GETADJACENT call to
a software library or logical volume manager interface that encapsulates the required low-level
parameters. This software layer can exist on the host as a device driver or within a disk array. It
would either receive the necessary parameters from the disk manufacturer or extract them from the
disk drives when the logical volume is initially created. Our prototype implementation of a logical
volume manager described in Section 5 takes the latter approach when creating logical volumes
from one or more disks.
A useful way to analyze the adjacency relationships between disk blocks is by constructing
adjacency graphs, such as that shown in Figure 5. The graph nodes represent disk blocks and the
edges connect blocks that are adjacent. The graph in the figure shows two levels of adjacency: the
root node is the starting block, the nodes in the intermediate level are adjacent to that block, and
the nodes in the bottom level are adjacent to the blocks in the intermediate level. Note that adjacent
sets of adjacent blocks (i.e., those at the bottom level of the graph) overlap. This overlap limits the
overall dimensionality of the disk space to two, and is discussed further in Section 4. For brevity,
the graph shows only the first 6 adjacent blocks (i.e., D = 6), even though D is considerably larger.
Thus, higher-dimensional data can be efficiently folded into the 1-D abstraction of the disk.
Comparison of access times
Maxtor Atlas 10k III Seagate Cheetah 36ES
Figure 6: Quantifying access times. This graph compares the access times to blocks located within C cylinders.
For both disks, the average rotational latency is 3 ms. For the Atlas 10k III disk, C = 17 and seek time within C ranges
from 0.8 ms to 1.2 ms. For the Cheetah 36ES disk, C=12 and seek time ranges from 0.7 ms to 1.2 ms.
3.5 Quantifying access efficiency
A key feature of adjacent blocks is that, by definition, they can be accessed immediately after
the disk head settles. To quantify the benefits of such access, suppose an application is accessing
d non-contiguous blocks that map within D tracks. Without explicit knowledge of adjacency,
accessing each pair of such blocks will incur, on average, rotational latency of half a revolution,
in addition to the seek time equivalent to the settle time. If these blocks are specifically chosen to
be adjacent, then the rotational latency is eliminated and the access to the d blocks is much more
A system taking advantage of accesses to adjacent blocks outperforms traditional systems.
As shown in Figure 6, such system, labeled Adjacent, outperforms a traditional system, labeled
Normal, by a factor of 4 thanks to the elimination of all rotational latency when accessing blocks
within C cylinders. Additionally, the access time for the Normal case varies considerably due to
variable rotational latency, while the access time variability is much smaller for the Adjacent case;
it entirely due to the difference in seek time within the C cylinders, as depicted by the error bars.
4 Mapping multidimensional data onto disks
Given the underpinnings for efficient access to adjacent blocks, we now discuss how to create a
general model for mapping N-dimensional datasets to these adjacent blocks. We first discuss the
intrinsic dimension of the adjacency model. Then, we show on examples of mapping 2-D, 3-D and
4-D datasets its basic ideas, and finally present a general algorithm for mapping N-dimensional
datasets to adjacent blocks.
4.1 Intrinsic dimension of adjacency model
At a first glance, one might assume that a disk using the adjacency model with a depth of D can have
D+1 dimensions. It turns out to be more complicated. To obtain an insight to the dimensionality
of the adjacency model, we apply the power law method  to calculate its intrinsic dimension.
The power law formula N(r) = ra describes the internal structure of a dataset. Here, r denotes
the steps from a point and N(r) counts the average number of neighbors within r. The exponent a
is referred to as intrinsic or fractal dimension and usually changes with different scales of r.
Figure 7: Intrinsic dimensionality of the adjacency model. Depth, D is 64, track length, T, is 686, and the skew,
W, is 60.
The intrinsic dimension is very helpful to understand complex datasets where traditional distributions
are not applicable. It indicates how fast the dataset grows when increasing the range. For
example, lines have intrinsic dimensions of 1 because the number of neighbors increases linearly
with r. In practice, N(r) and r are often plotted in log-log scales, so the slope of the curve is the
“intrinsic dimension”. We apply the fractal analytical method to the adjacency model to get an
insight of its dimensionality.
In the following discussion, we count both the adjacent blocks (as shown in Figure 3) and the
consecutive block on the same track as the neighbors of a given block. Varying the value of r from
1 to the track length, we plot the log-scale curve of N(r) vs. r in Figure 7. The depth D of the
adjacency model used in Figure 7 is 64.
