A Survey on Cloud Storage
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paper_title: Compute and Storage Clouds Using Wide Area High Performance Networks

paper_content:

We describe a cloud-based infrastructure that we have developed that is optimized for wide area, high performance networks and designed to support data mining applications. The infrastructure consists of a storage cloud called Sector and a compute cloud called Sphere. We describe two applications that we have built using the cloud and some experimental studies.
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paper_title: The Google file system

paper_content:

We have designed and implemented the Google File System, a scalable distributed file system for large distributed data-intensive applications. It provides fault tolerance while running on inexpensive commodity hardware, and it delivers high aggregate performance to a large number of clients. While sharing many of the same goals as previous distributed file systems, our design has been driven by observations of our application workloads and technological environment, both current and anticipated, that reflect a marked departure from some earlier file system assumptions. This has led us to reexamine traditional choices and explore radically different design points. The file system has successfully met our storage needs. It is widely deployed within Google as the storage platform for the generation and processing of data used by our service as well as research and development efforts that require large data sets. The largest cluster to date provides hundreds of terabytes of storage across thousands of disks on over a thousand machines, and it is concurrently accessed by hundreds of clients. In this paper, we present file system interface extensions designed to support distributed applications, discuss many aspects of our design, and report measurements from both micro-benchmarks and real world use.
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paper_title: Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities

paper_content:

This keynote paper: presents a 21 st century vision of computing; identifies various computing paradigms promising to deliver the vision of computing utilities; defines Cloud computing and provides the architecture for creating market-oriented Clouds by leveraging technologies such as VMs; provides thoughts on marketbased resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; presents some representative Cloud platforms especially those developed in industries along with our current work towards realising market-oriented resource allocation of Clouds by leveraging the 3 rd generation Aneka enterprise Grid technology; reveals our early thoughts on interconnecting Clouds for dynamically creating an atmospheric computing environment along with pointers to future community research; and concludes with the need for convergence of competing IT paradigms for delivering our 21 st century vision.
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paper_title: MapReduce: simplified data processing on large clusters

paper_content:

MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.
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paper_title: Bigtable: A Distributed Storage System for Structured Data

paper_content:

Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications place very different demands on Bigtable, both in terms of data size (from URLs to web pages to satellite imagery) and latency requirements (from backend bulk processing to real-time data serving). Despite these varied demands, Bigtable has successfully provided a flexible, high-performance solution for all of these Google products. In this paper we describe the simple data model provided by Bigtable, which gives clients dynamic control over data layout and format, and we describe the design and implementation of Bigtable.
---
paper_title: The Google file system

paper_content:

We have designed and implemented the Google File System, a scalable distributed file system for large distributed data-intensive applications. It provides fault tolerance while running on inexpensive commodity hardware, and it delivers high aggregate performance to a large number of clients. While sharing many of the same goals as previous distributed file systems, our design has been driven by observations of our application workloads and technological environment, both current and anticipated, that reflect a marked departure from some earlier file system assumptions. This has led us to reexamine traditional choices and explore radically different design points. The file system has successfully met our storage needs. It is widely deployed within Google as the storage platform for the generation and processing of data used by our service as well as research and development efforts that require large data sets. The largest cluster to date provides hundreds of terabytes of storage across thousands of disks on over a thousand machines, and it is concurrently accessed by hundreds of clients. In this paper, we present file system interface extensions designed to support distributed applications, discuss many aspects of our design, and report measurements from both micro-benchmarks and real world use.
---
paper_title: Compute and Storage Clouds Using Wide Area High Performance Networks

paper_content:

We describe a cloud-based infrastructure that we have developed that is optimized for wide area, high performance networks and designed to support data mining applications. The infrastructure consists of a storage cloud called Sector and a compute cloud called Sphere. We describe two applications that we have built using the cloud and some experimental studies.
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paper_title: Data Mining Using High Performance Data Clouds: Experimental Studies Using Sector and Sphere

paper_content:

