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Title

Effects on Performance and Energy Reductionby File Relocation basedon File-access Correlations

AuthorMasaru IRITANI, Haruo YOKOTA

Journal/Book nameProc. of the 1st Workshop on Enegy Data Management (EnDM2012),, ,Issue date2012, 3

DOI

http://dx.doi.org/10.1145/2320765.2320794

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Copyright (c)2012 Association for Computing Machinery

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The definitive version was published in Proceedings of the 2012, Proc.
of the 1st Workshop on Enegy Data Management (EnDM2012), , , ,
http://dx.doi.org/10.1145/2320765.2320794

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Effects on Performance and Energy Reduction
by File Relocation based on File-access Correlations
Masaru IRITANI

Haruo YOKOTATokyo Institute of Technology
Ookayama 2-12-1, Meguro
Tokyo, JapanTokyo Institute of Technology
Ookayama 2-12-1, Meguro
Tokyo, Japan

iritani@de.cs.titech.ac.jp

yokota@cs.titech.ac.jp

ABSTRACT
Many recent advanced applications of information systems
require large amounts of data to be stored in multiple harddisk drives (HDDs). The placement of data in these drives
is quite influential in reducing energy consumption while retaining the performance of the system. Several approaches
have been proposed in which frequently accessed files areconcentrated into a limited number of drives to enable spindown of the other drives. However, the placement of infrequently accessed data in these drives is also significant
because energy consumption during spin-up to access thesedata cannot be ignored, particularly when files tending to be
used together are placed on many different drives. In thispaper, we propose a novel method named PLECO (Placement of files for Latency and Energy Consumption Optimization), which aims to place correlated files into the same
drive to reduce power consumption and improve the performance of the system. We simulated and evaluated the proposed method, and these results indicate that our proposal
can reduce both the energy consumption and the access latency by up to 32% and 92%, respectively, compared with abaseline system.

General Terms
Energy Reduction

Keywords
File system, File correlation mining, Power saving

1. INTRODUCTION
In recent years, as the amount of data handled by advanced applications of information systems has been increasing rapidly, the energy consumed in storing these data has
also become large. For example, EPA (U.S. Environmental
Protection Agency) [1] reported that the number of installed
enterprise hard disk drives (HDDs) in the U.S. in 2010 would
be more than 3.7 times the number used in 2004. They also

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estimated that the energy consumption for enterprise storage devices had tripled between 2000 and 2006. Therefore,
the reduction of energy consumption by storage systems is
one of the most important issues for information systems,
particularly when handling large amounts of data, such as
in data centers or file servers.
To reduce the energy consumption of a storage systemcomprising multiple HDDs, several approaches based on theskewed distribution of file-access frequency have already been
proposed. These approaches divide the HDDs in a system
into two groups, and place frequently accessed files on HDDs
in one group by migrating or copying files from HDDs in theother group [7]. By such a placement, the number of accesses
to HDDs in the latter group become infrequent, with manyof them having the chance to spin-down (stop the rotationof the spindle motor), which is an effective means of power
reduction.
However, these approaches have two shortcomings. Whena file stored in an inactive HDD needs to be accessed, a spinup process is required to make the read or write operation
available on the HDD. It can take a long time (15–20 ms) for
the rotation to become stable and enable the operation to becompleted. Moreover, compared with normal disk accesses,
a HDD consumes large amounts of electricity when it spins
up. Therefore, frequent spin-downs and spin-ups incur longaccess-latency times and large energy consumption.
If a number of files accessed at the same time are scattered around multiple HDDs in the inactive group, theseshortcomings will become even more serious because the simultaneous spin-ups will raise the peak power consumptionof the storage system. On the other hand, if those files
tending to be accessed at the same time are placed in thesame HDD, the number of spin-ups can be reduced. Suchplacement is effective, not only for reducing the total power
consumption of the storage system but also for improving
its performance by omitting the access latency originatingfrom irrelevant spin-ups.
In this paper, we propose a novel method for migrating
those files tending to be accessed at the same time into thesame HDD to achieve better energy consumption and performance of the storage system. For this purpose, it is important to identify files accessed at the same time. We introduce the concept of a task to represent a group of files
used to accomplish some particular goal, such as writing areport or preparing presentation slides. This implies thata task will contain correlated files based on the behavior of
its users. The proposed method mines file-access logs to discover tasks, and tries to place the files belonging to the same

