THE HP AUTORAID HIERARCHICAL STORAGE SYSTEM J. Wilkes, R. Golding, C. Staelin T. Sullivan HP Laboratories, Palo Alto, CA

INTRODUCTION must protect data against disk failures: too frequent and too hard to repair possible solutions: for small numbers of disks: mirroring for larger number of disks: RAID

RAID Typical RAID Organizations Level 3: bit or byte level interleaved with dedicated parity disk Level 5: block interleaved with parity blocks stored on all disks

LIMITATIONS OF RAID (I) Each RAID level performs well for a narrow range of workloads Too many parameters to configure: data- and parity-layout, stripe depth, stripe width, cache sizes, write-back policies, ...

LIMITATIONS OF RAID (II) Changing from one layout to another or adding capacity requires downloading and reloading the data Spare disks remain unused until a failure occurs

A BETTER SOLUTION A managed storage hierarchy: mirror active data store in a RAID 5 less active data This requires locality of reference: active subset must be rather stable: found to be true in several studies

IMPLEMENTATION LEVEL Storage hierarchy could be implemented Manually: can use the most knowledge but cannot adapt quickly In the file system: offers best balance of knowledge and implementation freedom but specific to a particular file system Through a smart array controller: easiest to deploy (HP AutoRAID)

MAJOR FEATURES (I) Mapping of host block addresses to physical disk locations Mirroring of write-active data Adaptation to changes in the amount of data stored: Starts RAID 5 when array becomes full Adaptation to workload changes: Hot-pluggable disks, fans, power supplies and controllers

MAJOR FEATURES (II) On-line storage capacity expansion: system switches then to mirroring Can mix or match disk capacities Controlled fail-over: can have dual controllers (primary/standby) Active hot spares: used for more mirroring Simple administration and setup: appears to host as one or more logical units Log-structured RAID 5 writes

RELATED WORK (I) Storage Technology Corporation Iceberg: also uses redirection but based on RAID 6 handles variable size records emphasis on very high reliability

RELATED WORK (II) Floating parity scheme from IBM Almaden: Relocated parity blocks and uses distributed sparing Work on log-structured file systems at U.C. Berkeley and cleaning policies

RELATED WORK (III) Whole literature on hierarchical storage systems Schemes compressing inactive data Use of non-volatile memory (NVRAM) for optimizing writes Allows reliable delayed writes

OVERVIEW Processor, RAM and Control Logic Parity Logic 2x10MB/s bus DRAM Read Cache Matching RAM NVRAM Write Cache Control Other RAM SCSI Controller 20 MB/s Host Computer

PHYSICAL DATA LAYOUT Data space on disks is broken up into large Physical EXTents (PEXes): Typical size is 1 MB PEXes can be combined to form Physical Extent Groups (PEGs) containing at least three PEXes on three different disks PEGs can be assigned to the mirrored storage class or to the RAID 5 storage class Segments are the units on contiguous space on a disk (128 KB in prototype)

LOGICAL DATA LAYOUT Logical allocation and migration unit is the Relocation Block (RB) Size in prototype was 64 KB: Smaller RB’s require more mapping information but larger RB’s increase migration costs after small updates Each PEG holds a fixed number of RB’s

MAPPING STRUCTURES Map addresses from virtual volumes to PEGs, PEXes and physical disk addresses Optimized for finding fast the physical address of a RB given its logical address : Each logical unit has a virtual device table listing all RB’s in the logical unit and pointing to their PEG Each PEG has a PEG Table listing all RB’s in the PEG and the PEXes used to store them

NORMAL OPERATIONS (I) Requests are sent to the controller in SCSI Command Descriptor Blocks (CDB): Up to 32 CB’s can be simultaneously active and 2048 other ones queued Long requests are broken into 64 KB segments

NORMAL OPERATIONS (II) Read requests: Test first to see if data are not already in read cache or in non-volatile write cache Otherwise allocate space in cache and issue one or more requests to back-end storage classes Write requests return as soon as data are modified in non-volatile write cache: Cache has a delayed write policy

NORMAL OPERATIONS (III) Flushing data from cache can involve; A back-end write to a mirrored storage class Promotion from RAID 5 to mirrored storage before the write Mirrored reads and writes are straightforward

NORMAL OPERATIONS (IV) RAID 5 reads are straightforward RAID 5 writes can be done: On a per-RB base: requires two reads and two writes In batched writes: more complex but cheaper

BACKGROUND OPERATIONS Triggered when array has been idle for some time Include Compaction of empty RB slots, Migration between storage classes (using an approximate LRU algorithm) and Load balancing between disks

MONITORING System also includes: An I/O logging tool and A management tool for analyzing the array performance

PERFORMANCE RESULTS (I) HP AutoRAID configuration with: 16 MB of controller data cache Twelve 2.0GB Seagate Barracuda disks (7200rpm) Compared with: Data General RAID array with 64 MB front-end cache Eleven individual disk drives implementing disk striping but without any redundancy

PERFORMANCE RESULTS (II) Results of OLTP database workload: AutoRAID was better than RAID array and comparable to set of non-redundant drives But whole database was stored in mirrored storage! Micro benchmarks: AutoRAID is always better than RAID array but has smaller I/O rates than set of drives

SIMULATION RESULTS (I) Increasing the disk speed improves the throughput: Especially if density remains constant Transfer rates matter more than rotational latency 64KB seems to be a good size for the Relocation Blocks: Around the size of a disk track

SIMULATION RESULTS (II) Best heuristics for selecting the mirrored copy to be read is shortest queue Allowing write cache overwrites has a HUGE impact on performance RB’s demoted to RAID should use existing holes when the system is not too loaded

SUMMARY (I) System is very easy to set up: Dynamic adaptation is a big win but it will not work for all workloads Software is what makes AutoRAID, not the hardware Being auto adaptive makes AutoRAID hard to benchmark

SUMMARY (II) Future work includes: System tuning especially Idle period detection Front-end cache management algorithms Developing better techniques for synthesizing traces