We describe the design and implementation of a high performance cloud that we have used to archive, analyze and mine large distributed data sets. By a cloud, we mean an infrastructure that provides resources and/or services over the Internet. A storage cloud provides storage services, while a compute cloud provides compute services. We describe the design of the Sector storage cloud and how it provides the storage services required by the Sphere compute cloud. We also describe the programming paradigm supported by the Sphere compute cloud. Sector and Sphere are designed for analyzing large data sets using computer clusters connected with wide area high performance networks (for example, 10+ Gb/s). We describe a distributed data mining application that we have developed using Sector and Sphere. Finally, we describe some experimental studies comparing Sector/Sphere to Hadoop.
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paper_title: Provably secure authentication protocol based on convertible proxy signcryption in cloud computing

paper_content:

Mutual authentication between the user and the public cloud is essential requirement for the user to access the public cloud in cloud computing. In 2011, Juang et al. proposed a first authentication scheme based on proxy signature. The advantage of the scheme is that the user only needs to register on his home service cloud (HSC), and can pass through the authentication of the public cloud with the help of his HSC. However, their scheme has three weaknesses: 1) the user's HSC needs to update the user's public key in each session to protect the user's privacy; 2) HSC may suffer from network jam when many users in the same HSC need to register on different public clouds simultaneously; and 3) a secret key should be shared between HSC and visiting cloud. To overcome these weaknesses, a provably secure convertible proxy signcryption for privacy preserving is proposed. Based on this scheme, a novel one-round authentication protocol is proposed, which the user only needs to register on his HSC, and can pass through the authentication of the visiting cloud without the help of his HSC. On the other hand, the proposed protocol can provide some nice properties, such as user privacy protection, non-repudiation, without updating the user's public key, and secret key does not have to be shared between HSC and visiting cloud. In addition, the proposed scheme is provably secure in the random oracle model, and is more efficient than Juang et al.'s scheme.
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paper_title: ZettaDS: A Light-weight Distributed Storage System for Cluster

paper_content:

We have designed and implemented the Zetta data storage system (ZettaDS), a light-weight scalable distributed data storage system for cluster. While sharing many common characters with some of modern distributed data storage systems such as single meta server architecture, running on inexpensive commodity components, our system is a very light-weight one and aims to handle lots of small files efficiently. The emphases of our design are on scalability of storage capacity and manageability. Throughput and performance are considered secondary. Furthermore, ZettaDS is designed to minimize the resource consumption due to running on a non-dedicated system.The paper describes the details and rationales of the design and implementation. Also, we evaluate our system by some experiments. The results demonstrate that our system can use the storage spaces more efficiently and achieve better transfer performance when facing a large number of small files.
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paper_title: Sector and Sphere: the design and implementation of a high-performance data cloud

paper_content:

Cloud computing has demonstrated that processing very large datasets over commodity clusters can be done simply, given the right programming model and infrastructure. In this paper, we describe the design and implementation of the Sector storage cloud and the Sphere compute cloud. By contrast with the existing storage and compute clouds, Sector can manage data not only within a data centre, but also across geographically distributed data centres. Similarly, the Sphere compute cloud supports user-defined functions (UDFs) over data both within and across data centres. As a special case, MapReduce-style programming can be implemented in Sphere by using a Map UDF followed by a Reduce UDF. We describe some experimental studies comparing Sector/Sphere and Hadoop using the Terasort benchmark. In these studies, Sector is approximately twice as fast as Hadoop. Sector/Sphere is open source.
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paper_title: Rethinking deduplication in cloud: From data profiling to blueprint

paper_content:

Cloud storage system is becoming the substantial component of the cloud system due to emerging trend of user data. Different from other computing resources, storage resource is vulnerable to the cost issue since the data should be maintained during the downtime. In this paper, we investigate the benefit and overhead when deduplication techniques are adopted to the cloud storage system. From the result, we discuss several challenges across the cloud storage. Furthermore, we suggest the cloud storage architecture and the deduplication engine to optimize the deduplication feature in the cloud storage system. We expect that our suggestions reduce the cloud storage system cost efficiently without performance degradation of data transfer.
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paper_title: Cloud context-based onboard data compression