task on the same HDD in the inactive group.
We evaluated the proposed method via simulation. The
simulation results indicate that our proposal can reduce boththe energy consumption and the access latency by up to
32% and 92%, respectively, compared with a baseline dataplacement system.
The remainder of this paper is organized as follows. Section 2 briefly introduces related work to provide a background. As the basis of our proposal, Section 3 explainssome assumptions made about storage systems. In Section
4, we propose a method for placing correlated files on thesame HDD by mining file-access logs. Section 5 evaluates the
proposed method in comparison with some baseline methods, and reports on evaluation results, indicating that the
proposed method is effective for improving system performance and reducing energy consumption in storage systems.
Section 6 contains the conclusions of this paper and points
to future work.

2. RELATED WORK
2.1 Energy-conscious Storage Systems
There are a number of methods for reducing the energy
consumption of a storage system comprising multiple HDDs.
Among them, the Massive Array of Idle Disks (MAID) [7]
method is well known. MAID employs a number of HDDs
called cache disks that rotate continuously and store recently
accessed data. This concentrates most accesses to them, by
analogy with cache memory. RAPoSDA [15] enhances theconcept of MAID by including the primary-backup configuration and considering the rotational states of each HDD
to make the system more reliable and to reduce energy consumption further by utilizing the backups. Whereas MAID
and RAPoSDA adopt least-recently-used (LRU) placement,in this paper, we propose a dedicated data- placement strategy by analyzing file-access histories.
A framework that uses reinforcement learning to optimize
file accesses is proposed. Vengerov [18] proposed a methodthat observes the number of file input/output (I/O) requests
in the queue at each tier of a hierarchical storage system,and tunes parameters for estimating the effect of migration
using reinforcement learning. However, this was aimed atminimizing the file-access times and did not consider energy
consumption.
Power-saving storage systems based on RAID [14] technology have been also proposed. PARAID [19] introduces
the concept of changing gears, which varies the number ofactive HDDs to make energy consumption proportional toperformance. GRAID [13] places an emphasis on ensuring
reliability and power saving based on RAID10 disk arrays.
Using information in the disk which is added into the normal disk to store logs, the system only needs to update themirror disks periodically. Therefore the system can spindown all the mirror disks to a low-power mode for most of
the time and save energy. EERAID [12] is another powersaving method that is focused on the RAID controller layer.
EERAID reduces the power consumption using dynamic I/Oscheduling and a cache-management policy. Our approach
can also be applied to RAID-based storage systems, but focuses on file placement within storage systems, rather thanon block accesses.
Distributed storage systems employing the concept of power
proportionality through leveraging cluster-based data place-ment with data replication, such as Sierra [17] and Rabbit[4], have been proposed. These methods achieve a proportional relation between power consumption and system performance by dividing all storage nodes into groups and deciding which groups actively serve certain workloads. Our
approach does not aim for power proportionality.
Other approaches to reducing power consumption in astorage system involve changing the rotational speed of
HDDs. DRPM [10] controls the rotational speed of a HDD
dynamically, depending on how heavy its workload is. However, HDDs capable of dynamic control of their rotational
speed are not yet available commercially. Therefore, thismethod currently cannot be implemented in real-world service. Hybernator [23] leverages multispeed HDDs. Like
DRPM, a multispeed disk can change its rotational speed,
although the changes are stepwise and not continuous. While
keeping the rotational speed low makes it possible to reduce energy consumption by the HDD, changing speed itself consumes more energy than the other states. Therefore,changing the speed frequently might have a bad effect on theenergy consumption. This method adopts a coarse-grain response when it observes a workload, to suppress the numberof extra speed changes.
Whereas methods such as DRPM and Hybernator require
special hardware devices to control energy consumption dynamically, we assume the use of ordinary, commercially available HDDs for the proposed method. However, our method
could also be applied to any storage system that aims to
reduce energy consumption by switching between states in
HDDs.

