1 PERFORMANCE ANALYSIS OF SUPERCOMPUTING ENVIRONMENTS. Department of Computer Science, University of Illinois at Urbana-Champaign

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1 1 PERFORMANCE ANALYSIS OF TAPE LIBRARIES FOR SUPERCOMPUTING ENVIRONMENTS Ilker Hamzaoglu and Huseyin Simitci Department of Computer Science, University of Illinois at Urbana-Champaign {hamza, Abstract: In this paper, we analyzed the performance of various roboticallyaccessed tape libraries for supercomputing workloads. We used an event-driven simulator to conduct the performance analysis, and we considered the workloads at the National Center for Atmospheric Research (NCAR) and the National Center for Supercomputing Applications (NCSA). The simulation results showed that tape libraries with adequate performance parameters perform quite well on the supercomputing workloads, thus they are promising to be an important part of the mass storage systems used in the supercomputing environments. The results also showed that the tape drive forward search and rewind rate is the bottleneck in the tape libraries used under the supercomputing workloads. Keywords: tertiary storage, tape library, supercomputing, performance analysis, simulation 1.1 INTRODUCTION Over the last decade, as the performance of processors is signicantly increased, applications that involve huge amounts of data, e.g. computational science (2, 16, 17) and data mining (12, 13), became commonplace. It is not cost eective to use the secondary storage devices as the only storage devices to cope with this much of data. Instead, tertiary storage devices (3, 11) should be used to store much of this data. This research was supported in part by the Department of Computer Science, University of Illinois at Urbana-Champaign. 1

2 2 Random Access Memory (RAM) primary storage Magnetic Disk secondary storage Magnetic Tape Optical Disk tertiary storage Figure 1.1 Storage System Hierarchy Figure 1.1 shows a typical storage system hierarchy. At the top of the hierarchy is primary storage which usually consists of semiconductor random access memory (RAM) used for caches and main memory. The next level is secondary storage which usually consists of magnetic disk devices. Tertiary storage which usually consists of magnetic tapes and optical disks lies at the bottom of the storage hierarchy. The top level of the hierarchy has the highest cost per byte, and it is the smallest and the fastest of all three levels. As we go down in the hierarchy, the storage cost signicantly decreases, whereas the size and the access latency signicantly increase. Although tertiary storage includes optical tapes and holographical storage as well as magnetic tapes and optical disks, magnetic tapes and optical disks are the two major types of commonly used tertiary storage devices. Tapes have higher latency then optical disks, but their bandwidth is much higher. Since the les in supercomputing applications tend to be large, the dierence in transfer time between tape and optical disk is substantial. Therefore, for supercomputing applications, magnetic tapes are more suitable. In supercomputing environments such as the National Center for Atmospheric Research (NCAR) and National Center for Supercomputing Applications (NCSA), sustaining a good response time at a reasonable cost became a severe problem as the data grows at a rate of several terabytes (TB) per year (9, 14). To cost eectively store this much of data, an appropriate mass storage system that can handle the workloads of these computing centers should be used. In this paper, we will use the terms mass storage system and archive system interchangeably. The structure of a typical mass storage system is shown in Figure 1.2. Although early mass storage systems used in the supercomputing environments contained only manually loaded tape cartridge systems, this is no longer cost eective for obtaining a reasonable performance when dealing with terabytes of data. Robot-controlled tape libraries, however, are promising to be a cost

