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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Dynamic Request Redirection and Resource Provisioning for Cloud-Based Video Services under Heterogeneous Environment
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Dynamic Request Redirection and Resource Provisioning for Cloud-Based Video Services under Heterogeneous Environment

机译：异构环境下基于云的视频服务的动态请求重定向和资源配置

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摘要

Cloud computing provides a new opportunity for Video Service Providers (VSP) to running compute-intensive video applications in a cost effective manner. Under this paradigm, a VSP may rent virtual machines (VMs) from multiple geo-distributed datacenters that are close to video requestors to run their services. As user demands are difficult to predict and the prices of the VMs vary in different time and region, optimizing the number of VMs of each type rented from datacenters located in different regions in a given time frame becomes essential to achieve cost effectiveness for VSPs. Meanwhile, it is equally important to guarantee users’ Quality of Experience (QoE) with rented VMs. In this paper, we give a systematic method called Dynamical Request Redire ction and Resource Prov isioning (DYRECEIVE) to address this problem. We formulate the problem as a stochastic optimization problem and design a Lyapunov optimization framework based online algorithm to solve it. Our method is able to minimize the long-term time average cost of renting cloud resources while maintaining the user QoE. Theoretical analysis shows that our online algorithm can produce a solution within an upper bound to the optimal solution achieved through offline computing. Extensive experiments shows that our method is adaptive to request pattern changes along time and outperforms existing algorithms.
机译：云计算为视频服务提供商（VSP）提供了一种以经济高效的方式运行计算密集型视频应用程序的新机会。在这种范式下，VSP可以从靠近视频请求者的多个地理分布的数据中心租用虚拟机（VM）来运行其服务。由于用户需求难以预测，并且虚拟机的价格在不同的时间和区域变化，因此，在给定的时间范围内优化从位于不同区域的数据中心租用的每种类型的虚拟机的数量对于实现VSP的成本效益至关重要。同时，通过租用的虚拟机保证用户的体验质量（QoE）同样重要。在本文中，我们提供了一种称为动态请求重发和资源提供（DYRECEIVE）的系统方法来解决此问题。我们将该问题表述为随机优化问题，并设计了基于Lyapunov优化框架的在线算法来解决该问题。我们的方法能够在保持用户QoE的同时最小化租用云资源的长期平均成本。理论分析表明，我们的在线算法可以在通过离线计算获得最佳解决方案的上限内产生解决方案。大量实验表明，我们的方法适用于随时间变化的模式请求，其性能优于现有算法。

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