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Storage Networks

External Memory Algorithms and Data Structures. Storage Networks. How to Handle Heterogeneity. January 24th, 2005 ETH Z ürich. B álint Miklós. Storage Networks – Motivation. What Storage Networks are?. Persistent Storage – Hard Disks

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Storage Networks

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

  2. Storage Networks – Motivation 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 2

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

  4. Storage Networks – Motivation 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? 4

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

  6. Storage Networks Outline • Model • AdaptRaid • HERA • RIO • Conclusions 6

  7. Storage Networks – Model 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 7

  8. Storage Networks – Model 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 8

  9. Storage Networks Outline • Model • AdaptRaid • HERA • RIO • Conclusions 9

  10. Storage Networks – Heterogeneity 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

  11. Storage Networks – AdaptRaid 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 www.raidrecoveryguide.com

  12. Storage Networks – AdaptRaid AdaptRaid 0 • Extend RAID layout for heterogeneity [Cortes, Labarta] • 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 12

  13. Storage Networks – AdaptRaid AdaptRaid 0 – Reducing Variance • 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 13

  14. Storage Networks – AdaptRaid AdaptRaid 5 • 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 14

  15. Storage Networks – AdaptRaid AdaptRaid 5 – Small-write Solution • 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 15

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

  17. Storage Networks – AdaptRaid AdaptRaid – generalization • 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. Storage Networks – AdaptRaid AdaptRaid – summary • 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. Storage Networks Outline • Model • AdaptRaid • HERA • RIO • Conclusions 19

  20. Storage Networks – HERA 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. 20

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

  22. Storage Networks – HERA 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) 22

  23. Storage Networks – HERA 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 23

  24. Storage Networks Outline • Model • AdaptRaid • HERA • RIO • Conclusions 24

  25. Storage Networks – RIO 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. 25

  26. Storage Networks – RIO 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 26

  27. Storage Networks – RIO 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. 27

  28. Storage Networks – RIO 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 28

  29. Storage Networks – RIO Heterogeneous RIO – BSR • ndisks (Di): • Capacity: Ci • Bandwidth: Bi • Total capacity: • Total bandwidth: • Bandwidth space ratio (BSR): • BSR is a hint how much load disk can take 29

  30. Storage Networks – RIO 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? 30

  31. Storage Networks – RIO 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 : -> 31

  32. Storage Networks – RIO 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 32

  33. Storage Networks Outline • Model • AdaptRaid • HERA • RIO • Conclusions 33

  34. Storage Networks – Conclusions 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) 34

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

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