3 PERFORMANCE ANALYSIS OF TAPE LIBRARIES 3 Computing Environment I/O Processors Disk Storage Unit Robot-controlled Tape Library Manually Loaded Tape Drives Figure 1.2 Mass Storage System Structure eective solution for handling huge amounts of data (1, 5). A robot-controlled tape library consists of a storage rack for tape cartridges, a number of tape drives, and a robot arm that moves the cartridges between the storage rack and the tape drives. Since these tape libraries can handle massive amounts of data without user intervention, they reduce the data management cost and the response time, and they increase the data reliability. In this paper, we analyzed the performance of various robotically-accessed tape library congurations for supercomputing workloads. We also assessed the impact of the performance of various tape library components on the overall performance of the tape libraries for supercomputing workloads. We carried out the performance analysis using the simulation technique, because it is more accurate than analytical modeling and it provides easier trade-o evaluation, i.e. comparing the performance of alternative tape library congurations under a variety of workloads, than measurement (8). We used an event-driven simulator (3) to simulate the behavior of various tape library congurations under three dierent supercomputing workloads; NCAR Mass Storage System workload (14), NCSA Common File System workload (9), and NCSA UniTree File Archive System workload (7, 18). The simulation results showed that tape libraries with adequate performance parameters perform quite well on the supercomputing workloads, thus they are promising to be an important part of the mass storage systems used in the supercomputing environments. The results also showed that parallelism in tape libraries, i.e. having more than one tape drive, increases the performance signicantly. The robot arm speed has less impact on the overall tape library performance than the tape drive speed. The tape drive search and rewind rate has more impact on the overall tape library performance than the tape drive transfer rate. In other words, the tape drive forward search and rewind rate is the bottleneck in the tape libraries used under the supercomputing workloads.

4 4 The rest of the paper is organized as follows. The previous work in this area is presented in Section 1.2. The simulation framework including the supercomputing workloads, the tape libraries, and the tape library simulator used in the simulations, is described in Section 1.3. The simulation results are presented in Section 1.4. Finally, Section 1.5 presents the conclusions. 1.2 PREVIOUS WORK In this section, we will present the previous work about the performance analysis of tape libraries and the previous work about characterizing the workloads at the supercomputing environments. Chervenak's work on the performance analysis of tertiary storage devices presented an overview of the current tertiary storage technology, including magnetic tapes, holographic storage and optical tapes (3, 4). She dened a simulation model and developed an event-driven simulator for simulating the tertiary storage devices. She simulated the behavior of various tertiary storage systems under three dierent workloads; sequential workload, video server workload and digital library workload. She found that in order for the tertiary storage libraries to be eective for these workloads, the ones that contain large number of drives with satisfactory bandwidth should be used. She also performed a simulation study to assess the benets of tape library striping for various workloads, and she found that although striping is quite eective for sequential workloads, it performs poorly on the workloads that are not strictly sequential because it creates contention for a small number of tape drives. Johnson developed an analytical performance model of a robot-controlled tape library, and he analyzed the performance of various tape library congurations using this performance model (1). Johnson and Miller measured the performance of several low-end and high-end tape drives and they developed an analytical model of a tape drive using the performance values obtained from these measurements (11). Pentakalos et al. developed an approximate closed queuing network model of a hierarchical mass storage system and they analyzed the eect of workload intensity increase, use of le compression, and use of le abstractions on the mass storage system performance using this model (15). Jensen and Reed presented empirical data on the usage of the Common File System (CFS), the previous mass storage system used in NCSA (9). They analyzed the le transactions on the CFS based on 3 years of transaction records, and they quantied the le access patterns in terms of the request sizes, transaction rates and le interreference times. They found that small disks in the archival storage received most of the transactions, but these constituted only a small minority of the data, and the cartridge tapes received the fewest transactions and the largest volume data. 1% of all CFS transactions accounted for over 75% of the CFS data volume. The mean time between transactions was computed as 39 seconds, though there were many bursts of CFS requests with much less interarrival times. Finestead and Yeager explained the structure of the UniTree File Archive System (UFAS), the current mass storage system used in NCSA (7). UFAS is

