| Summary: | 碩士 === 國立臺北大學 === 資訊工程學系 === 99 === Cloud computing is an increasing research topic in the recent years. Cloud provides different types of services, such as PaaS, IaaS and SaaS. For an economical and efficiency way, hybrid cloud becomes an important environment. How to ensure QoS satisfaction in hybrid cloud is our main objective. We also need to maximum the utilization of private cloud and minimize the cost in public cloud.
In this thesis, we have proposed an Adaptive Scheduling with QoS Satisfaction in Hybrid Cloud Environment. Combine both advantages of private cloud and public cloud: stable, security, flexibility, economically and pay-per-use, using hybrid cloud environment. It provides QoS demand for user, and guarantee job response time. Using runtime estimation and dynamic programming to achieve near-optimal allocation in private cloud and maximize the utilization and minimize the runtime of tasks. For critical inputs or overloading in private cloud, tasks should be selected to public resources. For the features in public cloud, pay-per-use, may only charge for the submit tasks in brief time. But we still try to minimize the cost of renting public slots. By better allocation on private cloud, scheduling can reduce the amount of tasks that need public slots resources. For the tasks have to be dispatched to public cloud, we choose minimal cost strategy based on the characteristic of tasks such as code size and information data size.
As the experiment shows, our scheduling algorithm AsQ achieve better performance in reducing task waiting time, task runtime and task finish time than existing scheduling algorithm. In the same condition, AsQ can also guarantee more QoS satisfaction rate. Cost analysis shows for ensure deadline constraint, AsQ cost much less than others. In the experiment with COSHIC, AsQ presents better balancing between QoS and cost consideration.
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