Figure 7 shows that the slope decreases a the range r increases. We can split the curve into
four regions based on the changing trends of the slope a. The initial region starts from r = 1, and
ends at r = 2 with a slope of around 6. Next, the slope drops abruptly to 3 starting from r = 2,
marking the beginning of the first plateau. The dimension of 3 lasts until r = 9. After that, it
changes gradually into the second plateau with a slope of 2 until r = 95. The third region starts
from r = 95, where the slope decreases gradually from 2 to 1 until it reaches the last region.
The changes of the slope, that is the intrinsic dimension of the adjacency model, are due to
the complex and changing overlapping patterns among different adjacency sets at different r. The
larger the r, the higher the degree of overlap, and the smaller the number of new neighbors brought
into the neighbor set at each step. Therefore, the slope decreases with the increase of the range.
Without discussing more details about the intrinsic dimension of the adjacency model, we observe
that the adjacency model has an inherent limitation on the dimensions it can support. To get around
the restriction in practice, we rely on the fact that to build an N-dimensional space, one block does
not need more than N neighbors. For instance, to create a 3-D space, each block only needs three
neighbors, even though the value of D is very large, as discussed in Section 3. If we choose the
neighbors carefully, we can build a mapping for N-dimensional datasets with large N, which is the
topic of the next section.
T disk track length
D depth of the adjacency model
N dimensions of the dataset
Dimi notations of the N dimensions
Si length of Dimi
Ki length of Dimi in the basic cube
s time to access next sequential block
a time to access any adjacent block
s-neighbor sequential block on the same track
a-neighbor adjacent block on another track
i-th a-neighbor i-th adjacent block
Table 2: Notation definitions. For Dim, S, and K, 0 i N 1.
4.2 Mapping examples
To map an N-dimensional dataset onto disks, we first impose an N-dimensional grid onto the
dataset. Each cell in the grid will be assigned to an N-dimensional coordinate mapped to a single
disk block with its own LBN address and whose size is typically 512 bytes. In the rest of the paper,
a point refers to a cell, even though a single cell can actually contain multiple points of the original
discrete N-dimensional space.
We next illustrate MultiMap through three concrete examples for 2-D, 3-D, and 4-D datasets,
followed by a formal definition and a general algorithm. The notation used here is listed in Table 2.
In the following examples, we assume that T = 8, D = 4, and the disk blocks start from LBN 0. As
you will notice, the MultiMap algorithm need not know or use the value of skew W. This value is
abstracted away by the GETADJACENT function, which in turn uses the GETSKEW function, which in
turn builds upon the abstraction of the disk firmware mappings of logical block (LBNs) to physical
sectors, which usesW.
4.2.1 Example of 2-D mapping
Figure 8 shows how a 2-D rectangle is mapped to the disk. Each row in the sketch represents a
disk track and each cell corresponds to a disk block whose LBN is the number inside the box. In
this example, we have three tracks, each having 8 blocks. For simplicity, we choose a 2-D space
of the size S0 = T = 8 for Dim0 and S1 = 3 for Dim1.
The first dimension (Dim0) is mapped sequentially to consecutive LBNs mapped to the same
track, which ensures that accesses along Dim0 get the performance of streaming bandwidth. The
second dimension (Dim1) is mapped to the first a-neighbors (that is, the first adjacent blocks) For
example, LBN 8 is the first adjacent block of LBN 0, and LBN 16 is the first adjacent block of LBN
8. Although not as efficient as sequential access, he semi-sequential disk access along Dim1 is still
much more efficient than the accesses for the other schemes.
Now the rectangle is mapped to 3 contiguous tracks. The spatial localities on Dim0 and Dim1
are preserved on the disk. Note that in the 2-D mapping, we only leverage sequential blocks and the first a-neighbors.
Figure 9: Mapping 3-D dataset to disks. Each surface is a 2-D structure as in Figure 8. The third dimension is
mapped to the sequences of 4th-a neighbors.
4.2.2 Example of 3-D mapping
In this example, we use a 3-D dataset with the following parameters: S0 = T = 8, S1 = 4, and
S2 = 3. As shown in Figure 9, Dim0 is still mapped to the track direction as in the 2-D case. To
map the other two dimensions, we use (S2S1) adjacent tracks, partition them into S2 pieces each
occupying S1 consecutive tracks (one piece corresponds to one layer in Figure 9). For each piece,
we apply the same method from the 2-D example for mapping a S0S1 surface. Then we “fold”
these S2 pieces along the direction of Dim2 to build the third dimension.
