Long-Range Dependence at the Disk Drive Levelningfang/SimPaper/paper-QEST06-lrd.pdf ·...

Long-Range Dependence at the Disk Drive Level

Alma Riska Erik Riedel Seagate Research Seagate Research

125 1 Waterfront Place 125 1 Waterfront Place Pittsburgh, PA 15222 Pittsburgh, PA 15222

Alma.Riska@ seagate.com Erik.Riedel@ seagate.com

Abstract

Nowadays, the need for storage devices arises in a wide range of computer and electronic devices such as enterprise systems, personal computers, and consumer electronics. Understanding workloads at the disk level in these different systems is essential for enhancement of disk reliability, availability, and pe~ormance. In this papel; we present a characterization of disk drive workloads in a wide range of applications and computing environments. Although often idle, disk drives in all environments exhibit high variability and strong burstiness in interarrival-times and request location. We identzfy long-range dependence as a key statistical characteristic that should be taken into consider- ation when modeling storage systems and synthetic workload generators.

1 Introduction

Hard disk drives have become the only versatile form of persistent storage that offers reliability, high performance, and a wide range of form factors and capacities, which best suits the needs of highly dynamic and ever changing computing environments. Currently, storage solutions are in demand not only from traditional computer systems, such as the large, complex multi-user servers and the single-user desktoplnotebook PCs, but also from the very fast growing sector of consumer electronic devices, where computing resources are making a presence more and more each day.

Deployment of disk drives under such a wide range of applications with highly variable service requirements has increased the importance of accurate understanding of disk drive workloads. In this paper, we aim to characterize disk drive workloads and understand the commonalities between all the different environments as well as their differences. Such an understanding helps on accurate modeling of storage systems and synthetic workload generators as the nec- essary initial steps on designing new storage systems.

The evaluation of I 0 workloads in general, and of disk drive workloads in particular, has been the focus of the research community for a long time, because storage is the slowest processing unit in a computer system. Only an accurate understanding of I 0 workloads results in efficient storage operation optimization and performance improve- ment [I , 7, 171. File system level workloads rather than disk drive level workloads have been the focus of multiple I 0 workload characterization works [12, 15, 19, 211. At the file system level, instrumentation of the operating system modules enables system-level tracing. To measure disk level workloads, instrumentation is more complex, in particular when it comes to large enterprise systems with RAID controllers in them. HP labs [16] put a notable effort to col- lect and make public disk level traces in large enterprise systems.

Different from the previous efforts in I 0 tracing, our data collection is done with hardware assist rather than software instrumentation. Seagate engineers use SCSI or ATA bus analyzers, attached to the I 0 bus, to capture the electrical signals arriving at the disk drive and store them for later processing. Hence the data collection procedure does not effect the system performance and is not restricted on the systems with tracing software instrumentation in place.

The flexibility of the tracing procedure has enabled data collection in a variety of systems supporting a variety of applications from web servers, e-mail servers to game consoles and MP3 players. This set of traces (measured dur- ing the 2004 calendar year) covers the majority of applications that generate I 0 and disk drive workloads. In todays computing, it is critical to characterize storage workloads over the entire spectrum of deployment, because there is the emerging set of applications in the non-traditional computing environment of consumer electronics that needs to be positioned correctly when it comes to their storage demands and behavior.

Our evaluation, in compliance with previous I 0 workload characterizations [5, 8, 161, indicates that disk drives operate under traffic that suffers from long-range depen-

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dence. Furthermore, we show that this characteristic is exhibited in almost all environments, in particular, the consumer electronics ones. The presence of long-range dependence in all environments indicates that it is critical that any disk drive workload model captures correctly the dependence structure, in order to have accurate analysis and evaluation results. Long-range dependence and self-similarity are common characteristics of workloads generated by computer systems and various of their components. We mention here the self-similarity exhibited by Ethernet traffic [I I], Web server traffic 141, and file system traffic [9]. These works emphasize the impact that long-range dependence and self-similarity have in the performance of the respective components and of the entire system, and that approximat- ing them with simple processes such as Poisson or Expo- nential will yield inaccurate modeling results [g].