2.2

Log Mining to Find File Correlations

In our approach, we employ correlations between files toguide file placement in a storage system to reduce its energy
consumption. To derive the file correlation, there are several
approaches to mining access logs.
To group files into virtual directories corresponding to user
tasks, the FI and SUGOI methods [20] apply data-mining
techniques to file-access logs. They were originally proposed
to support file searches in file systems, but we adopted thisapproach in this paper for energy-consumption purposes.
The details of these methods are described in Section 4.
FARMER [21] is another mining method for finding correlations between files from file-access logs. It collects attributes of accessed files such as timestamps, file paths, andusers, and calculates the co-occurrence of attributes between
files.
XAPC [22] observes query patterns issued for an XML
tree structure to reduce the energy consumption of HDDs
by utilizing the relationships between XML data. It applies
a data-mining algorithm to uncover relationships between
parts of the data structure in the XML tree. It then migrates
these XML data into multiple HDDs based on the relationships between query patterns. It involves data placement
based on correlations, but focuses on logs of queries, not fileaccesses.
Several methods aim to derive relationships between files
based on access sequences to predict access patterns and toprefetch files. The baseline SUCCESSOR method logs subsequent accesses for each file. Griffioen et al. [9] proposeda method based on a probability graph that keeps multiple
successors for each file in the form of weighted edges. Noah[3] is an extension of the SUCCESSOR method that aims tofilter out noise patterns by updating the successor to eachfile only if the new successor can be regarded as reliable.
The Finite Multi-Order Context model [11] has been proposed for file-access pattern prediction. It originates fromthe contextual prefix tree (TRIE) of the PPM text compression algorithm [6]. It tracks the access sequences in a TRIE
whose multiple partitions are limited in size.

3. ASSUMPTIONSIn this section, we describe the assumptions about storage
systems that we use in applying our method.

3.1 States of a HDD
We assume that target storage systems comprise multiple HDDs for file storage. Most commercially available
HDDs have three states, namely “ACTIVE”, “IDLE”, and“STANDBY”.
The ACTIVE state is the state in which data in the HDD
are being accessed. Because its spindle motor, actuator, andcontroller are all working in this state, this state consumes
more power than the other two states. After completing thedata access, the HDD changes its state to IDLE. The HDD
keeps its platters rotating, but activates neither its actuator
nor its read/write controller. Therefore, power consumptionof a HDD in the IDLE state is less than that in the ACTIVE
state.
A HDD can stop its platter rotation to change its state
to the STANDBY state. In the STANDBY state, the HDD
stops rotating and moves its disk head away from the platters. This state requires much less power than the IDLE
state. Therefore, we can reduce the energy consumption of
HDDs if we keep most of them in the STANDBY state asmuch as possible.
However, the state transition from STANDBY to IDLE,
or vice versa, which involves spin-up or spin-down of its spindle motor, takes a relatively long time compared with thetransition between the ACTIVE and IDLE states. For example, a commercially available HDD [16] typically takes 15
seconds to spin-up, because the HDD waits until its rotation
becomes stable before read/write operations are acceptable.
Moreover, it consumes large amounts of electricity to stopor to resume the rotation of a HDD, even compared with the
ACTIVE state. For these reasons, trying to stop and start
disk rotation aggressively may lead to bad effects on boththe file-access latency and the energy consumption by disks
because the number of disk spin-ups would increase. To conclude, there are two important issues in reducing energy consumption by a storage system. One is to stop the rotationsof most of the HDDs, and keep them in the STANDBY state
as long as possible. The other is to suppress the number of
unnecessary spin-ups caused by an overaggressive spin-down
policy.

