On construction of a cloud storage system with heterogeneous software-defined storage technologies

With the rapid development of networks and Information technologies, cloud computing is not only becoming popular, the types of cloud services available are also increasing. Through cloud services, users can upload their requirements via the Internet to the cloud environment and receive responses following post-processing, for example, with cloud storage services. Software-Defined Storage (SDS) is a virtualization technology for cloud storage services. SDS uses software to integrate storage resources and to improve the accessibility and usability of storage services. Currently, there are many different open source projects available for SDS development. This work aims to utilize these open source projects to improve the efficiency of integration for hardware and software resources. In other words, in this work, we propose a cloud storage system that integrates various open source SDS software to make cloud storage services more compatible and user friendly. The cloud service systems can also be managed in a more convenient and flexible manner. The experimental results demonstrate the benefits of the proposed system.

[1]  Gary D. Knott,et al.  Interpolating Cubic Splines , 2001, J. Approx. Theory.

[2]  Paramvir Bahl,et al.  The Case for VM-Based Cloudlets in Mobile Computing , 2009, IEEE Pervasive Computing.

[3]  Amir Masoud Rahmani,et al.  Reliability and high availability in cloud computing environments: a reference roadmap , 2018, Human-centric Computing and Information Sciences.

[4]  Massimo Villari,et al.  Data On-Boarding in Federated Storage Clouds , 2013, 2013 IEEE Sixth International Conference on Cloud Computing.

[5]  Gregory R. Ganger,et al.  Object-based storage , 2003, IEEE Commun. Mag..

[6]  Chengzhang Peng,et al.  Building a Cloud Storage Service System , 2011 .

[7]  Qian Wang,et al.  Dynamic Proofs of Retrievability for Coded Cloud Storage Systems , 2018, IEEE Transactions on Services Computing.

[8]  MiaoBeibei,et al.  Main Trend Extraction Based on Irregular Sampling Estimation and Its Application in Storage Volume of Internet Data Center , 2016 .

[9]  Dan Feng,et al.  An Effective Cache Algorithm for Heterogeneous Storage Systems , 2013, TheScientificWorldJournal.

[10]  Jian Zhang,et al.  COSBench: cloud object storage benchmark , 2013, ICPE '13.

[11]  Dejun Wang An Efficient Cloud Storage Model for Heterogeneous Cloud Infrastructures , 2011 .

[12]  Paulvanna Nayaki Marimuthu,et al.  Managing distributed storage system through network redesign , 2013, 2013 15th Asia-Pacific Network Operations and Management Symposium (APNOMS).

[13]  Carlos Maltzahn,et al.  Ceph: a scalable, high-performance distributed file system , 2006, OSDI '06.

[14]  James Zijun Wang,et al.  A Heuristic Task Scheduling Algorithm for Heterogeneous Virtual Clusters , 2016, Sci. Program..

[15]  Ben Y. Zhao,et al.  Maintenance-Free Global Data Storage , 2001, IEEE Internet Comput..

[16]  Yong Zhao,et al.  Cloud Computing and Grid Computing 360-Degree Compared , 2008, GCE 2008.

[17]  Richard Wolski,et al.  The Eucalyptus Open-Source Cloud-Computing System , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[18]  Franco Callegati,et al.  Virtual Networking Performance in OpenStack Platform for Network Function Virtualization , 2016, J. Electr. Comput. Eng..

[19]  George Mastorakis Resource Management of Mobile Cloud Computing Networks and Environments , 2015 .

[20]  Lakshmi Sobhana Kalli,et al.  Market-Oriented Cloud Computing : Vision , Hype , and Reality for Delivering IT Services as Computing , 2013 .

[21]  S. Akarsh,et al.  File System Aware Storage Virtualization Management , 2012, 2012 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM).

[22]  Yi Mu,et al.  Efficient Public Verification of Data Integrity for Cloud Storage Systems from Indistinguishability Obfuscation , 2017, IEEE Transactions on Information Forensics and Security.

[23]  Young-Sik Jeong,et al.  Secure Authentication-Management human-centric Scheme for trusting personal resource information on mobile cloud computing with blockchain , 2018, Human-centric Computing and Information Sciences.

[24]  Mianxiong Dong,et al.  Energy-Efficient Matching for Resource Allocation in D2D Enabled Cellular Networks , 2017, IEEE Transactions on Vehicular Technology.

[25]  Kanishk Jain Object-based Storage , 2022 .

[26]  Xiaodong Ji,et al.  Resource Allocation in Cloud Radio Access Networks With Device-to-Device Communications , 2017, IEEE Access.

[27]  Chao-Tung Yang,et al.  Implementation of a Software-Defined Storage Service with Heterogeneous Storage Technologies , 2015, 2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops.

[28]  Zhenyu Zhou,et al.  Social Network-Based Content Delivery in Device-to-Device Underlay Cellular Networks Using Matching Theory , 2017, IEEE Access.

[29]  Erik Elmroth,et al.  A Cloud Environment for Data-intensive Storage Services , 2011, 2011 IEEE Third International Conference on Cloud Computing Technology and Science.

[30]  Bin Li,et al.  Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure , 2016, Comput. Intell. Neurosci..