Our characterization shows that long-range dependence is exhibited in both the arrival process and the request position process (the latter is tightly related to the service process). Between READs and WRITES, we note that READs have a stronger correlated structure than WRITES. The arrival and request position processes, viewed as combined processes of the respective READs and WRITES processes, always inherit the characteristic of the subprocess with the largest portion and do not exhibit stronger dependence structure than any of the two subprocesses they are composed of. Furthermore, we do not observe long-range dependence between the arrival process and the request position process, except in isolated cases. This result is important for devising accurate disk drive synthetic workload generators [5, 201. Finally the departure process, with the exception of only a few cases, has the same dependence structure as the arrival process because most of the traces are measured in systems with underutilized disk drives.

The rest of this paper is organized as follows. Section 2 presents a short background on disk drives design and operation. In Section 3, we explain the general format of the traces and describe the specifics of the measurement environment. Section 4 presents a detailed analysis of the interarrival process captured by our traces. In Section 5, we discuss request location and departure process characteristics. We conclude with Section 6 where we summarize our findings and outline our future work.

2 Background

In this section, we present a short summary of characteristics of disk drive operation, which explains some of the comments and discussions throughout the paper.

Disk drives store data in the form of circular tracks on both surfaces of circular platters. Data is read or written using the disk head, in multiples of sectors (currently 512 Bytes). Modem disk drives have up to 4 platters, one head

per surface, tens of thousands of tracks on each surface, and 500 to 1000 sectors per track. Hence, each location on a data surface is uniquely represented by the distance (in tacks) and the angle from a reference point, i.e., position of the disk head.

In order for the data to be read or written the head should be placed on top of the data location. Hence, disk service process has three main components; (1) seeking to move the disk head on the right track, (2) rotating to position the head on the right sector, and (3) transferring data to the host. (1) and (2) represent positioning components and are, at least, one order of magnitude longer than the data transfer com- ponent. Consecutively, request position on the disk surfaces rather than request length determines the service time. Seek time and rotational time are comparable. However, while rotational latency is linear to rotational angle, seek time is not linear to seek distance. Various optimizations exist to reduce seek times and rotational latencies via disk scheduling and caching [14, 1 81.

Note that disk scheduling is non-work conserving, because different ordering of requests generate different position latencies between consecutive requests and ultimately different service times. This is why request location, in particular, and workload characteristics, in general, are de- ciding factors on the optimization that can be done for improved utilization of disk resources and overall performance.

3 Measurement Environment and Initial Analysis

Our goal in the paper is to characterize disk drive workloads with respect to different computing environments which deploy disk drives. We categorize different computing environments based on the resource availability and applications that they support as follows:

The enterprise environment includes all high-end computer systems where performance, data availability, and reliability are critical. In such systems, the storage subsystem consists of multiple disk drives rather than single disks. Commonly the disks are placed in RAID arrays [13] for added reliability. Such storage subsys- tems are placed on the server side rather than on the client side and run applications such as web servers, databases, e-mail servers, user accounts servers etc.

The desktop environment consists of all computer systems where a single disk is sufficient. The reliability and performance requirements are less critical because such systems are usually used by singIe users (such as PCs and notebooks).



The CE environment consists of all consumer electronic devices that need a disk drive (or any other form of persistent storage such as the non-volatile flash memory) to fulfill their storage needs. Such devices include PVR (Personal Video Recorder), game consoles, MP3 players, digital cameras, and more recently video cameras and mobile phones. The sophistication of computing resources in such devices is limited. They operate on (relatively) simplistic file systems, have limited memory and computing power. However, they still have performance and reliability requirements that should be met from the storage subsystem. The CE devices use the same type of disk drives as the desktop systems, with the distinction of the form factor (size) in the mobile and small devices.

We stress that it is critical to distinguish the environments where disk drives operate, because traditionally different hardware is used in different environments. For example the specialized and high performing SCSI disk drives are used in the enterprise environment while ATALDE disk drives are used in the desktoplnotebook environment [2]. However, nowadays with various technological advances including the Native Command Queuing and Serial ATA (SATA), system designers are often replacing the SCSI disk drives with the SATA ones in various enterprise environments. Furthermore, the new CE applications are putting new service demands, i.e., performance, reliability, and cost, on the traditional ATWIDE disk drives. Understand- ing the characteristics of workloads per environment class is critical to decide if a specific hardware fits the requirements of the environment.

