Improving capacity-performance tradeoffs in the storage tier

Data-set sizes are growing. New techniques are emerging to organize and analyze these data-sets. There is a key access pattern emerging with these new techniques, large sequential file accesses. The trend toward bigger files exists to help amortize the cost of data accesses from the storage layer, as many workloads are recognized to be I/O bound. The storage layer is widely recognized as the slowest layer in the system. This work focuses on the tradeoff one can make with that storage capacity to improve system performance. ^ Capacity can be leveraged for improved availability or improved performance. This tradeoff is key in the storage layer, as this allows for data loss prevention and bandwidth aggregation. Typically these tradeoffs do not allow much choice with regard to capacity use. This work will leverage replication as the enabling mechanism to improve the capacity-performance tradeoff in the storage tier, while still providing for availability. ^ This capacity-performance tradeoff can be made at both the local and distributed file system level. I propose two techniques that allow for an improved tradeoff of capacity. The local file system can be employed on scale-out or scale-up infrastructures to improve performance. The distributed file system is targeted at distributed frameworks, such as MapReduce, to improve the cluster performance. The local file system design is MorphStore, and the distributed file system is BoostDFS. ^ MorphStore is a file system that significantly improves performance when accessing large files by using two innovations. MorphStore combines (a) load-adaptive I/O access scheduling to dynamically optimize throughput (aggregation), and (b) utility-xiii driven replication to best use capacity for performance. Additionally, adaptive-access scheduling can be utilized to optimize scheduling of requests (for throughput) on systems with a large number of storage devices. Replication is utilized to make available high utility files and then optimize throughput of these high utility files based on system load. ^ BoostDFS is a distributed file system that allows a better capacity-performance tradeoff via inter-node file replication. BoostDFS is built on the observation that distributed file systems currently inter-node replication for availability, but provide no mechanism to further improve performance. Replication for availability provides diminishing returns on performance, this is due to saturation of locality. BoostDFS exploits the common by improving I/O performance of these local tasks. This is done via intra-node replication by leveraging MorphStore as the local file system. This technique allows for capacity to be traded for availability as well as performance, with a small capacity overhead under constant availability. ^ Both MorphStore and BoostDFS utilize replication. Replication allows for both bandwidth aggregation and availability, This work primarily focuses on the performance utility of replication, but does not sacrifice availability in the process. These techniques provide an improved capacity-performance tradeoff while allowing the desired level of availability

Data partitioning and load balancing in parallel disk systems

Parallel disk systems provide opportunities for exploiting I/O parallelism in two possible ways, namely via inter-request and intra-request parallelism. In this paper we discuss the main issues in performance tuning of such systems, namely striping and load balancing, and show their relationship to response time and throughput. We outline the main components of an intelligent file system that optimizes striping by taking into account the requirements of the applications, and performs load balancing by judicious file allocation and dynamic redistributions of the data when access patterns change. Our system uses simple but effective heuristics that incur only little overhead. We present performance experiments based on synthetic workloads and real-life traces