3.2 Storage System Configuration
The target storage system comprises multiple HDDs. We
do not assume special functions for the HDDs, except thatthe HDD is able to spin-down (stop rotation of its spindle
motor) if there is no access during a specified time period.
A HDD in the STANDBY state will spin-up when it receives
an access request, and enter the ACTIVE state.
We divide HDDs in the target system into two groups,
placing frequently accessed files in one group and the remaining infrequently accessed files in the other group. In thispaper, we call a HDD in the former group a high-frequency
disk, and a disk in the latter group a low-frequency disk.
Whereas high-frequency disks are not spun down, a lowfrequency disk will be spun down when it has had no accesses
for the predetermined period of time.
The file system manages placement information and themetadata for each file to locate the files in the HDDs. The
file metadata contains information about the access frequency
for the file. For the access-frequency information, we use thenumber of accesses during a specified time span in this paper. We assume that these data are managed by some index
structure such as a B-tree [5].

3.3

Motivating File-access Patterns

If there are a number of files related to a task and they
are scattered across different low-frequency HDDs that havealready been spun down as a result of no access during thespecified time period, all these HDDs holding related files
will have to be spun up when the user accesses these files asa part of the task later. This will lead to rather large accesslatency and will consume a large amount of energy.
On the other hand, if these related files are located in one
disk, the number of spin-ups for the task will be reduced.
As a result, the access latency and the energy consumption
will be reduced. This will be effective for cases in which theaccess frequency of files changes periodically.
Files possibly used for the same task tend to be accessed
nearly simultaneously. Moreover, files accessed nearly simultaneously several times can be assumed to be used forthe same task. Therefore, these files will be accessed nearly
simultaneously in the future. The proposed method utilizes
this characteristic of file-access patterns.

4. PLECO
Here, we describe the proposed PLECO (Placement offiles for Latency and Energy Consumption Optimization)
method in detail. It extracts relationships between files
from the file-access log for a file server. It utilizes these
relationships to determine the appropriate HDD placement
for infrequently accessed files.
PLECO relocates infrequently accessed files which have astrong relationship to the same low-frequency disk to reduce
the number of spin-ups caused by scattered accesses among
low-frequency disks.

4.1

File-placement Strategy

To place files accessed nearly simultaneously into the same
HDD, PLECO migrates files between HDDs. It migrates afile to a low-frequency disk if it has not been accessed fora time longer than the predetermined threshold time. It
also periodically migrates files without accesses for a certain
period of time by observing the list of files without accesses.
The storage system does not execute relocation immediately. To suppress the number of spin-ups triggered by relocation processes, it keeps files to be relocated in a relocation buffer associated with the destination disk. It tries
to execute relocation processes when the disks required forrelocation are active for as long as possible.

4.2

File Correlation Discovery

The FI and SUGOI methods [20] use file-access logs tofind association rules between files using a data-mining algorithm. These methods were originally proposed to support

Access Log

File Correlation Rules

Time

File

Rules

Supp

Conf

13:03

a.txt

a.txt → b.xml

0.50

1.00

13:45

b.xml

a.txt → d.tex

0.25

0.50

14:32

d.tex

b.xml → d.tex

0.50

0.67

:

:

:

:

:

