scholarly journals ObliviStore: High Performance Oblivious Cloud Storage

2013 ◽  
Author(s):  
E. Stefanov ◽  
E. Shi
Keyword(s):  
Cloud Storage ◽  
High Performance

scholarly journals A Bit String Content Aware Chunking Strategy for Reduced CPU Energy on Cloud Storage

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Bin Zhou ◽  
ShuDao Zhang ◽  
Ying Zhang ◽  
JiaHao Tan
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
System Performance ◽  
Cloud Storage ◽  
High Performance ◽  
Factors Affecting ◽  
Redundant Data ◽  
Energy Factors ◽  
The Cost ◽  
Content Aware

In order to achieve energy saving and reduce the total cost of ownership, green storage has become the first priority for data center. Detecting and deleting the redundant data are the key factors to the reduction of the energy consumption of CPU, while high performance stable chunking strategy provides the groundwork for detecting redundant data. The existing chunking algorithm greatly reduces the system performance when confronted with big data and it wastes a lot of energy. Factors affecting the chunking performance are analyzed and discussed in the paper and a new fingerprint signature calculation is implemented. Furthermore, a Bit String Content Aware Chunking Strategy (BCCS) is put forward. This strategy reduces the cost of signature computation in chunking process to improve the system performance and cuts down the energy consumption of the cloud storage data center. On the basis of relevant test scenarios and test data of this paper, the advantages of the chunking strategy are verified.

scholarly journals A Dynamic Decision Support System for Selection of Cloud Storage Provider

2021 ◽  
Vol 11 (23) ◽  
pp. 11296
Author(s):  
Abdul Mateen ◽  
Seung Yeob Nam ◽  
Muhammad Ali Haider ◽  
Abdul Hanan
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Cloud Storage ◽  
High Performance ◽  
Complex Problem ◽  
Test Cases ◽  
Web Based ◽  
Cloud Environments ◽  
Selection Of

In recent years, the cloud computing model has gained increasing attention and popularity in the field of information technology. For this reason, people are migrating their applications to public, private, or hybrid cloud environments. Many cloud vendors offer similar features with varying costs, so an appropriate choice will be the key to guraranteeing comparatively low operational costs for an organization. The motivation for this work is the necessity to select an appropriate cloud storage provider offering for the migration of applications with less cost and high performance. However, the selection of a suitable cloud storage provider is a complex problem that entails various technical and organizational aspects. In this research, a dynamic Decision Support System (DSS) for selection of an appropriate cloud storage provider is proposed. A web-based application is implemented using PHP and MySQL to facilitate decision makers. The proposed mechanism has been optimized in a way that enables the system to address static database issues for which a user might not acquire the best solution. It focuses on comparing and ranking cloud storage providers by using two modules: scraping and parsing. The evaluation of the proposed system is carried out with appropriate test cases and compared with existing tools and frameworks.

scholarly journals Analyzing the Performance of the S3 Object Storage API for HPC Workloads

2021 ◽  
Vol 11 (18) ◽  
pp. 8540
Author(s):  
Frank Gadban ◽  
Julian Kunkel
Keyword(s):  
Best Practices ◽  
Performance Optimization ◽  
Cloud Storage ◽  
High Performance ◽  
File Systems ◽  
Performance Loss ◽  
Object Storage ◽  
Storage Service ◽  
Main Driver ◽  
Access Patterns

The line between HPC and Cloud is getting blurry: Performance is still the main driver in HPC, while cloud storage systems are assumed to offer low latency, high throughput, high availability, and scalability. The Simple Storage Service S3 has emerged as the de facto storage API for object storage in the Cloud. This paper seeks to check if the S3 API is already a viable alternative for HPC access patterns in terms of performance or if further performance advancements are necessary. For this purpose: (a) We extend two common HPC I/O benchmarks—the IO500 and MD-Workbench—to quantify the performance of the S3 API. We perform the analysis on the Mistral supercomputer by launching the enhanced benchmarks against different S3 implementations: on-premises (Swift, MinIO) and in the Cloud (Google, IBM…). We find that these implementations do not yet meet the demanding performance and scalability expectations of HPC workloads. (b) We aim to identify the cause for the performance loss by systematically replacing parts of a popular S3 client library with lightweight replacements of lower stack components. The created S3Embedded library is highly scalable and leverages the shared cluster file systems of HPC infrastructure to accommodate arbitrary S3 client applications. Another introduced library, S3remote, uses TCP/IP for communication instead of HTTP; it provides a single local S3 gateway on each node. By broadening the scope of the IO500, this research enables the community to track the performance growth of S3 and encourage sharing best practices for performance optimization. The analysis also proves that there can be a performance convergence—at the storage level—between Cloud and HPC over time by using a high-performance S3 library like S3Embedded.

Data Consistency Research of the Cloud Storage Environment Based on P2P Technology

2013 ◽  
Vol 380-384 ◽  
pp. 2589-2592
Author(s):  
Jun Wei Ge ◽  
Feng Yang ◽  
Yi Qiu Fang
Keyword(s):  
Cloud Storage ◽  
High Performance ◽  
Storage System ◽  
Model Systems ◽  
Data Consistency ◽  
Service Reliability ◽  
Cloud Storage Environment ◽  
P2p Technology ◽  
Server Architecture

Many of the characteristics of P2P technology such as decentralization, scalability, robustness, high performance and load balancing are in line with the cloud storage design requirements. In this article, it proposesd a cloud storage model based on P2P technology. This model uses multivariate data server architecture, which can effectively solve the bottleneck problem of centralized cloud storage system. Thereby increasing the cloud storage system performance and the quality of cloud storage services, enhance service reliability. In this article, through researching data consistency algorithm named Paxos and the model systems metadata consistency problem, we improve and optimize Basic Paxos making it applied in the model system.

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