On Distributed Object Storage Architecture Based on Mimic Defense

With the advent of the era of big data,cloud computing,Internet of things,and other information industries continue to develop.There is an increasing amount of unstructured data such as pictures,audio,and video on the Internet.And the distributed object storage system has become the mainstream cloud storage solution.With the increasing number of distributed applications,data security in the distributed object storage system has become the focus.For the distributed object storage system,traditional defenses are means that fix discovered system vulnerabilities and backdoors by patching,or means to modify the corresponding structure and upgrade.However,these two kinds of means are hysteretic and hardly deal with unknown security threats.Based on mimic defense theory,this paper constructs the principle framework of the distributed object storage system and introduces the dynamic redundancy and heterogeneous function in the distributed object storage system architecture,which increases the attack cost,and greatly improves the security and availability of data.

Design and Implementation of ZTE Object Storage System

This paper introduces the basic concepts and features of an obiect storage system. It also introduces some related standards, specifications, and implementations for several existing systems. ZTE＇ s Object Storage System （ZTE OSS） was designed by Tsinghua University and ZTE Corporation and is designed to manage large amounts of data. ZTE OSS has a scalable architecture, some open source components, and an efficient key-value database. ZTE OSS is easy to scale and highly reliable. Experiments show that ZTE OSS performs well with mass data and heavy

Data Utilization-Based Adaptive Data Management Method for Distributed Storage System in WAN Environment

Recently,research on a distributed storage system that efficiently manages a large amount of data has been actively conducted following data production and demand increase.Physical expansion limits exist for traditional standalone storage systems,such as I/O and file system capacity.However,the existing distributed storage system does not consider where data is consumed and is more focused on data dissemination and optimizing the lookup cost of data location.And this leads to system performance degradation due to low locality occurring in a Wide Area Network(WAN)environment with high network latency.This problem hinders deploying distributed storage systems to multiple data centers over WAN.It lowers the scalability of distributed storage systems to accommodate data storage needs.This paper proposes a method for distributing data in a WAN environment considering network latency and data locality to solve this problem and increase overall system performance.The proposed distributed storage method monitors data utilization and locality to classify data temperature as hot,warm,and cold.With assigned data temperature,the proposed algorithm adaptively selects the appropriate data center and places data accordingly to overcome the excess latency from the WAN environment,leading to overall system performance degradation.This paper also conducts simulations to evaluate the proposed and existing distributed storage methods.The result shows that our proposed method reduced latency by 38%compared to the existing method.Therefore,the proposed method in this paper can be used in large-scale distributed storage systems over a WAN environment to improve latency and performance compared to existing methods,such as consistent hashing.

Performance Analysis and Framework Optimization of Open Source Cloud Storage System

More and more embedded devices, such as mobile phones, tablet PCs and laptops, are used in every field, so huge files need to be stored or backed up into cloud storage. Optimizing the performance of cloud storage is very important for Internet development. This paper presents the performance evaluation of the open source distributed storage system, a highly available, distributed, eventually consistent object/blob store from Open Stack cloud computing components. This paper mainly focuses on the mechanism of cloud storage as well as the optimization methods to process different sized files. This work provides two major contributions through comprehensive performance evaluations. First, it provides different configurations for Open Stack Swift system and an analysis of how every component affects the performance. Second, it presents the detailed optimization methods to improve the performance in processing different sized files. The experimental results show that our method improves the performance and the structure. We give the methods to optimize the object-based cloud storage system to deploy the readily available storage system.