Abstract. The exponential growth of data first presented challenges to cuttingedge businesses such as Goggle, Yahoo, Amazon, Microsoft, Facebook, and Twitter. Data volumes to be processed by cloud applications are growing much faster than computing power. This growth demands new strategies for processing and analyzing information. Hadoop MapReduce has become a powerful computation model that addresses those problems. MapReduce is a programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters. Through a simple interface with two functions, map and reduce, this model facilitates parallel implementation of many real world tasks such as data processing for search engines and machine learning. Earlier versions of Hadoop MapReduce had several performance problems like connection between map to reduce task, data overload and slow processing. In this paper, we propose a modified MapReduce architectureMapReduce Agent (MRA) -that resolves those performance problems. MRA can reduce completion time, improve system utilization, and give better performance. MRA employs multi-connection which resolves error recovery with a Q-chained load balancing system. In this paper, we also discuss various applications and implementations of the MapReduce programming model in cloud environments.
Flash memory storage such as SSDs (Solid State disks) have been gaining popularity due to its low energy consumption and durability in embedded systems and laptops. With the fast technical improvement, Solid State Disks (SSDs) are becoming an important part of the computer storage hierarchy to significantly improve performance and energy efficiency. However, due to its relatively high price and low capacity, a major system research issue to address is on how to make SSDs play their most effective roles as a high-performance storage system in cost-and performance-effective ways. In this paper, we describe a NAS prototype that eliminates most software overheads and makes Solid State Disks a high-performance storage system in cost-and performance-effective ways. NAS integrates a network adapter into SSDs, so the SSDs remove the random access time, Reduce administrative overhead while delivering scalable, reliable data storage. NAS can also give support block based I/O rather than traditional NAS system which supports only file I/O protocol such as NFS and CIFS. Our scale-out NAS solutions can boost capacity and performance while enabling flexible provisioning, no disruptive operations, easy scalability, and virtualization. As the storage and processing requirements of the file server continued to increase data security and integrity became difficult to manage in conventional NAS, this NAS can ensure the data integrity and sanctity as well.
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