Performance Analysis and Optimization of the Hurricane File System ...

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CHAPTER 7. MACROBENCHMARK 75 avg # cycles per processor 6e+10 4e+10 2e+10 0e+00 2 4 6 8 10 12 # processors Unoptimized + logging Unoptimized Figure 7.8: Web server – unoptimized +logging. avg # cycles per processor 2e+09 1.5e+9 1e+09 5e+08 0e+00 2 4 6 8 10 12 # processors Optimized 2 + logging Optimized 2 Figure 7.9: Web server – optimized 2 +logging. avg # cycles per processor 2e+11 2e+11 1e+11 1e+11 5e+10 0e+00 2 4 6 8 10 12 # processors Unoptimized +logging +disk Optimized 2 +logging +disk Figure 7.10: Web server – +logging +disk latency. configuration was not intended to be the final parameters. Cache capacity misses may have been the culprit. Since the number of hash lists was reduced by a factor of 4, the remaining hash lists may have become 4 times longer. This change would have led to lengthier hash list traversals that accessed more cache entries more frequently, leading to a greater chance that the hash list elements were not resident in the secondary physical caches. The resulting additional contention on the memory bus from each processor could have led to the scalability problems seen in Figure 7.7. 7.3.3 Web Logging Web logging was implemented to create a slightly more realistic scenario and determine the impact of contending read and write requests to disks. To simulate efficient Web logging, a new block was written to a Web log file every 50 requests, emulating the actions of writing a file block to disk once it has been filled with Web logging data. We used 80 bytes as the typical size of a Web log entry. Each thread appended to a Web log file located on its designated exclusive disk. We believe this distributed logging technique would naturally be used in the real world under such a scenario, since using a single global log file located on a single shared disk would be quite naive and bring an obvious imbalance to the system. Results for the unoptimized and optimized versions are shown in Figure 7.8 and Figure 7.9. According to these graphs, Web logging did not affect file system scalability. The microbenchmark results for reading and writing were accurately reflected under this workload. 7.3.4 Web Logging and 15 ms Disk Latency In addition to Web logging, a 15 ms disk access latency was introduced to determine the approximate impact of disk performance on file system scalability. Since disk accesses were made at page granularity,
CHAPTER 7. MACROBENCHMARK 76 avg # cycles per processor 3e+09 2e+09 1e+09 0e+00 2 4 6 8 10 12 # processors Unoptimized user-level Optimized 2 user-level Optimized 2 server-level Figure 7.11: Web server – user-level. avg # cycles per processor 1e+10 8e+09 6e+09 4e+09 2e+09 0e+00 2 4 6 8 # processors Unoptimized user-level (1st run) Optimized 2 user-level (1st run) Figure 7.12: Web server – user-level, 1st run. every 4096 bytes of disk transfer suffered a 15 ms access latency. The results, shown in Figure 7.10 7 , indicate that there were scalability problems with the unoptimized version and that the optimizations were effective in improving scalability. More importantly, these results demonstrate that the optimizations were applicable to non-zero latency disks, despite the threat that all scalability benefits would be masked by disk latency. 7.4 User-Level Experiments To provide a more realistic execution environment, the Web server simulator was executed as a user-level application in a separate address space instead of within the file system. Due to time constraints, zero latency disks were used and Web logging was disabled. This configuration introduced a very different operating environment compared to the Web server simulator running from within the file system. Direct communication with the file system was no longer available. As a user-level application, the Web server simulator accessed the file system using user-level I/O libraries, which communicated with the VFS and the FCM using IPC mechanisms. These components then communicated with the file system using IPC. Execution at the server-level did not require these steps since the file system functions could be called directly. The advantage of execution at user-level was that the FCM cached file data while the VFS cached name tree and file attribute meta-data. The user-level configuration allowed a larger set of the operating system components to be stressed. 7 Due to time constraints, results for the unoptimized 8 and 12 processor configuration have not been obtained.
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Performance Analysis and Optimizati
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4.6 Measurements Taken and Graph In
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CHAPTER 1. INTRODUCTION AND MOTIVAT
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CHAPTER 1. INTRODUCTION AND MOTIVAT
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Page 95 and 96: Bibliography [1] Gene M. Amdahl. Va
Page 97 and 98: BIBLIOGRAPHY 88 [33] David Kotz, So
Page 99: BIBLIOGRAPHY 90 [71] Keith A. Smith
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Performance Analysis and Optimization of the Hurricane File System ...

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