Performance optimization of heterogeneous cloud storage with bandwidth & capacity considerations

2018 ◽  
Author(s):  
Po-Chun Huang ◽  
Yuan-Hao Chang ◽  
Tseng-Yi Chen ◽  
Chia-Heng Tu ◽  
Chun Chen ◽  
...  
Keyword(s):  
Performance Optimization ◽  
Cloud Storage ◽  
Heterogeneous Cloud ◽  
Bandwidth Capacity

scholarly journals Multi-security-level cloud storage system based on improved proxy re-encryption

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Jinan Shen ◽  
Xuejian Deng ◽  
Zhenwu Xu
Keyword(s):  
Performance Optimization ◽  
Cloud Storage ◽  
Storage System ◽  
Security Requirements ◽  
Control Factor ◽  
Security Level ◽  
Fine Grained ◽  
Attribute Based Encryption ◽  
And Performance ◽  
Heterogeneous Cloud

AbstractBased on the characteristics and data security requirements of the cloud environment, we present a scheme for a multi-security-level cloud storage system that is combined with AES symmetric encryption and an improved identity-based proxy re-encryption (PRE) algorithm. Our optimization includes support for fine-grained control and performance optimization. Through a combination of attribute-based encryption methods, we add a fine-grained control factor to our algorithm in which each authorization operation is only valid for a single factor. By reducing the number of bilinear mappings, which are the most time-consuming processes, we achieve our aim of optimizing performance. Last but not least, we implement secure data sharing among heterogeneous cloud systems. As shown in experiment, our proposed multi-security-level cloud storage system implements services such as the direct storage of data, transparent AES encryption, PRE protection that supports fine-grained and ciphertext heterogeneous transformation, and other functions such as authentication and data management. In terms of performance, we achieve time-cost reductions of 29.8% for the entire process, 48.3% for delegation and 47.2% for decryption.

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.

Verifiable Response in Heterogeneous Cloud Storage

2015 ◽  
pp. 91-108
Author(s):  
Abdul Wahid ◽  
Mohatesham Pasha Quadri ◽  
Ahmad Talha Siddiqui ◽  
Mudasir M. Kirmani ◽  
Khaleel Ahmad
Keyword(s):  
Cloud Storage ◽  
Research Work ◽  
Confidential Data ◽  
Client Data ◽  
Security Properties ◽  
Symmetric Key ◽  
Heterogeneous Cloud ◽  
Symmetric Key Cryptography ◽  
Distributed Cloud ◽  
The Impact

A Distributed Cloud Storage Provider (DCSP) is used for managing the client's data. However, the platform is vulnerable to security attacks, which results in huge losses of client data. The confidential data in an enterprise system may be illegally accessed through a remote interface provided by a heterogeneous-cloud and archives. The data may be lost or tampered with when it is stored in a storage pool outside the enterprise on a cloud. To achieve reliability, consistency, security, and confidentiality of user information Kerberos Protocol is used. The Kerberos protocol is designed to provide reliable authentication over open and insecure networks. The symmetric-key cryptography is maintained for transaction over the network. In this chapter, a cooperative Key-Distribution Center (KDC) scheme is proposed to support dynamic scalability on multiple storage servers. The results of the research work show that KDC provides effective security properties required by Kerberos protocol to mitigate the impact of various attacks in any cloud.

Towards the design of optimal data redundancy schemes for heterogeneous cloud storage infrastructures

2011 ◽  
Vol 55 (5) ◽  
pp. 1100-1113 ◽  
Author(s):  
Lluis Pamies-Juarez ◽  
Pedro García-López ◽  
Marc Sánchez-Artigas ◽  
Blas Herrera
Keyword(s):  
Cloud Storage ◽  
Data Redundancy ◽  
Heterogeneous Cloud
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