A clear trend in the evolution of network-based services is the ever-increasing amount of multimedia data involved. This trend towards big-data multimedia processing finds its natural placement together with the adoption of the cloud computing paradigm, that seems the best solution to cope with the demands of a highly fluctuating workload that characterizes this type of services. However, as cloud data centers become more and more powerful, energy consumption becomes a major challenge both for environmental concerns and for economic reasons. An effective approach to improve energy efficiency in cloud data centers is to rely on traffic engineering techniques to dynamically adapt the number of active servers to the current workload. Towards this aim, we propose a joint computing-plus-communication optimization framework exploiting virtualization technologies, called MMGreen. Our proposal specifically addresses the typical scenario of multimedia data processing with computationally intensive tasks and exchange of a big volume of data. The proposed framework not only ensures users the Quality of Service (through Service Level Agreements), but also achieves maximum energy saving and attains green cloud computing goals in a fully distributed fashion by utilizing the DVFS-based CPU frequencies. To evaluate the actual effectiveness of the proposed framework, we conduct experiments with MMGreen under real-world and synthetic workload traces. The results of the experiments show that MMGreen may significantly reduce the energy cost for computing, communication and reconfiguration with respect to the previous resource provisioning strategies, respecting the SLA constraints.

中文翻译：

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云系统中多媒体处理的自适应计算加通信优化框架

基于网络的服务发展的一个明显趋势是涉及的多媒体数据量不断增加。这种大数据多媒体处理的趋势与云计算范式的采用相结合，这似乎是应对此类服务特征的高度波动的工作负载需求的最佳解决方案。然而，随着云数据中心变得越来越强大，能源消耗成为环境问题和经济原因的主要挑战。提高云数据中心能效的一种有效方法是依靠流量工程技术来动态调整活动服务器的数量以适应当前的工作负载。为了这个目标，我们提出了一种利用虚拟化技术的联合计算加通信优化框架，称为 MMGreen。我们的提议专门针对具有计算密集型任务和大量数据交换的多媒体数据处理的典型场景。所提出的框架不仅可以确保用户的服务质量（通过服务级别协议），还可以通过利用基于 DVFS 的 CPU 频率以完全分布式的方式实现最大程度的节能和绿色云计算目标。为了评估所提出框架的实际有效性，我们在真实世界和合成工作负载跟踪下对 MMGreen 进行了实验。实验结果表明，MMGreen 可以显着降低计算的能源成本，