Convert13:0014:30
14:3016:00

Relation mining

a.txt

b.xml













16:0017:30
17:3019:00
Access Transactions

4.3

c.png

d.tex

e.pdf
TransactionTransactionTime: Accessed

Figure 1: Mining file-correlation rules from accesslogs

file searches. The basic idea is that files used within thesame time period are related to each other. Using extracted
rules, these methods classify the files and represent them as
virtual directories to the user.
Our idea is to apply the FI method to find relationships
between files for file relocation. We briefly describe how to
calculate relationships between files in the FI method.
Figure 1 illustrates the overall flows for discovering filecorrelation rules. Firstly, the FI method collects the fileaccess logs for the storage system. The file-access logs should
contain at least the timestamp and the ID of the requested
file for each access issued by a client. It then splits the fileaccess logs into “transactions” based on the timestamps. The
transaction duration is the range of the timestamps for all
the requests in each transaction. The transaction durationfor each transaction is the same.
To derive the association rules from these transactions, theFI method adopts a data-mining algorithm. In this paper,
we describe our use of the Apriori algorithm [2] as the datamining algorithm for PLECO. Other data-mining algorithms
for discovering association rules could also be used. Here,an association rule for file access, i.e., a file-correlation rule,
is represented by the form {lhs} → {rhs}, which indicatesthat transactions containing items in {lhs} also tend to haveitems in {rhs}. In our proposal, only rules whose length is
two (i.e., {lhs} and {rhs} each have only one file) are used.
Each rule indicates that both of two files in the rule havebeen accessed in a certain number of transactions.
The Apriori algorithm uses two measurements about occurrence possibilities to discover the rules. Support is theprobability that all items in both left-hand and right-handsets appear in a transaction. Confidence is the probability
that all items in the right-hand set appear in a transaction
when all items in the left-hand set appear. The Apriori algorithm filters file-correlation rules by setting thresholds forthese two measurements. The thresholds for support andconfidence are called the minimum support and the minimum confidence, respectively. The algorithm does not extract a rule whose support or confidence is less than thesethreshold values. Therefore, we can control the number of
discovered file-correlation rules by setting the appropriate
values for minimum support and minimum confidence.

Rule Application to Migration

Using the file-correlation rules derived by the FI method,
PLECO chooses the best HDD among all the low-frequency
disks to locate each file.
PLECO uses these rules when it migrates files from a highfrequency disk to a low-frequency disk. Because considering
file locations between multiple disks that are always active
does not help these disks reduce the number of spin-downs
and spin-ups, it does not utilize rules for migration in thereverse direction. Instead, it determines locations for thefiles using the hash code of each file.
When PLECO relocates a file from a high-frequency disk
to a low-frequency disk, it determines the destination disk
for each unpopular file on using the relation score calculated
for each low-frequency disk. All relation scores are initialized
to 0, and increased by the iterative process described below.
Firstly, PLECO collects all rules that have a file to be relocated in their right-hand set. For each rule, it checks if thefile in its left-hand set is in the relocation buffer of a lowfrequency disk. If the file is in one of the relocation buffers,
PLECO increases the relocation score of the disk which corresponds to the relocation buffer by the confidence value ofthe rule between it and the file. Then, to migrate the file,
PLECO places it in the relocation buffer that corresponds
to the HDD with the maximum relation score.
Figure 2 gives an example of the association-rule application during a file relocation. The file named “e.pdf” (shown
as “e” in the figure) is now to be relocated. There are
two candidate destination disks for the file. Each disk has
two files to be relocated in its relocation buffer, with theleft-hand disk in the figure having “a.txt” and “b.xml” andthe right-hand disk having “c.png” and “d.tex”. There are
four rules that represent the relationships between these files
and “e.pdf”. In this case, the relocation scores for the lefthand and right-hand disks are 0.8 and 0.9, respectively, and
PLECO therefore decides to place “e.pdf” in the right-hand
disk.
PLECO applies rules which files in their left-hand set are
in relocation buffers. It aims to prevent performance degradation by scanning all the metainformation about files tofind locations for all files in related rules. This omission ofrules can be justified because files with similar access patterns tend to be relocated nearly simultaneously.

5. EVALUATION
We evaluated the proposed method by a simulation experiment, using a software disk simulator that we developed.
This simulates multiple accesses to different HDDs, and outputs the processing time and energy consumption for eachaccess request.

5.1

Setting

The parameters used in the simulation for the disk model,
system configuration, and workload are as follows.