Data allocation in disk arrays with multiple raid levels

There has been an explosion in the amount of generated data, which has to be stored reliably because it is not easily reproducible. Some datasets require frequent read and write access. like online transaction processing applications. Others just need to be stored safely and read once in a while, as in data mining. This different access requirements can be solved by using the RAID (redundant array of inexpensive disks) paradigm. i.e., RAIDi for the first situation and RAID5 for the second situation. Furthermore rather than providing two disk arrays with RAID 1 and RAID5 capabilities, a controller can be postulated to emulate both. It is referred as a heterogeneous disk array (HDA). Dedicating a subset of disks to RAID 1 results in poor disk utilization, since RAIDi vs RAID5 capacity and bandwidth requirements are not known a priori. Balancing disk loads when disk space is shared among allocation requests, referred to as virtual arrays - VAs poses a difficult problem. RAIDi disk arrays have a higher access rate per gigabyte than RAID5 disk arrays. Allocating more VAs while keeping disk utilizations balanced and within acceptable bounds is the goal of this study. Given its size and access rate a VA\u27s width or the number of its Virtual Disks -VDs is determined. VDs allocations on physical disks using vector-packing heuristics, with disk capacity and bandwidth as the two dimensions are shown to be the best. An allocation is acceptable if it does riot exceed the disk capacity and overload disks even in the presence of disk failures. When disk bandwidth rather than capacity is the bottleneck, the clustered RAID paradigm is applied, which offers a tradeoff between disk space and bandwidth. Another scenario is also considered where the RAID level is determined by a classification algorithm utilizing the access characteristics of the VA, i.e., fractions of small versus large access and the fraction of write versus read accesses. The effect of RAID 1 organization on its reliability and performance is studied too. The effect of disk failures on the X-code two disk failure tolerant array is analyzed and it is shown that the load across disks is highly unbalanced unless in an NxN array groups of N stripes are randomly rotated

Formal Representation of the SS-DB Benchmark and Experimental Evaluation in EXTASCID

Evaluating the performance of scientific data processing systems is a difficult task considering the plethora of application-specific solutions available in this landscape and the lack of a generally-accepted benchmark. The dual structure of scientific data coupled with the complex nature of processing complicate the evaluation procedure further. SS-DB is the first attempt to define a general benchmark for complex scientific processing over raw and derived data. It fails to draw sufficient attention though because of the ambiguous plain language specification and the extraordinary SciDB results. In this paper, we remedy the shortcomings of the original SS-DB specification by providing a formal representation in terms of ArrayQL algebra operators and ArrayQL/SciQL constructs. These are the first formal representations of the SS-DB benchmark. Starting from the formal representation, we give a reference implementation and present benchmark results in EXTASCID, a novel system for scientific data processing. EXTASCID is complete in providing native support both for array and relational data and extensible in executing any user code inside the system by the means of a configurable metaoperator. These features result in an order of magnitude improvement over SciDB at data loading, extracting derived data, and operations over derived data.Comment: 32 pages, 3 figure

Data partitioning and load balancing in parallel disk systems

Parallel disk systems provide opportunities for exploiting I/O parallelism in two possible ways, namely via inter-request and intra-request parallelism. In this paper we discuss the main issues in performance tuning of such systems, namely striping and load balancing, and show their relationship to response time and throughput. We outline the main components of an intelligent file system that optimizes striping by taking into account the requirements of the applications, and performs load balancing by judicious file allocation and dynamic redistributions of the data when access patterns change. Our system uses simple but effective heuristics that incur only little overhead. We present performance experiments based on synthetic workloads and real-life traces

CRAID: Online RAID upgrades using dynamic hot data reorganization

Current algorithms used to upgrade RAID arrays typically require large amounts of data to be migrated, even those that move only the minimum amount of data required to keep a balanced data load. This paper presents CRAID, a self-optimizing RAID array that performs an online block reorganization of frequently used, long-term accessed data in order to reduce this migration even further. To achieve this objective, CRAID tracks frequently used, long-term data blocks and copies them to a dedicated partition spread across all the disks in the array. When new disks are added, CRAID only needs to extend this process to the new devices to redistribute this partition, thus greatly reducing the overhead of the upgrade process. In addition, the reorganized access patterns within this partition improve the array’s performance, amortizing the copy overhead and allowing CRAID to offer a performance competitive with traditional RAIDs. We describe CRAID’s motivation and design and we evaluate it by replaying seven real-world workloads including a file server, a web server and a user share. Our experiments show that CRAID can successfully detect hot data variations and begin using new disks as soon as they are added to the array. Also, the usage of a dedicated partition improves the sequentiality of relevant data access, which amortizes the cost of reorganizations. Finally, we prove that a full-HDD CRAID array with a small distributed partition (<1.28% per disk) can compete in performance with an ideally restriped RAID-5 and a hybrid RAID-5 with a small SSD cache.Peer ReviewedPostprint (published version