The term RAID was coined in 1987 as an acronym for Redundant Array of Inexpensive Disks; a computer storage technology that was first described by researchers David Patterson, Garth Gibson and Randy Katz at the University of California, Berkley. The concept proposed that an increase in I/O performance and storage reliability could be obtained by arranging several low-cost disk drives into arrays. Several different schemes of organizing the data across the array emerged and were described by the word RAID followed by a single number. Each of these RAID levels has associated advantages and disadvantages however they all share the same primary characteristic in which the data is distributed across multiple disks and seen by the host computer as a single disk. There are three key concepts in RAID technology; mirroring, which writes the same data to more than one disk; stripping, which splits the data across more than one disk; and error correction, where redundant or ‘parity’ data is stored to allow errors in the array to be detected and fixed. Each of the individual RAID levels implements one or more of these concepts to increased I/O performance and improved data reliability. However, it is difficult for researches to design a RAID level that will meet all three and therefore there are tradeoffs when selecting a level for your RAID array. Each of the standard RAID schemes can have positive and negative affects on the reliability and performance of the array; mirroring, can speed up the reading of data but will be slow to write data since the data must be written to the entire mirrored disks. RAID 0 stripes the data across multiple disks without parity or mirroring; resulting in improved read/write performance and space efficiency... ... middle of paper ... ...tripe units can be read simultaneously; however, the write performance takes a hit as the parity stripe has to be recalculated when new data is written. RAID 5 is mainly used for applications requiring decent data redundancy and very good read performance such as the data drives of highly available server systems. RAID 6 expands on the strengths of RAID 5 by striping the data across multiple disks with dual distributed parity; resulting in excellent fault tolerance and data availability. Traditionally a single parity RAID array is vulnerable to data loss until the failed drive is replaced and the array is rebuilt. By making use of dual distributed parity stripe units RAID 6 is able to survive dual drive failures. Due to its similarity to RAID 5, RAID 6 is used in similar applications but where an extra level of data redundancy and fault tolerance is required.
Palmer, Lorrie. “Rebooting the Mythical Array.” Extrapolation. 53.1. (2012): 122. InfoTrac Academic One. Web. 27. Nov. 2013.
Zollinger, H. (2001). AS/RS Application, benefits and Justification in Comparison to Other Storage Methods. http://www.mhia.org/PSC/pdf/asrswhitepaper2.pdf. Retrieved May, 2007.
In a larger cluster,an extra NameNode called secondary NameNode is configured to avoid single point of failure .HDFS is managed with dedicated NameNode to host the file system index, and secondary NameNode can generate snapshots of namenode's memory structures.In this way ,it preventing file-system errors or corruption and reducing data loss. Similarly, job scheduling can manage through a standalone JobTracker server. In clusters,the Hadoop MapReduce engine deployed against an alternate file system, the NameNode, DataNode,secondary NameNode.
The system should be able to incorporate more data storage space with minimum down time.
Director x stream and Ilinkagg x stream are the programs which are based on cut through architecture which provide necessary low latency and predictable jitter that enables data centre architects and designer to build best solution that mee...
Apache Hadoop is one of the solutions; it is an open-source software framework for storage and large scale processing of data-sets on clusters of commodity hardware [3]. Also, Apache Hadoop is a scalable fault-tolerant distributed system for data storage and processing. The core of Hadoop has ...
Google File System (GFS) was developed at Google to meet the high data processing needs. Hadoop’s Distributed File System (HDFS) was originally developed by Yahoo.Inc but it is maintained as an open source by Apache Software Foundation. HDFS was built based on Google’s GFS and Map Reduce. As the internet data was rapidly increasing there was a need to store the large data coming so Google developed a distributed file system called GFS and HDFS was developed to meet the different client needs. These are built on commodity hardware so the systems often fail. To make the systems reliable the data is replicated among multiple nodes. By default minimum number of replicas is 3. Millions of files and large files are common with these types of file systems. Data is more often read than writing. Large streaming needs and small random needs are supported.
As the risks I mentioned in point #2, other device works great as a backup device as well but when it comes to reliability, cloud storage is still the best.
I365. (2010). Cloud Storage – The Issues and Benefits. Retrieved February 10th, 2011 from the Bitpipe website: http://viewer.media.bitpipe.com/1112911604_804/1285694435_799/cloud-storage-issues-and-benefits_wp_en_w.pdf
There are many factors that need to be monitored to determine the performance of a hard drive system. For the purpose of this paper, I will be focusing on two areas that I feel are important. They are disk space and disk efficiency.
It simplifies the storage and processing of large amounts of data, eases the deployment and operation of large-scale global products and services, and automates much of the administration of large-scale clusters of computers.
As we all know that Exascale computers runs million processors which generates data at a rate of terabytes per second. It is impossible to store data generated at such a rate. Methods like dynamic reduction of data by summarization, subset selection, and more sophisticated dynamic pattern identification methods will be necessary to reduce the volume of data. And also the reduced volume needs to be stored at the same rate which it is generated in order to proceed without interruption. This requirement will present new challenges for the movement of data from one super computer to the local and remote storage systems. Data distribution have to be integrated into the data generation phase. This issue of large scale data movement will become more acute as very large datasets and subsets are shared by large scientific communities, this situation requires a large amount of data to be replicated or moved from production to the analysis machines which are sometimes in wide area. While network technology is greatly improved with the introduction of optical connectivity the transmission of large volumes of data will encounter transient failure and automatic recovery tools will be necessary. Another fundamental requirement is the automatic allocation, use and release of storage space. Replicated data cannot be left
of multiple types of end users. The data is stored in one location so that they
We put all of the data onto the platters. They are inside of the hard
System design in a data center network provides the tools for addressing the challenges that occur with expansion of data center infrastructure. This includes support for the rapid growth of applications and their data and storage bandwidth, managing and modifying data storage requirements, optimize server-processing resources and access information