Because of the Non-Disclosure Agreements that are in place for the traces that we are evaluating in this paper, we will not explain in detail the measurement environment but will identify each trace by the application that run dedicat- edly in the measured system. However we note that our enterprise and desktop traces are measured in real systems in the field. Enterprise traces are measured in servers de- ployed in various organizations and the desktop traces are measured in employees (engineers) PCs while they run their daily applications. The CE traces (because of the characteristic of the applications) are measured in controlled systems (i.e., the scenario of the application is set by the engineer performing the measurement). A11 traces are measured using a bus analyzer that intercepts the electrical signals in the I 0 bus. Hence trace collection does not affect system performance and does not require any instrumentation in the I 0 hierarchy.

Each trace records for each request the arrival time and the departure time in microseconds (providing fine gran- ularity for our evaluation purposes), the request length in

Trace

Web Database

L I I I I

1 PVR A 1 CE I 20hrs 1 89% 1 9515% 1 111 I

Sys. Ent. Ent.

E-mail Soft.Dev. User Acc. Desktop 1 Desktop 2 Desktop 3

Table 1. General trace characteristics. Queue length column has the average and maximum queue length captured in traces.

92% 94% 98% 99% 99% 99%

PVR B MP3 Player Game Console

bytes, the first sector on the disk where the requested data is located, the type of each request, i.e., a READ or a WRITE, the disk ID (when measurements are performed in an array of disks), and the queue length at the disk (applicable only for enterprise environments where the maximum allowable disk queue length is more than one). The length of the traces varies from several minutes to several hours and each trace has from tens of thousands to several million requests.

From the extended set of traces that we have, we se- lected a few representative ones to make the presentation easier. Table 1 lists all traces that we evaluate in this paper and their main characteristics such as the environment, trace length (in hours), system idleness (calculated from the trace), the portion of READS and WRITES in the measured I0 traffic, and the average and maximum queue length at the disk drive.

Observe in Table 1, that, apart from a few exceptions, disk drives are mostly idle. This is the result of the system architecture which aims at utilizing caches and rnini- mizing accesses to the slow disk drives. However, bus idleness measured in our traces does not include the background work often scheduled in disk drives (mostly in those that operate in enterprise environments) which aims at enhancing both reliability and performance. Queue length at the disk drive is often minimal. Among the measured drives, only the ones in enterprise systems (i.e., SCSI drives) support queuing. The Database trace, as shown in Table 1 , has the largest average and maximum queue length.

We distinguish between the READ and WFUTE traffic because they are handled differently throughout the memory hierarchy. The main reason behind that is that WRITES modify data and should be safely stored on a persistent me- dia and avoid data loss in the case of system or disk drive

Length 7.3 hrs 4.7 hrs

Ent. Ent. Ent.

Desk Desk Desk

25 hrs 12 hrs 12 hrs 21 hrs 18 hrs 24 hrs

,

CE CE CE

Idle

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QL 1116 4/58

82% 18% 95%

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111 111 111



E-mail. Desktov 1 Game Console

Time in hrs Time in hn Time in hn

Figure 1. Access pattern for three traces. Plots represent the position on disk for each request as a function of time. Straight lines indicate sequential disk accesses.

crashes [16]. Note in Table 1 that the READfWRITE ratio varies widely and it is an application dependent rather than an environment dependent characteristic.

Another important characteristic of disk level workloads is the access pattern, i.e., the request position on the circular disk surfaces as function of time. Each disk request is iden- tified by the address of the first requested sector (i.e., a unit of data on the disk is a sector which usually is 512Bytes long) The access pattern can be random, sequential, or a mix of the two. Random workload has consecutive requests distributed uniformly across the disk while sequential workload has consecutive requests located next to each other on the disk. In general, it is accepted that enterprise and desktop environments do operate under random workloads (see Figure 1). CE application which mostly do video and audio streaming generate workloads that are mostly sequential (see Figure 1). Random workload have high service demands, because the disk arm has to move substantially from one place to another to locate and serve each request. Se- quential workloads have lower service demands when com- pared to random workloads because requests are located close to each other and the disk head locates the requested data relatively fast. Figure 1 depicts hour-long access patterns for only three traces from Table 1. Observe that the E- mail trace (enterprise) is not only random but also spreads over a very large area on the disk (i.e., 70GB), which means that the service demands are higher than the desktop and CE traces which access a small area on the disk.