5.1.1 Disk Model
The target system in the evaluation had 16 HDDs for storing files. We adopted a disk model by reference to the specification of the commercially available HDD shown in Table1.

5.1.2 System Configuration

CDE AF

F F

F F

F

100
90
Energy Consumption (MJ)

80
B

D
A

F

60
50
40
30

AB

20
10

→

A

A

B CD →E

F

0
STRIPE→→70

RELOCATE

SUCCESSOR

PLECO

E

Figure 2: Applying association rules for a relocation
Table 1: The specification of the HDD model
Referenced model
HGST Deskstar 7K2000 [16]Capacity
2 TB
32 MB
Cache Size
Cache Replacement
LRU
Power on ACTIVE
11.1 W
Power on IDLE
7.5 W
0.8 W
Power on STANDBY
Energy for SPIN-DOWN 35.0 J
Energy for SPIN-UP
450.0 J
0.7 sec.
Time for SPIN-DOWN
15.0 sec.
Time for SPIN-UP

We assumed that the system could handle up to 32 requests simultaneously. If there are more than 32 simultaneous requests, all except the 32 first requests are blocked. A
request that requires access to a disk being issued anotherrequest is also blocked until the other request finishes its
access.
Initially, the files in the system are randomly scattered. To
compare the effectiveness of the various relocation methods,
the file server simply swaps files, keeping the number of files
in each disk the same.

5.1.3 Workload
We used an open trace of an actual NFS file server in the
EECS Department of Harvard University [8] as the workload for the evaluation. In this experiment, we extracted
records of READ and WRITE requests from the trace between September 1st, 2001, and September 10th, 2001.

5.2 Competitors
To compare energy consumption and access latency, we
implemented four methods, including PLECO, that place
files in multiple HDDs.
A baseline method named STRIPE simply distributes files
based on the hash code of each file. It does not attempt any
file relocation. To compare its energy consumption to methods that utilize two classes of disks (high-frequency and lowfrequency disks), our STRIPE implementation maintained
two HDDs as always active, and spun down any of the 14
HDDs without accesses for 30 seconds.

Figure 3: Energy-consumption comparison for thevarious placement strategies

RELOCATE is a method for relocating files based on theiraccess frequency. It maintains a list of files that have notbeen not accessed and periodically relocates files in the list to
disks with infrequently accessed files. In this experiment, we
configured a RELOCATE system with two high-frequency
disks and 14 low-frequency disks. It observes the states of alldisks and tries to execute relocation when both the source
disk and the destination disk are spinning. This helps reduce the number of spin-ups invoked by the file-relocation
processes. RELOCATE determines the destination disk forrelocated files using the hash code of each file.
The last-successor method is known as a basic file prefetching method, which logs the file accessed just after it is accessed [11]. We modified the last-successor method for filecorrelation extraction to examine the characteristics of filecorrelations extracted by our method. SUCCESSOR is amethod based on the last-successor. It assumes that eachfile has a relationship to its successor file. In other words,
SUCCESSOR treats an edge of the access sequence A → B
as an association rule {A} → {B} whose confidence score
would be 1.0 in the PLECO method.
PLECO behaves similarly to RELOCATE but uses information about the access relationships between files in the
file-relocation process. It recalculates file correlations using
the Apriori algorithm every 24 hours. We chose 90 minutes as the length of each transaction. We assumed thatthe data-mining process can be executed as a backgroundprocess, with no adverse effects on the services of the fileserver. Although the process has to read the file-access log
once, there are only a few of these processes compared with
the number of file accesses to the server, and we assumed
that the effect of this process on energy consumption would
be negligible. Therefore, we did not include the execution
and energy overheads of the data-mining process.

