Parallelizing video transcoding with load balancing on cloud computing

Abstract

Cloud computing is emerging as a very promising technology for computing and storage services. However, the multi-resources load balancing over heterogeneous cluster or cloud is a NP-hard problem. To obtain an optimized solution, in this paper, we propose a heuristic algorithm named Minimum Longest Queue Finish Time (MLFT). In the proposed scheme, we first divide the high computation task into multiple sub-tasks, and re-organize all the tasks into multiple task queues to shorten the entire finish time of all the tasks submitted to the cluster and launched in parallel according to load balancing. In the task division process, an adaptive segmentation algorithm is proposed according to the complexity and maximum segmentation granularity of the input task. Based on the proposed algorithm, an efficient parallel video transcoding framework with cloud computing is presented. Experimental results show that the proposed algorithm outperforms the existing algorithms significantly on the entire finish time of the tasks and approaches to the optimal solution closely.