4 Interarrival Times Process

We start our analysis with the arrival process and focus on the statistical properties of the interarrival times. In Table 2, we present the mean and coefficient of variation (CV), i.e., the ratio between the standard deviation and the mean, of the interarrival times for all traces under our evaluation. Note that enterprise and CE environments experience higher arrival rates than the desktop environment.

Table 2. The mean and CV of interarrival and service times for our traces.

Trace

The interarrival times are highly variable with CVs being as high as 19, except a few cases in the CE environment. The CE traces with low CV represent those applications that do mostly streaming of videolaudio and low variability is expected. The PVR A CE trace represents a scenario where some background activity is present and breaks the almost deterministic pattern of videolaudio streaming.

Interamval times 1 Service times Mean (ms) 1 CV I Mean (ms) 1 CV

Results of Table 2 are confirmed also from the plots of Figure 2, where we present the arrival rate (as number of requests per second) as a function of time for only three of our traces. Note that both enterprise and desktop environment are characterized by bursts in the arrival process, but the bursts are short lived in the desktop environment and sustained longer for the enterprise environment. Results of Figure 2, in particular arrival process for the PVR B trace, indicate that arrival process is self-similar and we quantify its long-range dependence in various ways as presented in the following subsection.

Database

User Acc.

Desktop 1 Desktop 2 Desktop 3

PVR A PVR B

MP3 Player Game Console

509.8 405.0

2882.2

80.6 72.7

195.2 148.4

17.7 7.0

19.2

8.2 1.0 2.1 3.2

3.08 2.63 2.64

9.77 8.20 5.71 6.93

2.22 1.98 1.97

0.55 1.24 2.38 0.83



Time in hrs Time in hn Time in hrs

User Acc. Desktop 2 PVR B

Figure 2. Arrival process as a function of time for three of our traces.

250 250 250

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4.1 Dependence Structure

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Initially, we use the autocorrelation function (ACF) to evaluate the dependence structure in the interamval process. ACF is a statistical measure of the relationship between a random variable and itself [3]. In a stationary time series of random variables {X,), where n = 0 , . . . , co, ACF of lag k , p x ( k ) , is the value of the comelation coefficient for lag k > 0:

0 0.5 1 1.5 2 2.5 0 0.5 1 1.5 2 2.5 O 0 0.5 1 1.5 2 2.5

where p is the mean and b2 is the common variance of {X,). The lag k denotes the time separation between the occurrences Xt and Xt+k. The values of px ( k ) may range from -1 to 1. If px (k) = 0, then there is no autocorrelation at lag k .

In most cases ACF approaches zero as k increases. The ACF decay rate determines if the time series is short-range dependent or long-range dependent. The ACF captures the "ordering" of random values in the time series. Positive ACF values imply that there is strong temporal locality, i.e., a value of the random variable has a high probability to be followed by another variable of the same order of magnitude, while negative ACF implies the inverse.

We would like to point out that measuring long-range dependence or burstiness in traces is challenging given that the data contains noise from various sources. This noise, in particular the periodic and patterned activity, affects the stationarity of the time series which is a ondition for the ACF (or the Hurst parameter later on) to old as a statistical test. Even in the literature it is accepte that there is not one single pre-processing procedure to re ove noise before measuring long-range dependence. At th disk level, with service times in the level of milliseconds, with very lim- I ited resources such as CPU power and c che, and with a very strict communication interface (SCSI or ATA), we ex- 1 pect periodicity to have a lesser impact. e confirmed thjs assumption by partitioning the traces in sibtraces and generating the ACF for each of them. Com aring them, we I'

noticed minimal difference from the overall results that we presents in the remaining of this paper.

We calculate up to 1000 lags and plot the results in order to visualize the strength of long-range dependence in the interarrival process at the disk-drive level. Note that the ACF plots are not presented in the log-log scale as commonly done in the literature. We opted for such presentation for two reasons. First, we would like to show both the body and the tail of the ACF because for some traces it is inter- esting and useful information. Secondly, the ACF plots are intended only for qualitative evaluation of long-range dependence and we use the Hurst parameter [3] for the quantitative evaluation.