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A simplified cloud mask algorithm suitable for present-day onboard processing is developed based on the operational MODIS cloud mask algorithm. The code is reduced to less than 45% of its original size while retaining 90% of the accuracy. Clear sky data compression is tested using our simplified cloud mask followed by application of the CCSDS lossless compression algorithm. For most of the channels, compressing the cloud masked MODIS L1B data can reduce the data volume by about 40% compared with compression without using cloud mask. The cloud mask was also applied to L1A data with comparable or even better data reduction performance depending on local data filling schemes. The cloud mask itself can be compressed to less than 0.5 bit-per-pixel. This paper summarizes our approach and provides results in detail.
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paper_title: Evaluating Cloud Platform Architecture with the CARE Framework

paper_content:

There is an emergence of Cloud application platforms such as Microsoft’s Azure, Google’s App Engine and Amazon’s EC2/SimpleDB/S3. Startups and Enterprise alike, lured by the promise of ‘infinite scalability’, ‘ease of development’, ‘low infrastructure setup cost’ are increasingly using these Cloud service building blocks to develop and deploy their web based applications. However, the precise nature of these Cloud platforms and the resultant Cloud application runtime behavior is still largely an unknown. Given the black box nature of these platforms, and the novel programming and data models of Cloud, there is a dearth of tools and techniques for enabling the rigorously evaluation of Cloud platforms at runtime. This paper introduces the CARE (Cloud Architecture Runtime Evaluation) approach, a framework for evaluating Cloud application development and runtime platforms. CARE implements a unified interface with WSDL and REST in order to evaluate different Cloud platforms for Cloud application hosting servers and Cloud databases. With the unified interface, we are able to perform selective high stress and low stress evaluations corresponding to desired test scenarios. Result shows the effectiveness of CARE in the evaluation of Cloud variations in terms of scalability, availability and responsiveness, across both compute and storage capabilities. Thus placing CARE as an important tool in the path of Cloud computing research.
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paper_title: A Secure Cloud Backup System with Assured Deletion and Version Control

paper_content:

Cloud storage is an emerging service model that enables individuals and enterprises to outsource the storage of data backups to remote cloud providers at a low cost. However, cloud clients must enforce security guarantees of their outsourced data backups. We present Fade Version, a secure cloud backup system that serves as a security layer on top of today's cloud storage services. Fade Version follows the standard version-controlled backup design, which eliminates the storage of redundant data across different versions of backups. On top of this, Fade Version applies cryptographic protection to data backups. Specifically, it enables fine-grained assured deletion, that is, cloud clients can assuredly delete particular backup versions or files on the cloud and make them permanently inaccessible to anyone, while other versions that share the common data of the deleted versions or files will remain unaffected. We implement a proof-of-concept prototype of Fade Version and conduct empirical evaluation atop Amazon S3. We show that Fade Version only adds minimal performance overhead over a traditional cloud backup service that does not support assured deletion.
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paper_title: General survey on massive data encryption

paper_content:

With the rapid development of Cloud computing, Internet of Things and social network technologies, the network data are increasing dramatically. The security of massive data interaction has attracted more and more attention in recently years. This paper discusses the encryption principles, advantages and disadvantages of some mainstream massive data encryption technologies, i.e., the encryption technology based on modern cryptosystem, the encryption technology based on parallel and distributed computing, the encryption technology based on biological engineering, and Attribute-based massive data encryption technology. Finally, we outlook the need-to-be solved problems and development trend of the massive data encryption technology.
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Title: A Survey on Cloud Storage

Section 1: INTRODUCTION
Description 1: This section introduces the concept of cloud storage, its significance in IT projects, and the distinctions between public, private, and hybrid cloud storage.

Section 2: INTRODUCTION FORMATION OF CLOUD COMPUTING
Description 2: This section traces the evolution of cloud computing concepts, the emergence of cloud storage as part of this evolution, and the role of major technology companies in advancing cloud storage technologies.

Section 3: CORE TECHNOLOGY OF CLOUD STORAGE
Description 3: This section discusses the fundamental technologies underpinning cloud storage, including Google File System (GFS), Hadoop Distributed File System (HDFS), and other high-performance storage technologies.

Section 4: CLOUD STORAGE RESEARCHS
Description 4: This section reviews current research and developments in cloud storage, examining high-performance distributed file systems, private storage clouds, and various academic contributions to the field.

Section 5: CONCLUSIONS AND FUTURE WORK
Description 5: This section summarizes the advantages of cloud storage over traditional storage solutions and outlines potential future research directions in cloud storage technology.