Description

In this paper, we investigate the energy-efficient job dispatching algorithm for transcoding as a service (TaaS) in a multimedia cloud. We aim to minimize the energy consumption of service engines in the cloud while achieving low delay for TaaS. We formulate the job-dispatching problem as a constrained optimization problem under the framework of Lyapunov optimization. Using the drift-plus-penalty function, we propose an online algorithm that dispatches the transcoding jobs to service engines, with an objective to Reduce Energy consumption while achieving the QUEue Stability (REQUEST). We first characterize the fundamental tradeoff between energy consumption and queue delay for the REQUEST algorithm numerically and obtain its performance bound theoretically. Second, we study the robustness of the REQUEST algorithm, with numerical results indicating that the REQUEST algorithm is robust to the inaccuracy of estimating the transcoding time. Third, we compare the performance of the REQUEST algorithm with the other two algorithms, i.e., the Round Robin and Random Rate algorithms. By simulation and real trace data, we show that by appropriately choosing the control variable, the REQUEST algorithm outperforms the Round Robin and Random Rate algorithms, with smaller time average energy consumption and time average queue length. The proposed REQUEST algorithm can be applied in cloud-assisted multimedia transcoding service