Quantitative Analysis and Performance Evaluation of Target-Oriented Replication Strategies in Cloud Computing

Data replications effectively replicate the same data to various multiple locations to accomplish the objective of zero loss of information in case of failures without any downtown. Dynamic data replication strategies (providing run time location of replicas) in clouds should optimize the key performance indicator parameters, like response time, reliability, availability, scalability, cost, availability, performance, etc. To fulfill these objectives, various state-of-the-art dynamic data replication strategies has been proposed, based on several criteria and reported in the literature along with advantages and disadvantages. This paper provides a quantitative analysis and performance evaluation of target-oriented replication strategies based on target objectives. In this paper, we will try to find out which target objective is most addressed, which are average addressed, and which are least addressed in target-oriented replication strategies. The paper also includes a detailed discussion about the challenges, issues, and future research directions. This comprehensive analysis and performance evaluation based-work will open a new door for researchers in the field of cloud computing and will be helpful for further development of cloud-based dynamic data replication strategies to develop a technique that will address all attributes (Target Objectives) effectively in one replication strategy.

Combination of Scheduling and Dynamic Data Replication for Cloud Computing Workflows

Cloud computing is a powerful and high-capacity system, because it can satisfy various demands and share resources for users. It also to benefits from a capacity for treatment and unlimited storage. However, it is burdensome for the providers of internet services that the user demands are increasing as computer capacity is growing stronger and stronger. Therefore, the techniques of workflow scheduling and data replication are used to decrease the costs of the data intensive application. Unfortunately, these two approaches, which are very complementary, are used separately. In this article, a combination of workflow scheduling based on the clustering of data and dynamic data replication strategies has been introduced together. A Cloud simulator, Cloudsim, is used to evaluate the performance of the proposed algorithm. Simulation results show the effectiveness of the proposed algorithm in comparison with well-known algorithms such as random data placement and the Build Time algorithm.