A crucial step here is that the points along the third dimension are mapped to a sequence of
the S1-th a-neighbors. For instance, LBN 32 is the 4th a-neighbor of LBN 0, and LBN 64 is the
4th a-neighbor of LBN 32. Since D = 4, access along Dim2 fetches adjacent blocks. So it has
the same performance as the access along Dim1. Therefore, the spacial locality of Dim2 is also
preserved (the locality of Dim1 is guaranteed by the 2-D mapping). Note that that the size of S1
is restricted by the value of D; to guarantee efficient along Dim2 as well. We will discuss the case
where S1 > D in the general mapping algorithm in the next section. The resulting 3-D mapping
occupies (S1S2) contiguous tracks.
4.2.3 Example of 4-D mapping
In the 4-D example, shown in Figure 10, we use a dataset of size (S0 = T = 8, S1 = 2, S2 = 2,
S3 = 3). Note that the value of S1 is different from the 3-D example. As before, Dim0 is mapped
along the track. To map the other three dimensions, we use (S3S2S1) consecutive tracks and
partition them into S3 pieces each mapped to (S2S1) consecutive tracks. The same 3-D mapping
Figure 10: Mapping 4-d dataset to disks. Fold K3 3-D structures as in Figure 9 to build the fourth dimension. The
fourth dimension is mapped to the sequences of adjacent blocks with a step of K1K2.
method is applied on each of the pieces.
We fold again the S3 pieces to get the fourth dimension Dim3. The points along Dim3 are
therefore mapped to a sequence of (S1 S2)-th a-neighbors. This time LBN 32, the 4th adjacent
block of LBN 0, becomes the “next” block to LBN 0 along Dim3. Access along Dim3 involves
fetching adjacent blocks, so it has same performance as fetching blocks along Dim1 and Dim2.
Notice that in the 4-D example, S1 and S2 must satisfy the restriction: (S1S2) D.
4.3 Definition of the mapping problem
Following the discussion in the previous section, it becomes clear that mapping to the space of N
dimensions is an iterative extension of the problem of mapping N 1 dimensions. In this section,
we give the formal definition of the mapping problem and a general algorithm, MultiMap, to map
N-dimensional datasets and discuss its limitations.
The mapping problem we are trying to solve is defined as follows: given D adjacent blocks
and track length, T, map an N-dimensional dataset to the disk, so that the spatial locality of the
dataset can be preserved. By preserving locality, we mean that neighbors of one point P in the
original space will be stored in the blocks that are adjacent to the block storing P. In the following
discussion, we assume that the size of the dataset is smaller than the disk capacity and relax it Section
4.5, when we describe how to map datasets that are use logical volumes built out of multiple
Before describing the mapping of any N-dimensional grid to one or more disks, we first discuss
how to map a single basic cube to a single disk. Basic cube is a cube in the N-dimensional space
with a size of ÕN1
i=0 Ki, satisfies the following requirements:
start lbn = the first LBN of the basic cube
= the LBN storing point (0; : : : ;0)
Figure 11: Mapping a point in space to an LBN.
size, subject to the disk capacity. The last equation sets a limit on the lengths of K1 to KN2. The
volume of the (N 2)-dimensional space, that is ÕN2
i=1 Ki, should be less than D, otherwise the
locality of the last dimension cannot be preserved because accessing the consecutive points along
the last dimension cannot be done within a.
4.4 Mapping the basic cube
We map the basic cube in the following way: Dim0 is mapped to the track direction; Dim1
is mapped to the sequence of the first a-neighbors; . . . ; Dimi+1(1 i N 2) is mapped to a
sequence of (K1K2 : : :Ki)-th a-neighbors.
The above procedure is generalized in the MultiMap algorithm shown in Figure 11. The inputs
of MAP are the coordinates of a point in the basic cube, the output is the LBN to store that point.
MAP starts from the point (0;0; : : : ;0). Each inner iteration proceeds one step along Dimi, which on
a disk corresponds to jump over (K1K2 : : :Ki1) adjacent blocks. Therefore each iteration of
the outer loop goes from point (x; : : : ; x[i1];0; : : : ;0) to point (x; : : : ; x[i1]; x[i];0; : : : ;0).
The mapping algorithm has three important properties. First, Dim0 is mapped to the disk track
so that accesses on this dimension enjoy the best performance of the sequential scan. Second, all
the other dimensions are mapped to a sequence of adjacent blocks with different steps. Any two
neighboring blocks on all dimensions are mapped to adjacent blocks at most D tracks away. (see
Equation 3). Requesting these (non-contiguous) blocks results in semi-sequential accesses that
are much more efficient than accessing blocks mapped within D tracks at random. And third, the
mapping algorithm preserves the spatial locality within the basic cube because neighbors in the
cube are mapped to neighboring blocks (s-neighbor and a-neighbor) within D tracks.
