YouChoose: A Performance Interface Enabling Convenient and Efficient QoS Support for Consolidated Storage Systems

Transcription

1 YouChoose: A Performance Interface Enabling Convenient and Efficient QoS Support for Consolidated Storage Systems Xuechen Zhang Yuhai Xu Song Jiang The ECE Department Wayne State University Detroit, MI The 27th IEEE Symposium on Massive Storage Systems and Technologies(MSST '11)

2 Outline Introduction Related Work Proposed Scheme Performance Evaluation Conclusions

3 Introduction (1/5) Applications become more data intensive Scientific applications may analyze large data sets Internet search and E-commerce rely on efficient data access Applications performance highly depends on I/O service quality Advantages of consolidated storage system High utilization due to resource sharing Cost-effectiveness of centralized management Low operating cost however, lack of an effective performance interface through which users can present their goals on quality of I/O service (QoS)

4 Introduction (2/5)

5 Service Level Agreement (SLA) Introduction (3/5)

6 Introduction (4/5) many aspects of the I/O service are so dynamic that any static bounds cannot capture the real performance demands I/O requests can be very bursty requests in the burst period share the same latency bound with those in the quiet period? request size can be highly variable one common latency bound for small and large requests? spatial locality of requests can vary one common throughput bound for random and sequential requests? solution: Use Reference Storage System (RSS) as Performance Interface

7 Introduction (5/5)

8 Related Work (1/2) Research on Characterizing Storage Systems Performance there are 3 methods to characterize performance behaviors, namely, analytic device modeling, simulation, and black-box modeling Worthington et al. developed DiskSim, which represents the state of the art in disk simulation DiskSim models almost all performance-relevant components of a disk, including device drivers, buses, controllers, and caches

9 Related Work (2/2) compared with simulations, analytic models are much faster but cannot capture as many details as simulators black-box modeling, without assuming the knowledge of storage system s internal components or algorithms the training data set are fed into a machine learning model and then predict its response to an input the accuracy of the method relies on the selection of training data set and design of feature vectors

10 Proposed Scheme (1/5) Interpret RSS for the I/O scheduler to implement the interface use the classification and regression tree (CART) machine-learning model to predict known for its efficiency and accuracy predict what the latency of an arriving request is if it was received by RSS Efficiently implement the RSS interface meet simultaneously RSS requirements for different VSDs able to exploit request locality for system efficiency Migrate virtual storage devices (VSDs) for high device utilization different disk arrays exhibit various efficiency in hosting VSDs migrate VSDs to host arrays for high efficiency

11 feature vector: the measures includes request size, on-disk location of requested data, and the gap between the locations of two consecutive requests

12 Proposed Scheme (3/5) N+1 clocks N reference clocks (ref_clock) for N VSDs 1 wall clock (wall_clock) when the stream is considered for scheduling if its request is dispatched, then ref_clock += ref_service_time no pending requests, then ref_clock = wall_clock 09:00 09:20 09:40 10:20 09:20 10:00 09:05 09:15 09:25 09:10 09:13 09:16 09:22 09:19 09:28 10:00 10:00

13 Proposed Scheme (4/5) interference of multiple VSDs hosted on the same physical system can be a performance barrier YouChoose batches requests from the same VSD into time slots and schedules requests from the same time slot

14 Proposed Scheme (5/5) Migrating VSDs goal: to make the host system most efficiently used so as to accommodate as many VSDs as possible random workload sequential workload if currently VSD1 on HDD array A and VSD2 on SSD array, both arrays can significantly reduce their loads if we switch the hosts of the two VSDs VSD4 is insignificant in terms of resource consumption, and should be excluded from migration

15 Performance Evaluation (1/7) Disk arrays simulated by DiskSim QUANTUN_TORNADO with 10025RPMs and 1.245ms (fast disk) SEAGATE_ST32171W with 7200RPMs and 1.943ms (slow disk) Synthetic traces request size: 4 KB spatial locality from 0% to 100% the probability of two consecutive requests for contiguous data four Real-world I/O traces OpenMail: collected on a production system running the HP OpenMail WebSearch: collected from a popular search engine Financial: collected when an OLTP application runs in a financial institute VideoStreaming: collected when playing movies ( sequential reads)

16 Performance Evaluation (2/7) accuracy of the RSS interface the request s relative error, which is the ratio of the absolute error between the service time of each request predicted by the model and service time given by the RSS Fig(a) the average relative error for all the requests is 24% Fig(c) when the window size is 0.08s, more than 85% of the relative errors are smaller than 15%

17 Performance Evaluation (3/7) the accuracy of the model is acceptable with 80% of the errors less than 30% even if without sample workload

18 Performance Evaluation (4/7) impact of spatial locality Fig(a) on dedicated RSS Fig(b) on shared storage with YouChoose Fig(c) on shared storage using the 100 IOPS bound result 1: throughput is correlated to the workload s spatial locality result 2: YouChoose faithfully implements the performance interface specified with a chosen RSS, independent of spatial locality

19 Performance Evaluation (5/7) performance isolation Fig(a) on dedicated RSS Fig(b) on shared storage with YouChoose Fig(c) on shared storage using 100 IOPS bound workload 1: three segments of requests, each with a differing spatial locality (100%, 75%, and 0%), running on one VSD workload 2: 75% spatial locality and runs on another VSD throughput of workload is accordingly reduced whenever workload 1 at 165 th second, workload 2 ends, host can use reduces its spatial locality 100% 75% 0% the released resource to serve workload 1

20 Performance Evaluation (6/7) performance isolation Fig(a) real-world workloads on dedicated RSS Fig(b) real-world workloads workload 1: segments of WebSearch trace workload 2: segments of OpenMail, WebSearch, and Financial, one-after-one OpenMail WebSearch Financial

21 Performance Evaluation (7/7) random migration threshold = 10% * (1) 31.0% % = 16.5% > migration threshold = 10% (2) SSD Array spare capability = 100% % % = 71.3% (3) do migration, VSD7 is migrated to SSD Array sequentiality factor = 0.3 and 0.7, 24% and 20% more VSDs can be held in the system tradeoff between VSD migration and the improved efficiency

22 Conclusions Introduce Reference Storage System (RSS) as the performance interface Dynamic access behavior is well accommodated in the interface Use the machine learning technique to implement the RSS interface Achieve system efficiency with batched request scheduling