QoS-Aware Storage Virtualization for Cloud File Systems. Christoph Kleineweber (Speaker) Alexander Reinefeld Thorsten Schütt. Zuse Institute Berlin

Transcription

1 QoS-Aware Storage Virtualization for Cloud File Systems Christoph Kleineweber (Speaker) Alexander Reinefeld Thorsten Schütt Zuse Institute Berlin 1

2 Outline Introduction Performance Models Reservation Scheduling Evaluation Conclusion 2

3 Introduction Cloud Storage Different interfaces E.g. block device, object store, database, file system Shared infrastructure Multi tenants Heterogeneous hardware Different devices (e.g. HDDs and SSDs) Multiple generations Different customers' requirements Capacity Throughput (MB/s, IOPS) Different access patterns (sequential, random) 3

4 XtreemFS Architecture Object based file-systems Separation of data and metadata storage Scale capacity and performance by adding / removing OSDs 4

5 Quality of Service Extensions Application and device Modeling Classify applications Determine OSD performance before usage Reservation scheduling Find appropriate OSDs for a customer Reservation enforcement (future work) Proportional-fair-share queue on OSDs 5

6 Application and Device Behavior Applications have different access patterns Sequential Random Performance depends on pattern and device Sequential vs. random HDDs vs. SSDs OSD stack causes uncertainties Local file system properties (Multiple) caches Aging effects Use empirical performance models 6

7 Device Benchmarks (HDD) Sequential Random Mixed 7

8 Device Performance - Observations Sequential throughput decreases with a growing number of concurrent streams on HDDs Almost constant random performance for HDDs and SSDs Mixed workloads result in performance degradation on HDDs SSDs are insensitive to mixed workloads and concurrent access 8

9 Reservation Scheduling Find sufficient OSDs for reservation Requirements: capacity, sequential throughput (MB/s), random throughput (IOPS) OSDs have limited resources OSD resources depend on current schedule Performance can be affected by Selecting OSDs with fitting capabilities Number of reservations per OSD Striping over multiple OSDs Minimize used OSDs 9

10 Multi-Dimensional Bin-Packing Formulate reservations and OSDs as vectors Each dimension represents one resource (capacity, seq. throughput, random throughput) Sum of reservations must not exceed OSD vector Using Toyoda's algorithm to solve bin-packing Find OSD with minimized angle between OSD vector and sum of reservation vectors Online problem 10

11 Scheduling Algorithm 11

12 XtreemFS Integration Using logical volume per reservation Virtual file system with own namespace Implemented new reservation scheduler service Requests OSDs from directory server Determines OSD performance by initial benchmarks Computes schedule Limits OSDs per volume by setting OSD selection policy Reservations are enforced on OSDs by adding proportional fair-share queue (future work) 12

13 Evaluation - Setup Discrete event based simulation Reservations created at a rate of 0.5 Random and sequential reservations each with probability 0.5 Capacity, throughput and lifetime exponentially distributed with mean of 100 (GB, MB/s, IOPS, steps) Storage cluster of SSDs and HDDs Performance characteristics from device benchmarks Baseline Allocating dedicated OSDs for sequential access reservations Random access reservations may share OSDs 13

14 Evaluation Saved OSDs over Time 14

15 Evaluation Saved OSDs vs. Active Reservations 15

16 Conclusion Object-based file systems are well suited for cloud storage QoS-aware scheduling algorithm reduces resource usage while guaranteeing performance Detailed knowledge about device characteristics is necessary 16

17 Questions? Website: xtreemfs.org Repository: github.com/xtreemfs Mailinglist: 17