The Infrastructure-as-a-Service (IaaS) cloud is attracting applications due to the scalability, dynamic resource provision, and pay-as-you-go cost model. Scheduling scientific workflow in the IaaS cloud is faced with uncertainties like resource performance variations and unknown failures. A schedule is said to be robust if it is able to absorb some degree of the uncertainties during the workflow execution. In this paper, we propose a novel workflow scheduling algorithm called Dynamic Earliest-Finish-Time (DEFT) in the IaaS cloud improving both makespan and robustness. DEFT is a dynamic scheduling containing a set of list scheduling loops invoked when some tasks complete successfully and release resources. In each loop, unscheduled tasks are ranked, a best virtual machine (VM) with minimum estimated earliest finish time for each task is selected. A task is scheduled only when all its parents complete, and the selected best VM is ready. Intermediate data is sent from the finished task to each of its child and the selected best VM before the child is scheduled. Experiments show that DEFT can produce shorter makespans with larger robustness than existing typical list and dynamic scheduling algorithms in the IaaS cloud.

Social recommendation systems that make use of the user's social information have recently attracted considerable attention. These recommendation approaches partly solve cold-start and data sparsity problems and significantly improve the performance of recommendation systems. The essence of social recommendation methods is to utilize the user's explicit social connections to improve recommendation results. However, this information is not always available in real-world recommender systems. In this paper, a solution to this problem of explicit social information unavailability is proposed. The existing user-item rating matrix is used to compute implicit social information, and then an ISRec (implicit social recommendation algorithm) which integrates this implicit social information and the user-item rating matrix for social recommendation is introduced. Experimental results show that our method performs much better than state-of-the-art approaches; moreover, complexity analysis indicates that our approach can be applied to very large datasets because it scales linearly with respect to the number of observations in the matrices.