In Figure 3, we show the ACF of disk interarrival times for all the traces of Table 1. We group the traces by their environment. Note that enterprise and CE traces have a slow decaying ACF which indicates strong dependence structure in the interarrival process. The desktop traces on the other hand experience only short-range dependence. Observe the oscillating correlation structure of the PVR B trace in the CE plot, which is a result of the almost deterministic behavior of video and audio streaming in the PVR application.

While the ACF plots of Figure 3 qualitatively indicate long-range dependence in the interarrival-times process, we quantify it by estimating the Hurst parameter of the interarrival-times process. Hurst parameter is used widely in various studies [3,11] as a way to quantify long-rangede- pendence and self-similarity in stochastic processes. Since statistical definition of long-range dependence [3] is a limit result for a finite time series, the accuracy of the Hurst parameter estimation depends on the time series itself and the estimation method. Hence, the most accurate way to estimate the Hurst parameter is to use several methods and see if all estimations agree on the value of the Hurst parameter. In our analysis, the computation of Hurst parameters is done using five different estimation methods as shown in Table 3. More details on each method can be found in [3]. We use Selfis [lo] to compute the Hurst parameter in a time series.

Hurst parameter values of larger than 0.5 indicate long- range dependence [3]. Table 3 shows that the Hurst param-



Enterprise Desktop CE 0 . 4 t S ~ i I I I , ,

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I: 0.2 2 0.2 .d d z 9 0.1 0.1

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0 100 200 300 400 500 600 700 800 900 1003 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 loo0 Lag (U Lag (k) Lag (k)

Figure 3. ACF of interarrival times for all traces grouped by their environment.

Table 3. Hurst parameters for the interarrival times of all traces calculated using various statistical estimators.

PVR A PVR B MP3 Player Game Console

eter is higher than 0.5 in all traces and for all estimation procedures. Consistent with the results of Figure 3, enterprise and CE environments are more bursty than the desktop environment. Recall that CE devices use the same type of disk drives as the desktop environment, however their workload characteristics are more likely to impact performance than the desktop ones.

4.2 READs vs. WRITEs

0.868 0.549 0.837 0.826

The incoming traffic to a storage system, in general, and to a disk drive, in particular, is a mix of two traffic streams; (1) the READ traffic and (2) the WRITE traffic. There are various reasons why READs and WRITEs are distinguished and served differently by a disk drive. First, a WRITE in- volves changing the data on the disk drive and because users want assurance that the data is stored safely on the disk drive, the latter is designed to minimize WRITE'S waiting time in queue and consequently reducing the probability of data loss in the case of a system crash. Secondly, READs only transfer data from the disk drive to the file system and

various aggressive pre-fetching and post-fetching schemes are in place to exploit temporal and spatial locality of requests. Finally, the hardware design of disk drives is such that an average WRITE takes slightly longer to complete than an average READ.

In Table 1, we indicate the portion of READs and WRITEs in each of the traces in our evaluation. Note that the READIWRITE ratio is application oriented. For example, the Database has a more W E intensive workload than the E-mail server workload. However, the environment also plays a role in the ratio between READs and WRITEs. For example, in large enterprise systems, there are layers of caches in the path of an I 0 request before reaching the disk drive. These caches serve the majority of READs, and in such environments it is expected to have more WRITEs than READs. In our traces this is not quite the case (see Table I), because we suspect the systems have had non- volatile caches somewhere in the I0 path that safely stores some of the WRITE traffic.

0.710 0.573 0.594 0.511

In Figure 4, for three representative traces, we plot the ACF of interarrival times separately for READs and

0.614 0.577 0.928 0.842

0.515 0.582 0.747 0.726

0.551 ' 0.615 0.703 0.682



Email Desktop 1 MP3 0.5 0.5

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0 0 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 WO 10M)

Time in h n Time in hrs Time m hrs

Figure 4. Autocorrelation function of the interarrival times for three of our traces where READ and WRITE traffic is separated.

WRITES, and, for reference, also the overall interarrival process. Note that the READS traffic exhibits stronger long- range dependence than the WRITES traffic. The combined process is dominated by the characteristics of the traffic stream that has the largest portion in the combined process. For example, the ACF of the combined interarrival process for the M P ~ trace, with twice more READs than WRITES, is similar to the long-range dependent interarrival process Table 4. General statistics for three scenarios of the READ traffic. Note that the ACF for the combined of PVR A application. Hurst parameter is es- READ and WRITE traffic is not stronger than both the ACF timated with the Whittle method

of READs or WRITES separately.

