As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. The average consumption of a single data center is equivalent to the energy consumption of 25.000 households. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimization policies, but accurate and fast power modeling is a complex challenge for high-end servers not yet satisfied by analytical approaches. This work proposes an automatic method, based on Multi-Objective Particle Swarm Optimization, for the identification of power models of enterprise servers in Cloud data centers. Our approach, as opposed to previous procedures, does not only consider the workload consolidation for deriving the power model, but also incorporates other non traditional factors like the static power consumption and its dependence with temperature. Our experimental results shows that we reach slightly better models than classical approaches, but simultaneously simplifying the power model structure and thus the numbers of sensors needed, which is very promising for a short-term energy prediction. This work, validated with real Cloud applications, broadens the possibilities to derive efficient energy saving techniques for Cloud facilities.
Summary
Computational demand in data centers is increasing because of the growing popularity of Cloud applications. However, data centers are becoming unsustainable in terms of power consumption and growing energy costs so Cloud providers have to face the major challenge of placing them on a more scalable curve. Also, Cloud services are provided under strict Service Level Agreement conditions, so trade‐offs between energy and performance have to be taken into account. Techniques as Dynamic Voltage and Frequency Scaling (DVFS) and consolidation are commonly used to reduce the energy consumption in data centers, although they are applied independently and their effects on Quality of Service are not always considered. Thus, understanding the relationship between power, DVFS, consolidation, and performance is crucial to enable energy‐efficient management at the data center level. In this work, we propose a DVFS policy that reduces power consumption while preventing performance degradation, and a DVFS‐aware consolidation policy that optimizes consumption, considering the DVFS configuration that would be necessary when mapping Virtual Machines to maintain Quality of Service. We have performed an extensive evaluation on the CloudSim toolkit using real Cloud traces and an accurate power model based on data gathered from real servers. Our results demonstrate that including DVFS awareness in workload management provides substantial energy savings of up to 41.62% for scenarios under dynamic workload conditions. These outcomes outperforms previous approaches, that do not consider integrated use of DVFS and consolidation strategies.
Abstract-The computational and cooling power demands of enterprise servers are increasing at an unsustainable rate. Understanding the relationship between computational power, temperature, leakage, and cooling power is crucial to enable energy-efficient operation at the server and data center levels. This paper develops empirical models to estimate the contributions of static and dynamic power consumption in enterprise servers for a wide range of workloads, and analyzes the interactions between temperature, leakage, and cooling power for various workload allocation policies. We propose a cooling management policy that minimizes the server energy consumption by setting the optimum fan speed during runtime. Our experimental results on a presently shipping enterprise server demonstrate that including leakage awareness in workload and cooling management provides additional energy savings without any impact on performance.
The reliable operation of modern infrastructures depends on computerized systems and Supervisory Control and Data Acquisition (SCADA) systems, which are also based on the data obtained from sensor networks. The inherent limitations of the sensor devices make them extremely vulnerable to cyberwarfare/cyberterrorism attacks. In this paper, we propose a reputation system enhanced with distributed agents, based on unsupervised learning algorithms (self-organizing maps), in order to achieve fault tolerance and enhanced resistance to previously unknown attacks. This approach has been extensively simulated and compared with previous proposals.
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