We require that Ki 2 (0 i N 1), otherwise the basic cube deteriorates to a cube in a
space with less than N dimensions. Usually, we consider the basic cubes with equal length along
dimensions Dim1; : : : ;DimN2, that is K1 =K2 =: : :=KN2 =K. With these assumptions, derived
from Equation 3, we have N b2+logK Dc (K 2) (4)
13 Nmax = b2+log2Dc (5)
For modern disks, D is typically on the order of hundreds, allowing mapping for space of more
than 10 dimensions.
4.5 Mapping large dataset
The algorithm described above maps a basic cube to within D tracks of a single disk and determines
the proper values for K1 to KN2. As Equation 3 indicates, D is the maximum volume of the
(N 2)-D cube. The track length, T, restricts the size of the basic cube along the first dimension.
Finally, the total number of tracks, Rmax, on a disk set an upper bound for KN1, according to the
If the length of the dataset’s, and hence basic cube’s, Dim0 is less than T, we simply pack as many
basic cubes next to each other along the track as possible. Naturally, if at all possible, it is desirable
to select a dimension whose length is larger than T and set it as Dim0. When jDim0j mod T = A
and A = 0, we have total coverage and no internal fragmentation of our LBN space. When A > 0,
there will be at most AN unallocated LBNs due to internal fragmentation when the entire dataset
(i.e., all basic cubes) is allocated.
The basic cube defined in Section 4.3 serves as an allocation unit when we map larger datasets
to the disks. If the original space is larger than the basic cube, we partition it into basic cubes to
get a new N-dimensional cube with a reduced size of
To map each basic cube to the disk, we can apply any traditional algorithm used to linearize Ndimensional
datasets. The only difference is the size of the “point” is many more (non-contiguous)
LBNs instead of one disk block.
Given the mapping of basic cubes, spatial locality for points in different basic cubes is not
preserved, and the cost of a jumps between basic cubes will be a seek that is slightly larger than
settle time with some rotational latency. However, when requests for the destination basic cube
are issued together, this rotational latency is still much smaller than 1/2 revolution; the firmware
disk scheduler can reorder these requests to minimize overall rotational latency. Despite these
discontinuities, MultiMap still outperforms existing solutions, as shown in Section 5.
When using multiple disks, we can apply existing declustering strategies to distribute the basic
cubes of the original dataset across the disks comprising a logical volume just as traditional linear
disk models decluster stripe units across multiple disks. The key difference lies in the way how
multidimensional data is organized on a single disk. The MultiMap scheme thus works nicely
with the existing declustering methods and enjoys an increase in throughput brought by parallel
I/O operations. In the reminder of our discussion, we focus on the performance of MultiMap on
a single disk, with the understanding that multiple disks can deliver I/O throughput scalable with
the number of disks. The access latency for each disk, however, remains the same regardless of the
number of disks.
We evaluate MultiMap’s performance using a prototype implementation that runs queries against
3-D and 4-D datasets stored on real disks. In addition, we use an analytical model to estimate
MultiMap’s performance for 8-D datasets.
We compare MultiMap to Naive and Hilbert schemes. Naive linearizes an N-dimensional
space along a chosen dimension, called major order. In all experiments, we choose Dim0 as the
major order. Hilbert mapping orders the N-dimensional points according to their Hilbert curve
5.1 Experimental setup
The experiments are conducted on a two-way 1.7 GHz Pentium 4 Xeon workstation running Red-
Hat 8.1 distribution with Linux kernel 2.4.24. The machine has 1024 MB of main memory and is
equipped with one Adaptec Ultra160 Wide SCSI adapter connecting two 36.7 GB disks: Seagate
Cheetah 36ES disk (ST336706LC) and Maxtor Atlas 10k III disk (KU036J4). The disks were introduced
in late 2001 and early 2002, respectively and both have two platters (four surfaces, R=4).
The Cheetah disk maps logical blocks across 26,302 cylinders with track sizes between 736–574
sectors, while the Atlas disk uses 31,002 cylinders with track sizes between 686–396 sectors.