4.3 Application Behavior Variability

In our analysis, we have observed that various characteristics of the arrival process are tightly related to the application. However the same application operates in different scenarios and exhibits variable behavior. The variability in application behavior is observed in all environments, because the majority of applications is user driven and less likely to abide to a stationary behavior. In our evaluation, we focus only in one application, i.e., PVR A, for which we have traces measured in three different scenarios. In Table 4, we present the general statistics of the three PVR A traces. Note that in almost all metrics that we present here, i.e., READlWRITE ratio, mean of interarrival times, and the Hurst parameter of the interarrival times, there are differences between the three traces.

In Figure 5, we present the ACF of the interarrival times for the three PVR A traces of Table 4. Note the differences in the dependence structure of the three traces. Trace "PVR A / Scenario 1" exhibits no long range dependence, however for the other two traces the dependence structure strengthen considerably, especially for trace "PVR A / Scenario 3".

5 Service and Departure Processes

In the previous section, we presented evidence that the interarrival process at the disk drive level exhibits long

Scenario 1 -- Scenario 2 Scenano 3 - - - - -

Figure 5. Autocorrelation function of interarrival times for three scenarios of PVR A application.

range dependence and high variability. In this section, we extend our evaluation to the service process and the departure process at the disk drive by extracting various metrics from the traces of Table 1. Recall that in our traces we have recorded only the arrival time and the departure time of each request. Except the enterprise traces, disk response time is equal to disk service time because there is no queuing at the disk level. For the enterprise traces, where there is queuing at the disk level, we estimate disk service times by the response times of requests that were served in busy periods with only one request. In our traces with low utilization, in average, one in every three requests was served in a busy



Enterprise Desktop CE 0.5 0.5 r~

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2 0.2

3 0.1

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0 100 200 300 400 500 600 700 800 900 loM) 0 100 200 300 400 500 600 700 800 900 loo0 0 100 200 300 400 500 600 700 800 900 IOOD Lag (k) ~ a e (k) Lag (k)

Figure 6. Autocorrelation function of seek distances in all traces grouped by their environment.

period with only one request (the worst case was one in five requests served in a busy period of one). Hence we main- tain that the accuracy of this estimation is acceptable. We verified this estimation procedure by computing the average service time as the ratio of the total amount of busy time to the total number of served requests and came up with very similar results.

In Table 2, we present the mean and the coefficient of variation for the estimated service times for all traces under our evaluation. Note that the average service time is in the range of a couple of milliseconds consistent through all traces. Also, there is only limited variability in the service process (see the coefficient of variation in Table 2). This characterization is expected since position time (seek time + rotational latency) makes up for the majority of a service time and it is bounded by the number of tracks on each disk surface and the length of each track.

Because the service process depends on the request location on the disk rather than on the request size, we perform an evaluation of request positions. Such an evaluation is critical when it comes to modeling the disk-level work- - loads in general and devising disk-level synthetic workload generators. Note that currently the most common way to model and analyze disk drive operation and performance is via simulations rather than analytic models 1171, because of the complexity that is involved especially when modeling accurately the service process. We analyze the characteristics of the request positions on the disk, by evaluating the distance between two consecutive requests, known as the "seek distance". Recall that for each request in the trace, we have the number of the first sector that stores the requested data and from that information we extract the respective request seek distance.

In Figure 6, we present the ACF of the request seek distances for all traces in our evaluation grouped by their environment. Note the strong dependence structure throughout the traces. The dependence structure in request location is much stronger than in the interarrival process. Such behavior is expected, because locality is an inherent characteristic of disk drive workloads 1161. Enterprise is considered

a more random environment than the desktop or CE ones. However, in enterprise systems in the I0 path there are several I 0 schedulers and caches that intend to order requests such that the seek distances are minimized. CE applications have the strongest dependence structure among all the environments and this is because the CE applications read or write long video and audio files with only a few caches in the I 0 path.

In Figure 7, we present the results of the same analysis of the dependence structure of the request seek-distances, but we separate the READS traffic from the WRITES traffic for only three traces. Note that the READ traffic is more correlated than the WNTE traffic across all three traces. Note the Desktop 2 plot in Figure 7. Although the READ process has stronger correlation structure than the WRTTE process, the combined process has a similar dependence structure with the WFUTE process because the latter makes up for 85% of the overall traffic.