5 PERFORMANCE ANALYSIS OF TAPE LIBRARIES 5 Table 1.1 File Access Pattern Parameters for NCAR MSS and NCSA CFS Workloads Parameter NCAR,MSS NCSA,CFS File interreference time 161 sec 329 sec Request placement distribution ZIPF ZIPF Percentage write accesses 33% 34% Request size distribution Uniform Uniform Request size mean 8 MB 17 MB an archival storage system that automatically migrates les from local disks to tapes when disk space becomes low or les have not been accessed in a certain amount of time. UFAS, currently, uses only manually loaded tape drives (6). Miller and Katz examined a dierent supercomputing environment, NCAR Mass Storage System (MSS) (14). They analyzed 24 months of trace data gathered from system logs. They found that although 66% of all the transactions were received by the archive disks, these transactions only accounted for the 1% of the data trac. On the other hand, even though only 2% of the transactions were received by the manually loaded tape cartridge system, these transactions constituted the 66% of the data trac in the archive system. They found that the average interval between mass storage system requests was 18 seconds. However, 9% of all references were followed by another one in less than 1 seconds which shows that I/Os were clustered. 1.3 SIMULATION FRAMEWORK We simulated the behavior of two dierent tape libraries under the supercomputing workloads using an event-driven tape library simulator. In this section, we will describe the supercomputing workloads, the tape libraries, and the tape library simulator used in this work Supercomputing Workloads We analyzed the performance of various tape library congurations for NCAR Mass Storage System (MSS), NCSA Common File System (CFS), and NCSA UniTree File Archive System (UFAS) workloads. All three workloads are determined by using the le access requests received by the tape archive part of the corresponding mass storage system. We determined the NCSA CFS and the NCAR MSS workloads by using the le access patterns presented in (9) and (14) respectively. The le access pattern parameters characterizing these workloads are presented in Table 1.1. For both systems, we assumed a Uniform request size distribution, and a Zipf request placement distribution. Since, in both systems, the input/outputs are clustered and for the rereferenced les the second access came soon after the rst one, we used the Zipf distribution to imitate this behavior. Because this provides a workload with access locality that follows the Zipf's Law distribution

6 6 Table 1.2 Performance Parameters for EXB85 and DST65 Tape Drives Parameter EXB85 DST65 Tape Eject Time 15 sec 5 sec Tape Load Time 5 sec 5 sec Rewind Startup Time 2 sec 5 sec Rewind Rate (after startup) 5 MB/sec 8 MB/sec Forward Search Startup Time 15 sec 5 sec Forward Search Rate (after startup) 5 MB/sec 8 MB/sec Read Transfer Rate 5 KB/sec 15 MB/sec Write Transfer Rate 5 KB/sec 15 MB/sec Tape Capacity 5 GB 5 GB (19), i.e. the nth most popular movie of N movies is requested with probability C/n, where C = 1 / (1 + 1/2 + 1/ /N). The request arrivals are generated using a Normal le interreference time distribution with the given mean values. The standard deviation of the distribution is taken to be the same as its mean value in order to provide an irregular request arrival pattern. We determined the NCSA UFAS workload by using one week transaction records logged by Unitree (6). Request arrival times, request sizes and request types (read or write) are directly taken from the log les. Request placement information, i.e. request start block, is adapted to the simulated tape library conguration. The start block of a le in a secondary or tertiary storage system depends on many factors, e.g. le allocation policy and storage system capacity. Therefore, it's not possible to use this information directly from the log les, and it should be adapted to the capacity of the simulated storage system Tape Libraries We simulated the behavior of two dierent tape libraries, Exabyte EXB12 and Ampex DST712, under the supercomputing workloads. Exabyte EXB12 is a small low-end tape library produced by Exabyte Corporation (5). It contains four Exabyte EXB85 8mm helical scan tape drives. It can store up to 1.2 terabytes using 116 tapes. Ampex DST712 is a large high performance tape library produced by Ampex Corporation (1). It contains two Ampex DST65 19mm helical scan tape drives. It can store up to 5.8 terabytes using 116 tapes. The tape drive and the tape library models used for simulating the EXB85 and DST65 tape drives and the EXB12 and DST712 tape libraries are based on the performance parameters presented in Tables 1.2 and 1.3 respectively. Load and eject times, robot arm movement, and pick up and place times are modeled as constant values. Search and rewind times are calculated using a model consisting of a constant startup overhead followed by a linear positioning rate. We also made the same optimistic assumption as Chervenak (3) that the devices operate at streaming rates which is true for supercomputing environments.