Our prototype system consists of two software components running on the same host machine:
a logical volume manager (LVM) and a database storage manager. In a production system,
the LVM would likely reside inside a storage array system separate from the host machine. The
LVM exports a single logical volume mapped across multiple disks and identifies adjacent blocks
using the algorithm described in Section 3.3. It uses a DiskSim disk model  whose parameters
we obtained from our disks with an on-line extraction tool . The database storage manager
maps multidimensional datasets by utilizing the GETADJACENT and GETTRACKBOUNDARIES functions
provided by the LVM. Based on the query type, it issues appropriate I/Os to the LVM, which
then breaks these I/Os into proper requests to individual disks.
The datasets used for our experiments are stored on multiple disks in our LVM. Akin to commercial
disk arrays, the LVM uses disks of the same type and utilizes only a part (slice) of the
disk’s total space . The slices in our experiments are slightly less than half of the total disk
capacity and span one or more zones (cylinders with equal number of sectors per track).
The LVM generates requests to all the disks during our experiments, but we report performance
results from a single disk. The reason is that we examine average I/O response times,
which depend only on the characteristics of a single disk drive. Using multiple drives improves the
overall throughput of our experiments, but does not affect the relative performance of the mappings
we are comparing.
5.2 Query workloads
Our experimental evaluation compares two classes of queries for 3-D and 4-D datasets. Beam
queries are one-dimensional queries retrieving data points along the lines parallel to the dimensions.
A beam query along Dimk can be described in SQL-like syntax as follows:
SELECT all points X (x0; : : : ; xN1) FROM dataset WHERE xi = qi, AND 0 i N 1 AND i 6= k
Dim0 Dim1 Dim2
I/O time per cell [ms]
Maxtor Atlas 10k III
Dim0 Dim1 Dim2
Seagate Cheetah 36ES
(a) Beam queries.
1% 2% 3%
Total I/O time [ms]
Maxtor Atlas 10k III
1% 2% 3%
Seagate Cheetah 36ES
(b) Range queries.
Figure 12: Performance of the 3-D dataset.
The (x0; : : : ; xN1) are the coordinates of point X in the N-dimensional space. And the query
predicate says all (x0; : : : ; xN1) coordinates are fixed except for xk.
Range queries, called p%-length cube queries, fetch an N-dimensional cube with an edge
length equal to the p% of the dataset’s edge length. With (q0; : : : ;qN1), (Q0; : : : ;QN1) as its
lower-left and upper-right corners, a cube query has the following expression:
SELECT all points X (x0; : : : ; xN1) FROM dataset WHERE xi BETWEEN qi AND Qi AND (0 i N 1)
5.3 3-D dataset
For these experiments, we use a dataset with 102410241024 cells. We partition the space into
chunks of at most 259259259 cells that fit on a single disk. For both disks, MultiMap uses
D = 128 and conservatism of 30.
Beam queries. The results for beam queries are presented in Figure 12(a). We run beam queries
along all three dimensions, Dim1, Dim2, and Dim3. The graphs show the average I/O time per cell
(disk block). The values are averages over 15 runs and the standard deviation is less that 1% of the
reported times. Each run selects a random value between 0 and 258 for the two fixed dimensions
and fetches all points (0 to 258) along the remaining dimension.
As expected, Naive performs best along Dim0, the major order, as it utilizes efficient sequential
disk accesses with average time of 0.035 ms per cell. However, accesses along the non-major
orders take much longer as neighboring cells along Dim1 and Dim2 are stored respectively 259 and
67081 (259259) blocks apart. Fetching each cell along Dim1 experiences mostly just rotational
latency; two consecutive blocks are often on the same track. Fetching cells along Dim2 results
in a short seek (1.3 ms for both disks) followed by rotational latency. Even though the jumps for
Dim2 are evenly spaced on both disks, the overall performance of the Dim2 on the Cheetah disk is
better. The rotational offset of two consecutive LBNs on the Atlas disk results in rotational latency
of 3.8 ms on top of the 1.3 ms settle time, while it is only 2.1 ms for the Cheetah disk.
True to its goals, the Hilbert scheme achieves balanced performance across all dimensions.
It sacrifices the performance of sequential accesses that Naive can achieve for Dim0, resulting in
2.0 ms per cell vs. 0.035 ms for Naive (almost 57 worse). Hilbert outperforms Naive for the
other two dimensions, achieving 22%–136% better performance for the two different disks.
The MultiMap scheme delivers the best performance for all dimensions. It matches the streaming
performance of Naive along Dim0 despite paying a small penalty when jumping from one basic
Dim0 Dim1 Dim2 Dim3
I/O time per cell [ms]
Maxtor Atlas 10k III
Dim0 Dim1 Dim2 Dim3
Seagate Cheetah 36ES
(a) Beam queries.