5.3

Energy Consumption

Energy consumption by the HDDs for the various placement strategies is compared in Figure 3.
As shown in the figure, PLECO consumes less energy thanthe other three methods. STRIPE consumed over 90 MJ,
which is the highest energy consumption among the methods. As described in Section 5.2, the STRIPE method does
not attempt any file migration between disks. Many popular

9000080000
70000# of Spinups

60000
50000
40000
30000
20000
10000
0
STRIPERELOCATE

SUCCESSOR

PLECO

Figure 4: Comparison of the number of spin-ups
80

Average Access Latency (sec.)

70
60
50
40
30
20
10
0
STRIPERELOCATE

SUCCESSOR

PLECO

Figure 5: Comparison of the average access latency
files were put on one of the low-frequency disks, where theyremained. As a result, accesses to these popular files caused
a number of spin-ups.
By relocating files based on their access frequencies, the
RELOCATE method reduced the energy consumption ofdisks by 18% compared with the naive STRIPE method.
This is because the number of spin-ups caused by accesses topopular files was decreased by moving those files to alwaysactive disks. Furthermore, the SUCCESSOR method required less energy than the RELOCATE method by utilizing
file correlations.
Although RELOCATE and SUCCESSOR reduced the energy consumption required by disks to some extent, PLECO
consumed much less energy even than them, with an energy
reduction of 32% compared with the naive STRIPE method.
PLECO even achieved 8% less energy consumption than the
SUCCESSOR method.
To investigate the relationship between energy consumption and disk spin-ups, we counted the number of spin-ups
during the simulation. Figure 4 shows the results, whichindicate the same tendencies as the energy consumption result in Figure 3. This confirms that reducing the number of
HDD spin-ups reduces the energy consumption.

5.4 Access Latency
We measured the average file-access latency to evaluate

the performance of each data-placement strategy. The average access latency is defined as the amount of time required
to respond to an file-access request when it is issued by aclient. Figure 5 compares the access latency for the four
methods.
These results show the same tendency as the energy consumption comparison described above. Because there area large number of accesses in very short periods of time in
the workload, simply scattering files across the disks caneasily increase the average access latency. In fact, STRIPE
required over 70 seconds on average.
This serious access latency is caused by a number of requests being issued nearly simultaneously. In such a situation, most requests are blocked until the server finishes
processing the preceding access requests, which may require
some disk spin-ups.
By migrating files depending on their access frequency
and consolidating disk accesses in high-frequency disks, RELOCATE can reduce the average access latency by 20%.
However, it remains high because RELOCATE cannot completely eliminate cases where accesses are distributed to multiple low-frequency disks. If there are many accesses to files
that have not been accessed for a while, the system may haveto spin-up many low-frequency disks. Therefore, RELOCATE can have the same problems as the STRIPE method.
The experimental results indicate that utilizing file correlations can be one of the effective ways of solving this problem. SUCCESSOR can respond to file accesses in only 12%of the time required by RELOCATE. SUCCESSOR is able
to uncover relationships between files to some extent, andthese file correlations help the file system consolidate thefiles that tend to be accessed nearly simultaneously. This is
the reason for SUCCESSOR having a reduced average accesstime compared with STRIPE and RELOCATE.
PLECO has the best results in this evaluation. PLECO
only requires 7.6% of the time required by STRIPE and isfaster than SUCCESSOR by 18%.

5.5

Effects of Parameters

Two parameters control PLECO’s behavior, namely minimum support and minimum confidence. To examine theeffects of these two parameters, we evaluated PLECO withvarious combinations of them. We used minimum support
values of 0.10, 0.15, and 0.20, and minimum confidence values of 0.2, 0.4, 0.6, and 0.8.
The evaluation results are shown in Figures 6–8. These
three results show similar characteristics, confirming thatthe number of HDD spin-ups is strongly related to theirenergy consumption and performance.
As these results show, lower confidence tends to result inbetter energy consumption and access latency. A higher confidence means that the association rules for files extracted
from the file-access log become more reliable while the number of rules decreases. For minimum support values, theresults show similar characteristics. According to these results, applying as many rules as possible, including possibly
unreliable rules, is considered effective in most cases for reducing energy consumption and access latency.
However, the idea to utilize as many rules as possible does
not always give the best result. For example, when the minimum support is 0.15, the best confidence value among thefour choices is 0.4 rather than 0.2. As described in 4.3,
PLECO calculates the relocation scores for each disk by ac-

