LAUSR

A growing number of social video distribution services are turning toward to the cloud-based architecture for lower cost and better scalability. Although appealing, such a cloud content delivery network (cloud-based CDN) involves two key tasks: to dynamically migrate the contents across multiple data centers on diverse locations and to place user requests in the proper sites such that the monetary cost and service latency are targeted appropriately. In this paper, we formulate these two objectives into a combinational optimization problem and develop a context-aware community-based computing model to discover the contextual information of video propagation. In particular, we explore the social graph that people created to estimate the potential demands of each video and to propose a basic scheme for video migration and request placement under the heterogeneous cloud paradigm. Since the basic scheme adopts a greedy algorithm per step, it only obtains the short-term optimality. To solve this problem, we design an advanced scheme that can align to the long-term optimum solution and can ensure that the total monetary cost is minimized while meeting the predefined service level agreements (SLAs). Compared with present scheduling techniques, we found that the designed algorithm achieves lower service cost yet guarantees that the response time of requests is all within the specified quality of service (QoS) requirements.