64 104 124
Total I/O time [ms]
Maxtor Atlas 10k III
64 104 124
Seagate Cheetah 36ES
(b) Range queries.
Figure 13: Performance of the 4-D earthquake dataset.
cube to the next one. More importantly, MultiMap outperforms Hilbert for Dim1 and Dim2 by
25%—35% and Naive by 62%–214% for the two disks. Finally, notice that MultiMap achieves
almost identical performance on both disks unlike Hilbert and Naive. That is because these disks
have comparable settle times, which affect the performance of accessing adjacent blocks for Dim1
and Dim2. We observed that by using a smaller conservatism value the average I/O times for the
Atlas disk is lowered by another 2–10% (not shown in the graphs).
Range queries. The first set of three bars, labeled 1 per, in Figure 12(b) shows the performance
of 1%-length cube queries expressed as their total runtime. As before, the performance of each
scheme follows the trends observed for the beam queries. MultiMap improves the query performance
by 37% and 11% respectively compared to Naive and Hilbert.
Both MultiMap and Hilbert outperform Naive as it can not employ sequential access for range
queries. MultiMap outperforms Hilbert as it must fetch some cells from physically distant disk
blocks, although they are geographically close. These jumps make Hilbert less efficient compared
to MultiMap’s semi-sequential accesses.
To examine the sensitivity of the cube query size, we also run 2%-length and 3%-length cube
queries, whose results are presented in the second and third bar sets in Figure 12(b). The trends
are similar, with MultiMap outperforming Hilbert and Naive. The total run time increases because
each query fetches more data.
5.4 4-D earthquake simulation dataset
To evaluate performance for 4-D data sets, we use 4-D earthquake simulation data. In earthquake
simulations , the logical dataset is a 3-D region of the earth, modeled as a set of discrete points,
organized in a 3-D grid. The simulation computes the motion of the ground at each point, for a
number of discrete time steps. The 4-D simulation output contains a set of 3-D grids, one for each
The dataset we evaluate here models earthquake activity in a 14 km deep slice of earth of a
3838 km area in the vicinity of Los Angeles. The grid contains approximately one node per
200 m (unevenly distributed) and 2000 time steps. Given this dataset size and packing of up to
10 nodes per cell, this grid translates into a 4-D space of 2000646464 cells for a total size
of 250 GB of data. We choose time as the major order to the Naive and the MultiMap schemes.
Similar to the 3-D dataset, we partition the space into basic cubes of 530323232 cells to
fit in a single disk. We query the 4-D dataset in two ways: Time-varying queries, or beam queries,
Query Measured Calculated Measured Calculated
Dim0 0.03 0.03 0% 0.04 0.03 13%
Dim1 2.7 2.7 0% 1.7 1.5 8%
Dim2 5.2 4.3 17% 1.5 1.5 0%
1% 294 291 1% 205 168 18%
2% 1086 1156 6% 722 723 0%
3% 2485 2632 5% 1626 1758 8%
Dim0 0.02 0.02 0% 0.02 0.02 0%
Dim1 5.5 5.0 9% 2.1 1.9 9%
Dim2 2.2 4.0 82% 2.1 1.9 9%
Dim3 4.86 5.1 5% 1.8 1.6 11%
64 1142 1065 7% 513 436 15%
104 5509 5016 9% 1996 2150 8%
124 8905 8714 2% 3590 3960 9%
Table 3: Comparison of predicted and measured I/O costs.
retrieve the velocity of a particular point for multiple time-steps. Space-varying queries, or range
queries, retrieve the velocity for one time step, of all the nodes in a specific region.
The results, presented in Figure 13, exhibit the same trends as the 3-D experiments. The
MultiMap model again achieves the best performance for all beam and range queries. The range
queries, In Figure 13(a), the unusually good performance of Naive on Dim2 is due to a particularly
fortunate mapping that results in strided accesses that do not incur any rotational latency.
The ratio of strides to track sizes also explains the counterintuitive trend of the Naive scheme
performance on the Cheetah disk where Dim3 outperforms Dim2, and Dim2 outperforms Dim1.
The range queries, shown in Figure 13(b), perform on both disks as expected from the 3-D case.
In summary, MultiMap is efficient for processing queries against spatio-temporal datasets, such as
this earthquake simulation output, and is the only scheme that can combine streaming performance
for time-varying accesses with efficient spatial access.
5.5 Performance for higher dimensional space
We developed an analytical model that predicts the performance for the Naive and MultiMap
schemes for higher dimensional space. The model predicts the per-cell access time for any spatial
query in an N-dimensional cube based on disk characteristics and dataset dimensions.
