1. Storage Networks How to Handle Heterogeneity Bálint Miklós January 24th, 2005 ETH Zürich External Memory Algorithms and Data Structures

2. What Storage Networks are? Persistent Storage – Hard Disks Device capacity is doubled every 14-18 months – data grows faster Use many disks Need to protect, access, and manage the ever-growing volume of storage assets Storage Networks – Motivation 2

3. Hardware Failures Storage Networks – Motivation Trace collected from the Internet Archive (March 2003) courtesy of David Pease (UCSC) & Kelly Gottlib 3

4. Heterogen Storage Networks Increasing system speed, capacity: add new disks New disks usually have different characteristics than the older disks in the system. Many modern storage systems are distributed: Ethernet, FibreChannel. How to exploit this heterogeneity? Storage Networks – Motivation 4

5. Goal Storage system requirements: –space and access balance –availability –resource efficiency –access efficiency –heterogeneity –adaptivity –locality Very difficult to meet ALL requirements. Storage Networks – Motivation 5

6. Outline Model AdaptRaid HERA RIO Conclusions Storage Networks 6

7. What Model to Use? Why not to use the layout of external memory algorithms? –We need solution for all the (sub)problems –One has to bypass operating system: complex task Therefore different abstraction level: –Set of disks characterized by capacity and bandwidth –Connection network is unrestricted: e.g. SCSI, P2P Storage Networks – Model 7

8. Model assumptions Disk access patterns generated by file system (OS) Difficult to predict these and can change Assume uniform pattern, our goal is to distribute data evenly Storage Networks – Model 8

9. Outline Model AdaptRaid HERA RIO Conclusions Storage Networks 9

10. Heterogeneous Storage Networks Straightforward solution: –Clustering disks according their characteristics –We can have many clusters –Easy to extend –New, faster do not improve overall response time Randomized batched solution [Sanders] : –Map randomly data to disks –Schedule a batch of accesses by solving a network flow problem –Unfeasible for large systems: many flow problems to be solved –Batch like behavior is a disadvantage. 10 Storage Networks – Heterogeneity

11. RAID Redundant Array of Inexpensive Disks RAID level 0: –Striping data across a set of disks RAID level 5: –Add a redundancy block per stripe –Distribute redundancy information evenly on every disk 11 Storage Networks – AdaptRaid www.raidrecoveryguide.com

12. AdaptRaid 0 12 Storage Networks – AdaptRaid Basic idea: –Load each disk depending on its characteristics First solution: –Use all disks like in RAID0 until smallest disk is full –Then, discard full disks, and continue the same way –Distribution continues until all disks are full Lower portion of address space has better access times Extend RAID layout for heterogeneity [Cortes, Labarta]

13. AdaptRaid 0 – Reducing Variance 13 Storage Networks – AdaptRaid Reduce variance: –Algorithm temporarly assumes that disks are smaller. –Repeat pattern more times Stripes in a Pattern (SIP) defines the size of the pattern and the degree of variance Each disk has the same number of blocks like before

14. AdaptRaid 5 14 Storage Networks – AdaptRaid Similar idea, but one block is used for parity information Difference: A write implies updating of the parity. If not all the blocks in the stripe are written, a write needs additional read: small-write problem

15. AdaptRaid 5 – Small-write Solution 15 Storage Networks – AdaptRaid Reference stripe: OS assumes to be a full stripe Size of every stripe is a divisor of the reference stripe Logically three steps: –Decrease strip size –Distribute evenly empty space on all disks –Apply Tetris like method to fill empty blocks

16. AdaptRaid 5 – variance reduction Storage Networks – AdaptRaid We can use similar variance reduction like in AdaptRaid 0: –Repeat more times a smaller pattern 16

17. AdaptRaid – generalization Storage Networks – AdaptRaid What if bigger disks are not the faster ones? Until now we tried to use all blocks in a disk, now we want to use less blocks on slow disks Utilization Factor (UF): –0..1 value per disk UF can be set based: –disk size (until now) –performance 17

