Multi-Query Rate-Utility Optimized Scheduling for Cloud Streaming Data
Recent days live video sharing has received reputation as the broadcast media can easily distribute streaming data extensively for different multiple locations. The improved camera and its functionalities are widely distributing streaming data in cloud environment are subject to infrastructure limitations which are considered as a major issue and incompatible to meet the requirements of Latency services. In particular cloud-based resources where the streaming data sharing workflow schedules to the heterogeneous network across multiple locations needs high stability and scalability which are critical to cloud IaaS. The activity of real-time evaluation of continuous video streaming data will follow a scalable streaming procedure where the specific entities with a frequent interface can cause a delayed output. In order to analyze continuous streaming data in the real-time process, the extensible utility and optimized streaming process must accommodate various the streaming objects, which often interact with different resources. This paper addresses the utilization of the streaming data and schedule the workflow in IaaS clouds and propose a new Optimization Method using Multi-Query Rate-Utilization on various parameters such as Throughput, Latency, End-to-End Delay, Average Waiting Time and Resource Utilization. The result of the simulation shows that in most cases our algorithm can achieve considerably better optimized solutions when compared with the existing methods.
Key words: Distributed System, IaaS, Streaming Processing Systems, Resource Allocation, Workflow Scheduling and Multi-Query Optimization.