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A dynamic two-phase schedule strategy for streaming media edge cloud

For streaming media systems based on cloud service, an efficient and adaptive resource scheduling is strongly required. In this paper, we aim to make the small-scale streaming edge cloud admit more requests such that lower perstream operation cost is achieved. Traditionally, the video deployment and session migration is solely executed on the cloud. However, such a simple strategy can hardly guarantee the good trade-off between performance and cost since the popularity of user requests is highly varying, especially for the edge clouds closer to users. In this work, we propose a joint optimization algorithm of session migration and video deployment, i.e., both strategies are smoothly involved to obtain a better trade-off. Consequently, the proposed strategy is more adaptive to dynamic fluctuation of video popularity, and thus gains a flexible balance between service cost and quality. The trace-driven experiment verified the effectiveness of the proposed method, and the results show that our proposed method has better adaptation to popularity than other comparable methods.