Research Area:  Cloud Computing
Nowadays, more and more computation-intensive scientific applications with diverse needs are migrating to cloud computing systems. However, the cloud systems alone cannot meet applications requirements at all times with the increasing demands from users. Therefore, the multi-cloud systems that can provide scalable storage and computing resources become a good solution. The main challenges for such systems are multiple billing mechanisms, virtual resources heterogeneity, and systems reliability. In response to these challenges, we first build a multi-cloud systems fault-tolerant workflow scheduling framework, which tries to improve the scientific applications execution reliability and reduce their execution cost. Then, we use Weibull distribution to analyze task execution reliability and hazard rate, which is used to duplicate task with high execution hazard rate. Thirdly, we integrate different multi-cloud providers billing mechanism into the proposed scheduling framework, and we also formulate this workflow scheduling problem as a linear programming problem. Fourthly, we define the DAG tasks cost-efficient bottom level, and propose a fault-tolerant cost-efficient workflow scheduling algorithm (FCWS) that minimizes application execution cost, time while ensuring their reliability. Finally, The results clearly demonstrate that our proposed FCWS algorithm outperforms existing FR-MOS, CWS in terms of cost and reliability, and FCWS is also better than CWS.
Author(s) Name:  Xiaoyong Tang
Journal name:  IEEE Transactions on Cloud Computing
Publisher name:  IEEE
Volume Information:  Page(s): 1 - 1
Paper Link:   https://ieeexplore.ieee.org/abstract/document/9349203