7 PERFORMANCE ANALYSIS OF TAPE LIBRARIES 7 Table 1.3 Performance Parameters for EXB12 and DST712 Tape Libraries Tape Library Simulator Parameter EXB12 DST712 Robot movement time 2 sec 1 sec Robot pick (grab) time 2 sec 2 sec Robot place (put) time 2 sec 2 sec Number of tape drives 4 2 Number of robot arms 1 1 Number of tapes List price $1, $5, We have used the event-driven tape library simulator developed by Chervenak (3). The original simulator was a close simulator. A close simulator keeps the number of outstanding requests (concurrency) in the system constant throughout a simulation run by issuing a new request as soon as one request completes service. This way, Chervenak had a control over the workload concurrency and this allowed her to use the number of concurrent requests that can be sustained for a given response time limit as the performance metric. However, in order to simulate the tape libraries using the le access traces of applications in an actual system, there should be no restrictions on the workload concurrency and on the response time of the system. Therefore, in order to simulate the workloads accurately and to report the performance values in terms of response time without putting any restrictions on the response time itself, we modied the simulator so that it works as an open simulator. In an open simulator, requests arrive to the system without any restrictions and they leave the system after they are serviced. The new simulator either receives the requests according to an event arrival distribution as in the case of NCSA CFS and NCAR MSS workloads, or gets them directly from a given trace le as in the case of NCSA UFAS workload. We implemented a preprocessor to convert the NCSA UniTree le archive system traces into a suitable format that can be processed by the simulator. We also modied the simulator to directly accept this trace le, instead of generating requests according to an arrival distribution. The simulator is event-driven. A queue of relevant events is maintained and processed in time order. The events considered during a simulation run are arrival (request enters to system), device free (drive nishes processing a request), arm free (robot arm nishes moving a tape), and rewind and eject done (drive nishes rewinding and ejecting a loaded tape). In a simulation run, statistics are gathered only after a certain number of requests has been processed, in order to avoid using the initial transient tape library state. This improves the reliability of the results. We used a threshold value of 5 requests in our simulations. The simulator reports the tape library performance in terms of the mean response time (latency).

8 8 1.4 SIMULATION RESULTS We rst simulated the behavior of EXB12 and DST712 tape libraries under each workload. We simulated 1 request arrivals for NCAR MSS and NCSA CFS workloads, and 75 request arrivals for NCSA UFAS workload. Figure 1.3 shows the mean response times of both tape libraries for each workload. Jensen and Reed reported the response time of the NCSA CFS tape archive as 546 seconds (9). Only manually loaded tape drives are used in the NCSA CFS tape archive. The response time of EXB12 and DST712, even with 4 tape drives, is better than the NCSA CFS tape archive performance. Miller and Katz reported the response time of the NCAR MSS tape archive as 14 seconds (14). A StorageTek 44 tape library with a tape load time of 6 seconds and a transfer rate of 6 MB/sec is used in the NCAR tape archive. The EXB12 tape library, even with 16 tape drives, performed worse than the current tape archive system at NCAR. However, DST712 tape library performed better than the current tape archive system at NCAR. Finestead reported the average response time of the current NCSA UniTree tape archive as 27 seconds (6). 12 STK348 tape drives with a transfer rate of 3 MB/sec and 8 Metrum tape drives with a transfer rate of 2 MB/sec, all of which are manually loaded, are used in the Unitree tape archive. As in the case of the NCAR MSS workload, EXB12 tape library, even with 16 tape drives, performed worse than the current tape archive system at NCSA. However, DST712 tape library performed better than the current tape archive system at NCSA. These results show that tape libraries with adequate performance parameters perform quite well under the supercomputing workloads. The decrease in the response times of both tape libraries when going from a single tape drive to 4 tape drives demonstrates the importance of the input/output parallelism. However, tape drive parallelism without adequate tape drive and robot arm performance has only limited eect on the ability of a tape library to handle supercomputing workloads Improving Overall Performance of Tape Libraries In this section, we examine the eect of improving overall tape library performance by certain factors on the performance of tape libraries for supercomputing workloads. We simulated the behavior of two tape libraries that are twice as fast and ten times as fast as EXB12 tape library. The performance parameters for these tape libraries are shown in Table 1.4. We simulated three dierent tape library congurations for each speed up factor, e.g. for the speed up factor of 2, we simulated a tape library with a twice as fast tape drive as the original one and an unchanged robot arm, a tape library with a twice as fast robot arm as the original one and an unchanged tape drive, and a twice as fast tape library as the original EXB12. We simulated each tape library conguration both with 4 and 16 tape drives.

