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Hadoop is an open source implementation of the MapReduce Framework in the realm of distributed processing. A Hadoop cluster is a unique type of computational cluster designed for storing and analyzing large data sets across cluster of…
In this paper, a technology for massive data storage and computing named Hadoop is surveyed. Hadoop consists of heterogeneous computing devices like regular PCs abstracting away the details of parallel processing and developers can just…
Distributed storage systems such as Hadoop File System or Google File System (GFS) ensure data availability and durability using replication. This paper is focused on the analysis of the efficiency of replication mechanism that determines…
Recent improvements in both the performance and scalability of shared-nothing, transactional, in-memory NewSQL databases have reopened the research question of whether distributed metadata for hierarchical file systems can be managed using…
Cloud has been a computational and storage solution for many data centric organizations. The problem today those organizations are facing from the cloud is in data searching in an efficient manner. A framework is required to distribute the…
Workload consolidation, sharing physical resources among multiple workloads, is a promising technique to save cost and energy in cluster computing systems. This paper highlights a few challenges of workload consolidation for Hadoop as one…
This paper explores the changes required of TCP to efficiently support cluster file systems such as Hadoop Distributed File System (HDFS) where the storage nodes are connected through a software defined networking (SDN). Traditional chain…
Design and architecture of cloud storage system plays a vital role in cloud computing infrastructure in order to improve the storage capacity as well as cost effectiveness. Usually cloud storage system provides users to efficient storage…
We propose CFS, a distributed file system for large scale container platforms. CFS supports both sequential and random file accesses with optimized storage for both large files and small files, and adopts different replication protocols for…
Nowadays most of the cloud applications process large amount of data to provide the desired results. Data volumes to be processed by cloud applications are growing much faster than computing power. This growth demands new strategies for…
Hadoop is a distributed batch processing infrastructure which is currently being used for big data management. The foundation of Hadoop consists of Hadoop Distributed File System or HDFS. HDFS presents a client server architecture comprised…
Cloud computing is a general term that involves delivering hosted services over the Internet. With the accelerated growth of the volume of data used by applications, many organizations have moved their data into cloud servers to provide…
We explore the feasibility of implementing a reliable, high performance, distributed storage system on a commodity computing cluster. Files are distributed across storage nodes using erasure coding with small Low-Density Parity-Check (LDPC)…
In P2P systems, large volumes of data are declustered naturally across a large number of peers. But it is very difficult to control the initial data distribution because every user has the freedom to share any data with other users. The…
Distributed File Systems (DFS) are essential for managing vast datasets across multiple servers, offering benefits in scalability, fault tolerance, and data accessibility. This paper presents a comprehensive evaluation of three prominent…
Due to its advantages over traditional data centers, there has been a rapid growth in the usage of cloud infrastructures. These include public clouds (e.g., Amazon EC2), or private clouds, such as clouds deployed using OpenStack. A common…
Distributed File Systems (DFS) have emerged as sophisticated solutions for efficient file storage and management across interconnected computer nodes. The main objective of DFS is to achieve flexible, scalable, and resilient file storage…
Cloud computing services are becoming more and more popular. However, the high concentration of data and services on the clouds make them attractive targets for various security attacks, including DoS, data theft, and privacy attacks.…
Data-intensive platforms such as Hadoop and Spark are routinely used to process massive amounts of data residing on distributed file systems like HDFS. Increasing memory sizes and new hardware technologies (e.g., NVRAM, SSDs) have recently…
Deduplication has been largely employed in distributed storage systems to improve space efficiency. Traditional deduplication research ignores the design specifications of shared-nothing distributed storage systems such as no central…