Green Data Centers: A Survey, Perspectives, and Future Directions <s> B. Green Monitoring and Experimental Technique <s> Nowadays, power consumption of data centers has huge impacts on environments. Researchers are seeking to find effective solutions to make data centers reduce power consumption while keep the desired quality of service or service level objectives. Virtual Machine (VM) technology has been widely applied in data center environments due to its seminal features, including reliability, flexibility, and the ease of management. We present the GreenCloud architecture, which aims to reduce data center power consumption, while guarantee the performance from users' perspective. GreenCloud architecture enables comprehensive online-monitoring, live virtual machine migration, and VM placement optimization. To verify the efficiency and effectiveness of the proposed architecture, we take an online real-time game, Tremulous, as a VM application. Evaluation results show that we can save up to 27% of the energy when applying GreenCloud architecture. <s> BIB001 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> B. Green Monitoring and Experimental Technique <s> Cloud computing data centers are becoming increasingly popular for the provisioning of computing resources. The cost and operating expenses of data centers have skyrocketed with the increase in computing capacity. Several governmental, industrial, and academic surveys indicate that the energy utilized by computing and communication units within a data center contributes to a considerable slice of the data center operational costs. <s> BIB002 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> B. Green Monitoring and Experimental Technique <s> Energy consumption in data centers can be reduced by efficient design of the data centers and efficient management of computing resources and cooling units. A major obstacle in the analysis of data centers is the lack of a holistic simulator, where the impact of new computing resource (or cooling) management techniques can be tested with diffierent designs (i.e., layouts and configurations) of data centers. To fill this gap, this paper proposes Green Data Center Simulator (GDCSim) for studying the energy efficiency of data centers under various data center geometries, workload characteristics, platform power management schemes, and scheduling algorithms. GDCSim is used to iteratively design green data centers. Further, it is validated against established CFD simulators. GDCSim is developed as a part of the BlueTool infrastructure project at Impact Lab. <s> BIB003 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> B. Green Monitoring and Experimental Technique <s> As energy has become one of the key operating costs in running a data center and power waste commonly exists, it is essential to observe and reduce energy inefficiency inside data centers. In this paper, we develop an innovative framework, called PowerTracer, for diagnosing energy-inefficiency. Inside the framework, we first present a resource tracing method based on request tracing in multi-tier services of black boxes. Then, we propose a generalized methodology of applying a request tracing approach for energy-inefficiency diagnosis in multi-tier service systems. With insights into service performance and resource consumption of individual requests, we develop a bottleneck diagnosis tool that pinpoints the root causes of energy inefficiency. We implement the prototype and conduct experiments to validate its effectiveness. <s> BIB004 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> B. Green Monitoring and Experimental Technique <s> To optimize datacenter energy efficiency, SimWare analyzes the power consumption of servers, cooling units, and fans as well as the effects of heat recirculation and air supply timing. Experiments using SimWare show a high loss of cooling efficiency due to the nonuniform inlet air temperature distribution across servers. <s> BIB005
Monitoring is an effective method of detecting and proactively mitigating the failure of data centers. Moreover, we will explore efforts that yield the tracks and traces of energy usage, thermal emission, and power distribution for individual devices. Based on the collected information, intelligent mechanisms can then react to increase the energy efficiency and productivity of a data center. In BIB001 , the authors presented a comprehensive online monitoring service in their GreenCloud architecture. The monitoring service monitored and collected data concerning resource utilization, application workloads directed through virtual machine hypervisors, and power usage using an intelligent power distribution unit. Analyzing and mining this collected information provided a clear understanding of the data center energy usage and temperature behavior. Furthermore, the monitoring service helped to consolidate the Measure the percentage of hours in a year that a water-side economizer system is used to provide "free cooling" Lawrence Berkeley National Laboratory, 2009 →100% 33% = 2900 24×365  virtual machine workload and achieve significant energy savings while guaranteeing the real-time performance required for sensitive applications. Using a different approach, the authors of BIB004 developed PowerTracer, a request-tracing strategy for diagnosing energy inefficiencies in multi-tier service systems. They first profiled the patterns of requests through kernel instrumentation and then measured the server-side latency and service time of each tier. Finally, PowerTracer collected the resource consumption information for individual requests and analyzed the features of each tier and each pattern. By thus gaining insights into the root causes of energy inefficiency, the authors could devise efficient power-saving methods for multitier applications. Even in the initial phases of the design and development of a data center, an effective experimental and simulation platform enables the operator to identify and understand the design aspects that are critical to energy and resource efficiency. EEFSim, a virtualized data center simulator for cloud computing research, was designed and developed in  . The simulator was demonstrated to be suitable for testing, as it reproduced the behavior of a real cloud framework and the execution information that it offered. Another advantage of this simulator was that it could be easily used to evaluate the power performance of a system by testing different solutions and approaches within merely a few minutes. As a result, the simulator offered the possibility to validate different scheduling and migration policies for their power consumption associated with virtual machines in a cloud data center. Eliazovich et al. BIB002 presented GreenCloud, a packet-level simulator for energy-aware studies of cloud computing data centers. This simulator could be used to capture the details of the energy consumption of various data center components (e.g., servers, switches, and links) as well as packet-level communication patterns. Its effectiveness in utilizing power management schemes was demonstrated for two-tier, three-tier, and three-tier high-speed data center architectures. A more powerful tool for analyzing green data center design and resource management, named GDCSim, was proposed in BIB003 . This simulation tool was used for the iterative design of green data centers. Specifically, it captured the interdependencies between online resource management schemes and the physical behavior of data centers. The objective of the simulator was to generate data center configurations for particular purposes, such as CPU sleep-state transitions and DVFS for power management, as well as thermal behavior characterization. In BIB005 , the authors proposed a simulator, named SimWare, to analyze the power consumption of servers, cooling units, and fans as well as the effects of heat recirculation and air supply timing. SimWare considered the time required for air to travel from the CRACs to the servers and could evaluate user-defined job scheduling and virtual machine management algorithms. Data center designers could use this simulator to evaluate energy-conserving policies and to assess mechanical design options such as server placement, airflow management and CRAC control strategies.