Table 3 presents a comparison of the model’s predictions with the measurements from the
experiments described previously to establish the model’s validity. As shown, the prediction error
is for most cases less than 8% and 5 out of 24 predicted values are within 20%. Details of the
model are discussed in Appendix A.
Having demonstrated the model’s high accuracy, we can use the model to predict the performance
trends for N-dimensional space with N > 4. Figure 14 shows the predicted performance
for beam queries with an 8-D dataset with each dimension being 12 cells. These results exhibit
the same trends as those for 3-D and 4-D, demonstrating that MultiMap is effective for highdimensional.
Dim0 Dim1 Dim2 Dim3 Dim4 Dim5 Dim6 Dim7
I/O time per cell [ms]
Estimated cost for 8-D cube
Figure 14: Predicted performance of beam queries in 8-D space.
The MultiMap mapping scheme for N-dimensional datasets leverages technological trends of modern
disk drives to preserve spatial locality and deliver streaming bandwidth for accesses along one
dimension and efficient semi-sequential accesses in the other dimensions. It does so without breaking
established storage interfaces or exposing disk specific details, thanks to the logical volume
abstraction offered by the GETADJACENT function.
While the MultiMap scheme works for arrays of disks by simply mapping successive basic
cubes to different disks, our future work will investigate the effects of fine-grain striping within the
basic cube to potentially improve performance of (very small) point or range queries. However,
even without fine-grain striping, all spatial queries across 3-D, 4-D, and 8-D datasets evaluated in
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A Analytical cost model
We developed an analytical model to estimate the I/O cost for any query against a multidimensional
dataset. The model calculates the expected cost in terms of total I/O time for the Naive and the
MultiMap mappings given disk parameters, the dimensions of the dataset and the size of the query.
Our model does not predict the total cost for the the Hilbert curve mapping. Although it is possible
to estimate the number of clusters, which are groups of consecutive LBNs for a given query ,
no work has been done on investigating the distribution of distances between clusters, which would
be required for an accurate analytical model. Given the complexity of the disk access analysis for
the Hilbert curve model, deriving such a model is beyond the scope of this work.
In the following discussion, we use the notations defined in Table 2 in Section 4.2. As with
traditional disk I/O cost models, we express the total I/O cost of a query as a sum of two parts: the
total positioning time overhead, Cpos, and the total data transfer time, Cxfer. The positioning time
overhead is the time needed to position the disk heads to the correct locations before data can be
accessed. The transfer time is the time to read data from the media. The model does not include
the cost of transporting the data to the host. This constant overhead is the same for both Naive and
MultiMap mappings and depends on the interconnect speed and the number of blocks returned,
here referred to as the volume of the query.
Suppose we have a range query of the size (q0;q1; : : : ;qn1), where qi is the size of Dimi, in
an N-dimensional cube of size (S0;S1; : : : ;Sn1). Cxfer is calculated by the volume of the query
multiplied by the transfer time per disk block, s. Cpos is a function of the number of movements of
the disk heads, referred to as jumps in the LBN sequence, and their corresponding distances. The
total I/O cost of a query is thus expressed as:
Ccost = Cpos+Cxfer
where Njmp is the number of jumps incurred during the query execution, d j is the distance the disk
heads move in the j-th jump, expressed as the number of tracks, and l j is the rotational latency
incurred during each jump after the disk heads finish seeking. The function Seek(d) determines
the seek time for a given distance d from a seek time profile such as the one shown in Figure 1(b).
Given the mapping of an N-dimensional cube for both the Naive and the MultiMap mappings,
the most efficient path to fetch all points is first through Dim0, then Dim1, etc. We define nonconsecutive
points for Dimi as two adjacent points whose coordinates for all but Dimi are the same
and that are mapped to non-contiguous LBNs. Therefore, a jump occurs when we fetch these nonconsecutive
points and the distance of the jump can be calculated as the difference of the LBNs
storing these two non-consecutive points. We build the cost model based on the above analysis.
We assume that in addition to a seek, each jump incurs a constant rotational latency equal to half a
revolution,RotLat, which is the average rotational latency when accessing a randomly chosen pair
of LBNs. Finally, we assume some initial cost for positioning disk heads prior to fetching the first
block of Dim0 and denote this overhead Cinit.
A.1 Analytical cost model for Naive mapping
The following equations calculate the query I/O cost for the Naive model.