18. AdaptRaid – summary Storage Networks – AdaptRaid Decide UF for every disk: –How much we want to load a disk Decide SIP for the system: –How big the pattern is Performance: Adaptivity Speedup AdaptRaid 0: RAID 0 8%-35% AdaptRaid 5: ? < 30% Performance measured by simulators. 18

19. Outline Model AdaptRaid HERA RIO Conclusions Storage Networks 19

20. Heterogeneous Extension of RAID Disk merging tehnique Disks are partitioned into logical disks Logical disks have the same bandwidth and capacity We group logical disks in G parity groups We have G homogeneous systems. Storage Networks – HERA 20

21. Heterogeneous Extension of RAID Constraint: Each logical disk in a parity group should map to different physical disk Storage Networks – HERA 21

22. Heterogeneous Extension of RAID Read: online load balancing algorihtm directs request for a block to the disk with the least loaded disk. Every disk has a queue with all reads and deadlines. Deliver requested blocks based on deadline, and location on disk (to minimize seek-time overhead) Storage Networks – HERA 22

23. Heterogeneous Extension of RAID The availability is almost as good as the homogeneous case (RAID 5). But much more flexible than RAID 5. Performance relies on logical disk distribution, which is the task of administrator The authors recently proposed a configuration planning algorithm which optimizes for bandwidth and storage: [Zimmermann, Ghandeharizadeh: Highly Available and Heterogeneous Continuous Media Storage Systems] December 2004 Storage Networks – HERA 23

24. Outline Model AdaptRaid HERA RIO Conclusions Storage Networks 24

25. Random I/O Mediaserver Randomized distribution strategy Concentrates on delivering multimedia objects. Optimized for real-time reading: –Video on demand –3D interactive virtual world navigation –Interactive scientific visualization Idea: place data unit on a random disk at a random position. This will insure a long term load balance. Storage Networks – RIO 25

26. Homogeneous RIO – Data Placement A multimedia object is composed of a sequence of constant size data block. Data block is placed on random disk on random location -> long term load balancing By replicating a fraction of the data blocks, we allow short term balancing Storage Networks – RIO 26

27. Homogeneous RIO – Read Scheduler All reads have a deadline. Non real-time request have infinite deadline. Request for a block is routed to the disk with the least load A disk serves more blocks request in a cycle: –A number of blocks are selected from the disk request queue –The selected requests are reordered according to their location on disk to minimize the seek-time overhead and serviced. Storage Networks – RIO 27

28. Heterogeneous RIO – Data Placement Place data to a disk with probability proportional to its size: Probability to place data on disk: Note that: Disk capacity increasing faster than disk bandwidth -> faster, bigger disks are going to be bottleneck Storage Networks – RIO 28

29. Heterogeneous RIO – BSR n disks ( D i ): –Capacity: C i –Bandwidth: B i Total capacity: Total bandwidth: Bandwidth space ratio (BSR): BSR is a hint how much load disk can take Storage Networks – RIO 29

30. Heterogeneous RIO – Clusters Goal: redirect load from small BSR disks to higher BSR disks. Group disks in clusters based on their BSRs. Low BSP clusters would have high load. How much replication do we need to sustain a certain load? Storage Networks – RIO 30

31. Heterogeneous RIO – Replication Factor We want to sustain a maximum load of Data without replicas: Maximum load on a cluster is: To use all bandwidth we need : -> Storage Networks – RIO 31

32. Heterogeneous RIO – Summary Randomized data placement Read scheduler to optimized read bandwidth Based on disk characteristics we need different replication factor to sustain certain bandwidth Authors claim that in a few years 10% to 40% replication is sufficient to allow to use the full aggregate bandwidth of the network Storage Networks – RIO 32

33. Outline Model AdaptRaid HERA RIO Conclusions Storage Networks 33

34. Conclusions All three methods concentrate on optimizing bandwidth and space utilization. Adaptivity is hard to achieve AdaptRaid and HERA –Deterministic –Extend homogeneous RAID –AdaptRaid 5 wastes space? RIO –Randomized –How fast is read scheduler? –The only one where the autors showed a real-life implementation (Virtual World Data Center) Storage Networks – Conclusions 34

35. Storage Networks Thank You! Questions? Bálint Miklós 35