不同负载均衡策略下服务器功耗和硬件资源消耗分析

W. Dargie, A. Schill
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引用次数: 17

摘要

大多数Internet应用程序在集群设置中采用某种负载平衡策略,以实现可靠的服务提供以及处理资源瓶颈。但是,这些策略可能无法确保服务器中\textit{所有}硬件资源的使用效率相同。本文通过实验研究了采用不同负载均衡策略分配工作负载时多媒体服务器集群的功耗与资源利用率之间的关系。我们的观察结果如下:(1)单个硬件资源的瓶颈可能导致整个系统的大量未充分利用。(2)将整个集群的网络带宽增加十倍,可以使单个服务器的吞吐量增加一倍。单个服务器的相关功耗增量为1.2% only. (3) For TCP-based applications, session information is more useful than other types of status information to utilize power more efficiently. (4) The use of dynamic frequency scaling does not affect the overall throughput of IO-bound applications but reduces the power consumption of the servers; but this reduction is only 12% of the overall power consumption. More power can be saved by avoiding a resource bottleneck or through service consolidation.
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of the Power and Hardware Resource Consumption of Servers under Different Load Balancing Policies
Most Internet applications employ some kind of load balancing policies in a cluster setting to achieve reliable service provision as well as to deal with a resource bottleneck. However, these policies may not ensure the utilization of \textit{all} of the hardware resources in a server equally efficiently. This paper experimentally investigates the relationship between the power consumption and resource utilization of a multimedia server cluster when different load balancing policies are used to distribute a workload. Our observations are the following: (1) A bottleneck on a single hardware resource can lead to a significant amount of underutilization of the entire system. (2) A ten times increment in the network bandwidth of the entire cluster can double the throughput of individual servers. The associated increment in power consumption of the individual servers is 1.2% only. (3) For TCP-based applications, session information is more useful than other types of status information to utilize power more efficiently. (4) The use of dynamic frequency scaling does not affect the overall throughput of IO-bound applications but reduces the power consumption of the servers; but this reduction is only 12% of the overall power consumption. More power can be saved by avoiding a resource bottleneck or through service consolidation.
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