Cloud computing, as a new concept, provides a good solution for handling multimedia applications effectively and efficiently. It can facilitate the execution of complicated multimedia tasks, as well as supports specific and stringent multimedia QoS provisioning. However, for this new idea, a key challenge is how to make each multimedia service task to obtain the required resources in the shortest time. In this paper, we propose an effective load balancing approach for cloud-based multimedia system, called CMLB. Its main advantage is fully considering the load of all servers and the network conditions, and thus achieving reasonable resource allocation and scheduling. The evaluation results demonstrate the effectiveness of our approach.

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Road traffic anomaly denotes a road segment that is anomalous in terms of traffic flow of vehicles. Detecting road traffic anomalies from GPS (Global Position System) snippets data is becoming critical in urban computing since they often suggest underlying events. However, the noisy and sparse nature of GPS snippets data have ushered multiple problems, which have prompted the detection of road traffic anomalies to be very challenging. To address these issues, we propose a two-stage solution which consists of two components: a Collaborative Path Inference (CPI) model and a Road Anomaly Test (RAT) model. CPI model performs path inference incorporating both static and dynamic features into a Conditional Random Field (CRF). Dynamic context features are learned collaboratively from large GPS snippets via a tensor decomposition technique. Then RAT calculates the anomalous degree for each road segment from the inferred fine-grained trajectories in given time intervals. We evaluated our method using a large scale real world dataset, which includes one-month GPS location data from more than eight thousand taxicabs in Beijing. The evaluation results show the advantages of our method beyond other baseline techniques.

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