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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Load Balancing in a Cluster-Based Web Server for Multimedia Applications
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Load Balancing in a Cluster-Based Web Server for Multimedia Applications

机译：用于多媒体应用程序的基于群集的Web服务器中的负载平衡

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We consider a cluster-based multimedia Web server that dynamically generates video units to satisfy the bit rate and bandwidth requirements of a variety of clients. The media server partitions the job into several tasks and schedules them on the backend computing nodes for processing. For stream-based applications, the main design criteria of the scheduling are to minimize the total processing time and maintain the order of media units for each outgoing stream. In this paper, we first design, implement, and evaluate three scheduling algorithms, first fit (FF), stream-based mapping (SM), and adaptive load sharing (ALS), for multimedia transcoding in a cluster environment. We determined that it is necessary to predict the CPU load for each multimedia task and schedule them accordingly due to the variability of the individual jobs/tasks. We, therefore, propose an online prediction algorithm that can dynamically predict the processing time per individual task (media unit). We then propose two new load scheduling algorithms, namely, prediction-based least load first (P-LLF) and prediction-based adaptive partitioning (P-AP), which can use prediction to improve the performance. The performance of the system is evaluated in terms of system throughput, out-of-order rate of outgoing media streams, and load balancing overhead through real measurements using a cluster of computers. The performance of the new load balancing algorithms is compared with all other load balancing schemes to show that P-AP greatly reduces the delay jitter and achieves high throughput for a variety of workloads in a heterogeneous cluster. It strikes a good balance between the throughput and output order of the processed media units
机译：我们考虑一种基于群集的多媒体Web服务器，该服务器动态生成视频单元，以满足各种客户端的比特率和带宽要求。媒体服务器将作业划分为多个任务，并将它们安排在后端计算节点上进行处理。对于基于流的应用程序，调度的主要设计标准是最大程度地减少总处理时间并维护每个传出流的媒体单元顺序。在本文中，我们首先为集群环境中的多媒体代码转换设计，实现和评估三种调度算法，即首次拟合（FF），基于流的映射（SM）和自适应负载共享（ALS）。由于各个作业/任务的可变性，我们确定有必要预测每个多媒体任务的CPU负载并相应地安排它们。因此，我们提出了一种在线预测算法，该算法可以动态预测每个单独任务（媒体单元）的处理时间。然后，我们提出了两种新的负载调度算法，即基于预测的最小负载优先（P-LLF）和基于预测的自适应分区（P-AP），它们可以使用预测来提高性能。通过使用一组计算机进行实际测量，可以评估系统的性能，包括系统吞吐量，出站媒体流的乱序速率以及负载平衡开销。将新的负载均衡算法的性能与所有其他负载均衡方案进行了比较，表明P-AP大大降低了延迟抖动，并为异构集群中的各种工作负载实现了高吞吐量。它在处理后的媒体单元的吞吐量和输出顺序之间达到了很好的平衡

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