Delayed Replication Algorithm with Dynamic Threshold for Cloud Datacenters

Cloud computing offers a large amount of services as well as storage space over the Internet to a large number of customers. One of the services offered by the cloud is data as a service. Often, cloud computing is dealt with the challenge of managing data and also improving the availability of data. To ensure data availability and manage storage space, the concept of data replication has been used in this work. With the help of data replication technique, multiple copies of a data are created and distributed over geographically distributed sites. In this work, a delayed replica creation scheme based on dynamic threshold is designed and employed for creating replicas. Replication is done for the data based on its relative importance with other data. With the help of this work, the authors are able to improve the response time of accessing the data from a particular site and also reduce the cost involved in data access.

[1]  K. G. Srinivasagan,et al.  An improved dynamic data replica selection and placement in cloud , 2014, 2014 International Conference on Recent Trends in Information Technology.

[2]  Jawwad Shamsi,et al.  Data-Intensive Cloud Computing: Requirements, Expectations, Challenges, and Solutions , 2013, Journal of Grid Computing.

[3]  Jinzy Zhu,et al.  Cloud Computing Technologies and Applications , 2010, Handbook of Cloud Computing.

[4]  Hairong Kuang,et al.  The Hadoop Distributed File System , 2010, 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST).

[5]  Raouf Boutaba,et al.  Cloud computing: state-of-the-art and research challenges , 2010, Journal of Internet Services and Applications.

[6]  P. Vivekanandan,et al.  Efficient data integrity and data replication in cloud using stochastic diffusion method , 2018, Cluster Computing.

[7]  Rubén S. Montero,et al.  IaaS Cloud Architecture: From Virtualized Datacenters to Federated Cloud Infrastructures , 2012, Computer.

[8]  Abdelkader Hameurlain,et al.  Ensuring performance and provider profit through data replication in cloud systems , 2017, Cluster Computing.

[9]  Philippe Merle,et al.  Elasticity in Cloud Computing: State of the Art and Research Challenges , 2018, IEEE Transactions on Services Computing.

[10]  Ahmad Faraahi,et al.  A locality-based replication manager for data cloud , 2016, Frontiers of Information Technology & Electronic Engineering.

[11]  J. Morris Chang,et al.  QoS-Aware Data Replication for Data-Intensive Applications in Cloud Computing Systems , 2013, IEEE Transactions on Cloud Computing.

[12]  Ismaeel Al Ridhawi,et al.  Location-aware data replication in cloud computing systems , 2015, 2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).

[13]  Wei-Tek Tsai,et al.  Software-as-a-service (SaaS): perspectives and challenges , 2013, Science China Information Sciences.

[14]  Sherali Zeadally,et al.  Performance analysis of data intensive cloud systems based on data management and replication: a survey , 2016, Distributed and Parallel Databases.

[15]  Dan Feng,et al.  CDRM: A Cost-Effective Dynamic Replication Management Scheme for Cloud Storage Cluster , 2010, 2010 IEEE International Conference on Cluster Computing.

[16]  Ricardo Azevedo,et al.  The Building Blocks of a PaaS , 2012, Journal of Network and Systems Management.

[17]  Najme Mansouri.  Adaptive data replication strategy in cloud computing for performance improvement , 2016, Frontiers of Computer Science.

[18]  Marwa F. Mohamed.  Service replication taxonomy in distributed environments , 2015, Service Oriented Computing and Applications.