1. Issues and Challenges in the Performance Analysis of Real Disk Arrays Elizabeth Varki, Arif Merchant, Jianzhang Xu and Xiaozhou Qiu Presented by:Soumya Eachempati

2. RAID Redundant Array of Inexpensive Disks Redundancy and Striping –Capacity –Speed (Parallelism) –Availability of storage systems Multiple disks, large caches, array controllers - implement load-balancing, request coalescing, adaptive prefetching.

3. Why - Performance Modeling Storage systems are slow. Any improvement in the performance would escalate the system performance for I/O intensive applications. Metrics used: –Response time –Throughput –Queue length

4. Approaches Analytic - quick and dirty, less expensive Simulation - more accurate, lot of effort So which method will you choose? Authors develop analytic model that includes array controller optimizations

5. Previous Work Disk array performance Efficient Caching algorithms This paper models the parallelism of disk arrays and effects of caching on performance. Presents a Baseline model with known parameter values. Extraction of array controller optimizations using the baseline model and workloads that isolate specific optimizations.

6. Disk Array Architecture

7. RAID 1/0 configuration

8. Key Components

9. Some Assumptions M jobs generate synchronous requests. At-most one request from each stream at the disk array. Each job spends some time at the CPU before submitting request to the disk array. Read only and write only requests stream. No queueing at the CPU/terminal.

10. Read Model

12. Write Model

13. Response time Array_response_time[m] = cache_response_time[m] + disks_response_time[m] Array_throughput[m] = m / (CPU_delay + array_response_time[m]) Cache_queue[m] = cache_response_time[m] * array_throughput[m] (Little’s law)

14. Response time Arrival theorem states that the number of jobs “seen” by a job upon arrival at a subsystem is equal to the mean queue length at the subsystem computed when the network has one less job. The response time of the arriving job is equal to the sum of the arriving job’s service time plus the time required to service the jobs seen ahead.

15. Baseline model parameters Disk Service time Parallelism Overhead Disk access probability Cache parameters Write model input parameters.

16. Disk Service time Very important to get an accurate estimate. Seek distance - disk scheduling policy and location of disk IO requests.

17. Disk positioning time Degree of sequentiality has minimal effect for disk_queue > 3 Disk positioning time linear function of disk_queue length for disk_queue < 3 for simplicity.

18. Cyclic dependancy Disk service time depends on disk queue length which again depends on disk service time. Assume disk queue length is an input parameter. If CPU_delay = 0 then disk_queue = M * disk_access_probability.

19. Parameters Parallel_overhead = max_position_time - disk_position_time Disk_access_probability = cache_miss_probability * num_diskIOs_per_request / stripe_width Cache_hit = 1 - (read_ahead_miss * re_reference_miss) Cache_service_time = request_size / cache_transfer_rate. Once destage_threshold is reached, writes data to all the disks at a time. –µ = stripe_width / 2 * disk_service_time

20. Array controller Optimizations Access coalescing policy Redundancy based load balancing policy Adaptive prefetching Simplest baseline model - small random read requests with CPU_delay = 0.

21. Workload Characterization

22. Setup Trace of all IO activity at the device driver level. Trace contains –I/O submission and completion times. –Logical addresses –Sizes of requests

23. Optimizations Disk I/O accesses to contiguous data on the same disk coalesced into a single disk IO. Example : 96KB striped across 3 disks with stripe unit size = 16KB. Redundancy load balancing –Single request distributed to both the disk and its mirror. (transfer time reduction) –2 different requests to the disks and its mirrors. (queue length reduction)

24. Policy effects Access coalescing and load balancing effect number of disk IO requests per request. Large request sizes > stripe_width * stripe_unit_size Num_diskIOs_per_request = stripe_width (transfer time reduction) = stripe_width / 2 for disk_queue_length reduction.

25. Access Coalescing Random workloads - caching is eliminated. Using large request sizes both the policies can be evaluated > stripe_width / 2 Adaptive prefetching: Sequential read requests - workload. Explicit read-ahead is turned off. Re-reference hit is zero.

26. Modeling Adaptive prefetching and load balancing Num_disIOs_per_request = stripe_width for request_size > stripe_width * stripe_unit_size Ceiling(requests_size / stripe_unit_size)

27. Load Balancing Workload consists of same-sized requests.

29. Write-back caching Size at-most 2 stripes. Large requests - bypass the cache.

30. Validation Model performs better for random than sequential Ios Random Reads - 3.7% Random writes - 5.5% Sequential reads - 8.8% Sequential writes - 8.0% Adaptive prefetching works well for disk queue length < 4. Disk writes and disk reads similar performance.

31. Challenges Disk parameters disk queue length affects the caching policies as well. Paucity of data.(Bus transfer rate 35% higher) Performance as a function of disk array controller optimizations, array caching policies, workload distributions along with disk service time, the stripe unit size and stripe width. Better Approximation of disk queue length.

32. THANK YOU Questions?