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首页> 外文会议>7th ACM international workshop on data engineering for wireless and mobile access 2008 >Data Scheduling for Multi-item Requests in Multi-channel On-demand Broadcast Environments
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Data Scheduling for Multi-item Requests in Multi-channel On-demand Broadcast Environments

机译：多频道点播广播环境中多项目请求的数据调度

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摘要

On-demand broadcast is an effective wireless data dissemination technique to enhance system scalability and capability to handle dynamic user access patterns. With the rapid development of mobile applications, there is an increasing need for systems to support efficient processing of requests for multiple data items in multiple channels broadcast environments. Few studies, however, have considered the on-demand scheduling mechanisms for multi-item requests in multi-channel broadcast environments. In this paper, we investigate the scheduling problems arising in this new environment and observe that existing single-item requests based algorithms are unable to perform efficiently. Two potential problems are identified and examined. First, these algorithms take an excessively long time to serve the last few data items in a request because they disregard the relationship between data items and their parent requests. We claim that these algorithms suffer the request starvation problem in scheduling multi-item requests. Second, these algorithms cannot achieve the expected performance gain with multiple channels. We observed a broadcast mismatch problem in multi-channel broadcast environments. Thus, we propose an innovative algorithm to solve these two potential problems. The simulation results show that the performance of the proposed algorithm is superior to other classical algorithms under a variety of circumstances.
机译：点播是一种有效的无线数据分发技术，可增强系统可伸缩性和处理动态用户访问模式的能力。随着移动应用的快速发展，越来越需要对在多频道广播环境中支持对多个数据项的请求进行有效处理的系统。然而，很少有研究考虑在多频道广播环境中针对多项目请求的按需调度机制。在本文中，我们调查了在这种新环境中出现的调度问题，并观察到现有的基于单项请求的算法无法有效执行。确定并检查了两个潜在问题。首先，这些算法花费过多时间来服务请求中的最后几个数据项，因为它们忽略了数据项与其父请求之间的关系。我们声称这些算法在调度多项目请求时遇到了请求匮乏的问题。其次，这些算法无法在多个通道上获得预期的性能增益。我们在多频道广播环境中观察到广播不匹配问题。因此，我们提出了一种创新的算法来解决这两个潜在的问题。仿真结果表明，该算法在各种情况下的性能均优于其他经典算法。

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