9 PERFORMANCE ANALYSIS OF TAPE LIBRARIES Number of Tape Drives 3a: NCSA CFS Workload EXB12 DST EXB12 DST Number of Tape Drives 3b: NCAR MSS Workload EXB12 DST Number of Tape Drives 3c: NCSA UFAS Workload Figure 1.3 Performance of EXB12 and DST712 Tape Libraries

10 1 Table 1.4 Performance Parameters for EXB12 Tape Library at Various Speed up Factors Speed up Original 2X 1X (EXB12) Robot movement time (sec) Robot pick (grab) time (sec) Robot place (put) time (sec) Tape Eject Time (sec) Tape Load Time (sec) Rewind Startup Time (sec) Rewind Rate (after startup) (MB/sec) Forward Search Startup Time (sec) Forward Search Rate (after startup) (MB/sec) Read Transfer Rate (KB/sec) Write Transfer Rate (KB/sec) Figure 1.4 presents the mean response times of all the tape library congurations for each supercomputing workload. In this gure, R-T (robot arm? tape drive) represents the original EXB12 tape library. 2R represents a two times faster robot arm, and 2T represents a two times faster tape drive. Similarly, 1R represents a ten times faster robot arm, and 1T represents a ten times faster tape drive. Each combination of R and T represents a corresponding tape library conguration. For example, R-1T represents a tape library with an unchanged robot arm speed which is the same as the EXB12 robot arm speed and a ten times as fast tape drive as the original EXB85. The graphs in the gure show that improving overall performance of tape libraries had a similar eect on the tape library performance for all three workloads. The simulation results show that improving the performance of robot arms slightly improved the performance of the tape libraries for supercomputing workloads. However, improving the performance of tape drives signicantly decreased the tape library response times for supercomputing workloads. This indicates that, in order to improve the eectiveness of a tape library for a supercomputing workload, improving the performance of tape drives is more cost eective than improving the performance of robot arms Improving Individual Properties of Tape Drives In this section, we examine the eect of improving individual tape drive performance parameters on the tape library performance, in particular the tape drive transfer rate and the tape drive search and rewind rate. We simulated the behavior of EXB12 tape library with four EXB85 tape drives under the supercomputing workloads by varying a single parameter at a time and keeping the others constant at their original values. This way, we assessed the eect of improving a single performance parameter on the overall tape library performance.

11 PERFORMANCE ANALYSIS OF TAPE LIBRARIES tape drives 16 tape drives R-T R-2T 2R-T 2R-2T R-1T 1R-T 1R-1T Speedup Factors 4a: NCSA CFS Workload tape drives 16 tape drives R-T R-2T 2R-T 2R-2T R-1T 1R-T 1R-1T Speedup Factors 4b: NCAR MSS Workload 2 4 tape drives 16 tape drives R-T R-2T 2R-T 2R-2T R-1T 1R-T 1R-1T Speedup Factors 4c: NCSA UFAS Workload Figure 1.4 Performance of EXB12 Tape Library at Various Speed up Factors