5.1 Dependences between interarrival, location, and departure processes

Disk drive performance evaluation is mostly driven by simulations rather than analytic models and a critical com- ponent of the existing simulations 161 is the synthetic workload generators 1201. One of the components in accurate synthetic workload generators is the relation between the arrival process and the service process, i.e., request positions for the disk drives. In order to asses such relation, we compute and plot the correlation function for the series of interarrivals and request location for the traces of Table 1. We present our findings for only three of the traces in Figure 8. Observe that unlike most processes described previously in this paper, there is no correlation between arrivals and request seek distance in most applications. The exception is the PVR B trace (as shown in Figure X), where the same dependence structure seen in the individual interarrival times and seek distance processes is noticeable also in the dependence structure between the two. The results of Figure 8 indicate that the request seek distance and the interarrival times exhibit short range dependence in most cases. In ap-



Software Dev. Desktop 2 Game Console O . S I I I i I I ,

Time in hrs Time in h n Time in hrs

Figure 7. Autocorrelation function of seek distances in three traces where READS and WRITES are separated.

User Accounts Desktop 3 PVR B 0.25 t ~ i ~ , , m

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READs r 0.15

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0.05 0

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Lag (k)

Figure 8. Correlation function between request interarrivals and request seek distances. Only the PVR B trace shows correlation between arrivals and seek distances.

plications such as videolaudio streaming with an almost de- In Figure 9, we plot the ACF of the departure process and terministic access pattern the dependence structure between the corresponding interarrival process for three of the CE interarrival times and seek distances becomes stronger than traces. Notc that the PVR B and Game Console traces have in other environments and should not be ignored in model- the same structure of the interanival and the departure pro- ing and synthetic workload generation. cesses. For the PVR A trace, the correlation structure of the

departure process is stronger than for the arrival process, indicating burstiness in the service process itself.

The results of Figure 9, together with the rest of the results presented in this paper, indicate that the CE environment is quite challenging because, similarly to the enterprise environment, exhibits strong burstiness but it is sup- ported by disk drives that are used and designed to be used in the less challenging desktop environment.

Figure 9. Autocorrelation function of the departure and interarrival times for three of the CE traces.

The departure process of all traces under evaluation matches closely the arrival process that we described in Sec- tion 4. The reason for the close match is the low load as well as the small variability in the service process (see Table 2).

6 Conclusions and Future Work

In this paper, we evaluated characteristics of disk drive's workloads in three different environments, where disk drives are used extensively, i.e., enterprise systems, desktop computers, and consumer electronic devices. With our analysis, we intend to aide the disk-drive modeling, in general, and disk-drive synthetic workload generation, in particular. The accuracy of the latter is critical because due to their complexity, most of analysis and evaluation in disk drives is done via simulation.

Third International Conference on the Quantitative Evaluation of Systems (QEST'06)0-7695-2665-9/06 $20.00 © 2006Authorized licensed use limited to: Seagate Technology. Downloaded on November 25, 2008 at 10:14 from IEEE Xplore. Restrictions apply.

Our evaluation indicated that disk drives although mostly underutilized, operate under bursty traffic in all environments. Both interarrival times and the request locations on the disk exhibit long-range dependence. Disk drive READ traffic shows different characteristics from WRITE traffic. This is attributed to the different handling that WRITES and READS obtain from the application, file system, and/or RAID controllers. Usually the resulting overall process (ei- ther the interarrival times or the seek distances) is dominated by the traffic stream that contributes the largest portion of the overall traffic.

Our evaluation showed that the enterprise environment continues to be more challenging than the desktop environment. This justifies the use of specialized and high performing SCSI disk drives in enterprise environments. However, the new emerging CE applications generate disk drive workloads that, similarly to enterprise workloads, exhibit strong burstiness in various aspects. This stresses the importance of deriving specialized algorithms to increase disk drive ef- ficiency in CE environments where the amount of available resources is less than in the enterprise environment.

Acknowledgments

We would like to thank James Dykes, Jinglei Li, Scott Borton for all their effort on collecting and parsing these traces and making them available to us.

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