75

Confidence >= 0.2
Confidence >= 0.4
Confidence >= 0.6
Confidence >= 0.8

63000Energy Consumption (MJ)

62000

# of Spinups

61000
60000
59000
58000
57000
56000

25

Confidence >= 0.2
Confidence >= 0.4
Confidence >= 0.6
Confidence >= 0.8

Average Access Latency (sec.)

64000

70

65

55000

Confidence >= 0.2
Confidence >= 0.4
Confidence >= 0.6
Confidence >= 0.8

20

15

10

5

54000
53000

60
0.1

0.15

0.2

0
0.1

Minimum Support

0.15

0.2

0.1

Minimum Support

0.15

0.2

Minimum Support

Figure 6: Number of spin-ups for Figure 7: Energy consumption Figure 8: Average access latency
PLECO
for PLECO
for PLECO
cumulating the confidence values of all related rules. If thenumber of unreliable rules is relatively large, these wrong
rules may overwhelm the few reliable rules.
To conclude, whereas lower thresholds of support and confidence values result in better energy consumption and performance in most cases, overly low values of minimum support and minimum confidence may lead to the inappropriate
application of unreliable rules in some cases. Finding optimal threshold values based on the behavior of accesses is
included in our plans for future work.

5.6 Discussion
In this evaluation, all results confirm that reducing thenumber of spin-ups helps the file system reduce the energy
consumption and the access latency.
In the experiments, we found that migrating files based on
file-access frequency alone can reduce energy consumption
and improve performance. However, these results also showthat it is not always sufficiently effective, particularly when
there are many accesses to file groups distributed across multiple low-frequency disks. Utilizing file correlation can handle this problem and showed better performance with lessenergy consumption.
For both energy consumption and access latency, the proposed PLECO method achieved the best results of all competing methods. This is because PLECO can detect relationships between files and utilize this information in migration processes better than a file-prefetching method alone.
A prefetch method using access sequences such as SUCCESSOR can find inaccurate correlations because the sequential
access to two files does not always mean that there is a strong
relation between them. PLECO uses an association-mining
method, which can find access patterns that frequently occur, giving file-correlation rules that are more reliable thanthose of methods that use only access sequences.

6. CONCLUSION AND FUTURE WORKIn this paper, we proposed a novel data placement method
called PLECO for reducing the power consumption and improving the performance of a storage system comprising multiple HDDs. It derives association rules by mining file-accesslogs to discover correlations between files and relocates thecorrelated files to the same HDD, thereby eliminating irrelevant HDD spin-ups.
The evaluation results, from simulations that used an open
trace of an actual NFS file server, indicated that PLECO
provided the best results when compared with a baseline
method and successor-based methods with respect to energy

consumption by the storage system and the average accesslatency. Compared with the baseline system, PLECO reduced the energy consumption of the system and the average
access latency by 32% and 92%, respectively.
In our evaluation, we assumed that there is no cost in
calculating or managing the association rules. Although
the Apriori algorithm is efficient, it does consume a certain
amount of time. This cost may not be negligible, particularly when there are many files in the file system. In future
work, we plan to manage correlation rules between files in a
distributed data structure.
We evaluated our method using a set of file-access logs.
The optimal value of the parameters of the proposed method
may depend on the behavior of the file accesses in its workload and may vary with workload to some extent. Evaluating the proposed method with different workloads will be
required to discover any changes in optimal parameter values caused by changes in workload and to discover a way to
optimize these values.

Acknowledgments
This work is partly supported by Grants-in-Aid for Scientific
Research from the Japan Science and Technology Agency(A) (#22240005).

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