Cxfer = s
Cpos = Cinit+
i=1 "(Seek(di)+RotLat) (qi1)
qj # (8)
di = 2
j=0 Sj q0+1åi1
j=1 (qj 1) Õj1
k=0 Sk T 3
Equation 7 calculates the total transfer time; the per-block transfer time, s, is multiplied by
the query volume, which is the product of all cells returned by the query. Equation 8 calculates
the positioning overhead. The term (qi1) Õn1
j=i+1 qj calculates the number of jumps along Dimi,
where i > 0. Equation 9 computes the distance of such jumps along Dimi. This distance is the
LBN difference of the two points (x0; : : : ; xi1; xi; xi+1; : : : ; xn1) and (x0+q0; : : : ; xi1+qi1; xi+
1; xi+1; : : : ; xn1) divided by the track length, T.
A.2 Analytical cost model for MultiMap mapping
The following equations calculate the query I/O cost for the MultiMap model.
Cxfer = s
Cpos = Cinit+(Seek(d0)+RotLat) Njmp(q0;K0;S0)+
i=1 "( a(qiNjmp(qi;Ki;Si))+(Seek(di)+RotLat)Njmp(qi;Ki;Si))
di = &Õn1
T ' i1
Njmp(qi;Ki;Si) = qi
LocS(qi;Ki;Si) = LocK(qi;Ki) Si
Ki+ Si mod Ki
LocK(qi;Ki) = Ki(qi mod (Ki+1))+1 (15)
The transfer cost (Equation 10) is the same as the transfer cost of the Naive model. Equation
11 calculates the positioning cost and consists of three terms. The first term, Cinit, is the initial
positioning cost, the second term is the positioning cost when reading along Dim0, and the third
term is the positioning cost when reading along Dimi, where i > 0. Since MultiMap partitions
the N-dimensional cube into smaller basic cubes, the sequential accesses along Dim0 are broken
when moving from one basic cube to the next. Thus, the term (Seek(d0)+RotLat)Njmp(q0;K0;S0)
accounts for the overhead of seek and rotational latency multiplied by the expected number of such
jumps (determined by Equation 13).
The third term of Equation 11 is similar to the second term in Equation 8 for the Naive
model, but includes two additional expressions for calculating the cost along Dimi. The expression
a (qiNjmp(qi;Ki;Si)) accounts for the cost of semi-sequential accesses to adjacent blocks when
retrieving the points along the i-th dimension. The expression (Seek(di)+RotLat)Njmp(qi;Ki;Si)
accounts for the cost of jumps from one basic cube to the next.
Equation 12 calculates the distance of each jump on Dimi. This distance depends on the
volume of each basic cube, Õn1
i=0 Ki, which determines the number of LBNs needed when mapping
one basic cube. The term Õi1
Kj m determines how many basic cubes are mapped between two
basic cubes containing two successive points (x0; : : : ; xi; : : : ; xn1) and (x0; : : : ; xi+Ki; : : : ; xn1).
Given query size qi, basic cube size Ki, and original space size Si, Njump(qi;Ki;Si) (Equation
13) calculates the expected number of jumps across basic cubes along that dimension. With
query size of qi, there are Siqi+1 possible starting locations in the original space. LocS of the
possible starting locations will cause lqi
Ki m1 jumps along Dimi while the remaining locations will
cause one more jump. To calculate the expected number of jumps, we simply add the probabilities
for each possible type of starting locations multiplied by the number of such jumps. The probability
of the minimal number of jumps is the ratio of LocS(qi;Ki;Si) and Si qi +1. Therefore, the
probability of lqi
Ki mjumps is 1 LocS(qi;Ki;Si)
To determine LocS (Equation 14), we first count the locations within a basic cube that will
cause the minimal number of jumps, denoted as LocK (Equation 15), and multiply it by the total
number of complete basic cubes that the possible starting locations can span, given by the expression
Ki k. Finally, we add the number of locations causing the minimal number of jumps
in the last (possibly incompletely mapped) basic cube, which is j Si mod Ki
A.3 Model parameters
For the results presented in Section A, we used parameters that correspond to the Atlas 10 k III disk.
We determined the values empirically from measurements of our disks. The average rotational
latency RotLat = 3 ms, Cinit = 8.3 ms, which is the average rotational latency plus the average
seek time, defined as the third of the total cylinder distance. Based on our measurements, we
set s = 0.015 ms and a = 1.5 ms with the conservatism of 30 (Table 1 in Section 3.2). We set
T = 686, which is equal to the number of sectors per track in the outer-most zone.
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