Short-term Load Prediction and Energy-Aware Load Balancing for Data Centers Serving Online
Short-term Load Prediction and Energy-Aware Load Balancing for Data Centers Serving Online
复制标题
在线服务数据中心的短期负载预测和能源感知负载平衡
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
K. Ghose
中科院分区：
文献类型：
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作者：
Tyler Stachecki;K. Ghose
我们引入并评估了一种自动化技术，用于在满足在线服务的数据中心中动态配置服务器容量。短期负载预测用于实现有效的能源感知数据中心负载平衡技术，该技术可在不影响交付性能的情况下实现显着的节能。在我们由 150 台 Linux 服务器组成的异构数据中心中，负载均衡器能够在不同规模的配置和突发性工作负载中实现总体服务器能耗平均降低 30.8%，同时响应时间和吞吐量的下降最小。
We introduce and evaluate an automated technique for dynamically provisioning server capacity in a data center that caters to on-line services. Shortterm load prediction is used to realize an effective energy-aware data center load balancing technique that achieves significant energy savings without compromising the delivered performance. In our heterogeneous datacenter consisting of 150 Linux servers, the load balancer is able to achieve a 30.8% reduction in overall server energy consumption on average across differently-sized configurations and across bursty workloads with minimal degradation in both response time and throughput.