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Cost Aware Service Placement and Load Dispatching in Mobile Cloud Systems

机译:移动云系统中的成本感知服务放置和负载分派

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With proliferation of smart phones and an increasing number of services provisioned by clouds, it is commonplace for users to request cloud services from their mobile devices. Accessing services directly from the Internet data centers inherently incurs high latency due to long RTTs and possible congestions in WAN. To lower the latency, some researchers propose to ‘cache’ the services at edge clouds or smart routers in the access network which are closer to end users than the Internet cloud. Although ‘caching’ is a promising technique, placing the services and dispatching users’ requests in a way that can minimize the users’ access delay and service providers’ cost has not been addressed so far. In this paper, we study the joint optimization of service placement and load dispatching in the mobile cloud systems. We show this problem is unique to both the traditional caching problem in mobile networks and the content distribution problem in content distribution networks. We develop a set of efficient algorithms for service providers to achieve various trade-offs among the average latency of mobile users’ requests, and the cost of service providers. Our solution utilizes user's mobility pattern and services access pattern to predict the distribution of user's future requests, and then adapt the service placement and load dispatching online based on the prediction. We conduct extensive trace driven simulations. Results show our solution not only achieves much lower latency than directly accessing service from remote clouds, but also outperforms other classical benchmark algorithms in term of the latency, cost and algorithm running time.
机译:随着智能电话的激增以及由云提供的服务数量不断增加,用户从其移动设备请求云服务已司空见惯。由于较长的RTT和WAN中可能的拥塞,直接从Internet数据中心访问服务必然会导致高延迟。为了降低延迟,一些研究人员建议将服务“缓存”在访问网络中比互联网云更靠近最终用户的边缘云或智能路由器上。尽管“缓存”是一种有前途的技术,但到目前为止,还没有解决以最小化用户访问延迟和服务提供商成本的方式来放置服务和分派用户的请求。在本文中,我们研究了移动云系统中服务放置和负载分配的联合优化。我们显示此问题对于移动网络中的传统缓存问题和内容分发网络中的内容分发问题都是唯一的。我们为服务提供商开发了一套有效的算法,可以在移动用户请求的平均延迟时间和服务提供商的成本之间实现各种折衷。我们的解决方案利用用户的移动性模式和服务访问模式来预测用户未来请求的分布,然后根据该预测调整服务的位置和在线负载分配。我们进行广泛的跟踪驱动模拟。结果表明,与从远程云直接访问服务相比,我们的解决方案不仅实现了更低的延迟,而且在延迟,成本和算法运行时间方面也优于其他经典基准算法。

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