12 12 Figure 1.5 presents the mean response time of EXB12 tape library under the supercomputing workloads for each individual parameter improvement. The graphs in this gure show that improving individual properties of tape drives had a similar eect on the tape library performance for all three workloads. The simulation results show that improving the tape drive transfer rate slightly decreased the tape library response times for supercomputing workloads. Improving the tape drive search and rewind rate, however, decreased the tape library response times signicantly. This indicates that, in order to improve the eectiveness of a tape library for a supercomputing workload, improving the tape drive search and rewind rate is more cost eective than improving the tape drive transfer rate Ejecting and Loading Tapes at Arbitrary Positions In this section, we examine the eect of ejecting and loading tapes at arbitrary positions, rather than fully rewinding the tapes before ejecting them, on the performance of tape libraries for supercomputing workloads. This is becoming a practical way of using tape cartridges, as several tape drive manufacturers are reducing rewind and search times by implementing periodic zones on the tapes where eject and load operations are allowed (1, 5). Since Chervenak's tape library simulator (3) requires that a tape should be fully rewound before an eject operation can be performed, we modied the simulator to allow this behavior. This is achieved by keeping track of the position of each tape cartridge so that when a tape is reloaded to a tape drive, the tape block number under the tape head is known. We simulated the behavior of EXB12 and DST712 tape libraries for the NCAR MSS and NCSA CFS workloads using the new simulator. Figure 1.6 shows the mean response times of both the original and the improved tape libraries for the two workloads. The graphs in this gure show that this technique improves the tape library performance by 1% for both workloads. 1.5 CONCLUSIONS In this paper, we analyzed the performance of various robotically-accessed tape libraries for supercomputing workloads. We also assessed the impact of the performance of various tape library components on the overall performance of the tape libraries for supercomputing workloads. We used an event-driven simulator to conduct the performance analysis, and we considered the workloads at the National Center for Atmospheric Research (NCAR) and the National Center for Supercomputing Applications (NCSA). The simulation results showed that although the performance of EXB12 tape library for the NCAR MSS and NCSA CFS workloads is reasonable, it behaves poorly for NCSA UFAS workload. However, Ampex DST712 performs quite well on all three workloads. This shows that the robot-controlled tape libraries with adequate performance parameters are promising to be an important part of the mass storage systems used in the supercomputing environments.

13 PERFORMANCE ANALYSIS OF TAPE LIBRARIES Transfer Rate (MB/sec) 1 2 Forward Search/Rewind Rate (MB/sec) 5a: NCSA CFS Workload Transfer Rate (MB/sec) 1 2 Forward Search/Rewind Rate (MB/sec) 5b: NCAR MSS Workload Transfer Rate (MB/sec) 1 2 Forward Search/Rewind Rate (MB/sec) 5c: NCSA UFAS Workload Figure 1.5 Eect of Varying Tape Drive Transfer Rate and Forward Search/Rewind Rate on the Performance of EXB12 Tape Library

14 EXB12 EXB12 Arb. Pos. Eject DST712 DST712 Arb. Pos. Eject Number of Tape Drives 6a: NCSA CFS Workload EXB12 EXB12 Arb. Pos. Eject DST712 DST712 Arb. Pos. Eject Number of Tape Drives 6b: NCAR MSS Workload Figure 1.6 Performance of Tape Libraries that Eject and Load Tapes at Arbitrary Positions The results also showed that parallelism in tape libraries, i.e. having more than one tape drive, increases the performance signicantly. The robot arm speed has less impact on the overall tape library performance than the tape drive speed. The tape drive search and rewind rate has more impact on the overall tape library performance than the tape drive transfer rate. In other words, the tape drive forward search and rewind rate is the bottleneck in the tape libraries used under the supercomputing workloads. Ejecting and loading tapes at arbitrary positions, rather than fully rewinding the tapes before ejecting them, improves the performance of the tape libraries by 1%. Acknowledgments We thank to Ann Chervenak for providing us her tape library simulator. Thanks also to Arlan Finestead for providing us the NCSA UniTree log les.

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