Green Data Centers: A Survey, Perspectives, and Future Directions <s> C. Challenges and Design Guidelines <s> Recent outages in several Web services have demonstrated the potential damage of availability disruptions, since millions of end users rely on these services powered by thousands of servers in large-scale Internet data centers. As Internet data centers continue to grow in scale and complexity, it has become a daunting task to monitor and manage thousands of servers simultaneously. This paper presents a cooperative monitoring framework to continuously monitor availability of thousands of servers in Internet data centers. We propose a simple yet effective algorithm for locating monitor nodes for the purposes of load balancing and resilience, and demonstrate the performance of this method through simulations based on dataset collected from a large Internet content provider. The results show that the monitoring load are well divided among the servers in the data centers, and cooperative monitoring adapts gracefully to the events of monitoring node failures. <s> BIB001 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> C. Challenges and Design Guidelines <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> BIB002 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> C. Challenges and Design Guidelines <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> BIB003 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> C. Challenges and Design Guidelines <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> BIB004 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> C. Challenges and Design Guidelines <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> BIB005
Currently, the PUE is a widely accepted and measured metric in data centers, whereas the use of other green metrics is much less widespread. There is no doubt that green efficiency metrics will continue to be developed and drive improvements in sustainability and environmental friendliness. However, an effective metric to characterize the energy and resource usage efficiency of a data center must satisfy the following design guidelines: • able to track the important and actionable parameters to assist operators in making data centers more green; • consistent, easy and cost effective to implement and report for management purposes; • feasible, simple and inexpensive to monitor. Thus, developing metrics that are well suited for reducing energy consumption is a challenging task with a significant contribution to the construction of "green" data centers. Moreover, the monitoring of green metrics in data centers assists in diagnosing energy inefficiencies and in the implementation of proactive mechanisms to improve energy efficiency. However, this issue has received little attention thus far, and existing monitors have been proposed for either particular architectures BIB002 or special applications BIB005 . The challenges are the large number of hosted servers or virtual machines and the availability of communication resources in data centers. Consequently, centralized monitoring techniques suffer from low scalability and elasticity. Monitoring techniques adopted from cooperative monitoring BIB001 can be applied to overcome these drawbacks by enabling distributed and robust monitoring solutions for large-scale, complex data centers. The key research question of interest is how to minimize the costs incurred while guaranteeing monitoring accuracy. Finally, although platforms such as GreenCloud BIB003 and GDCSim BIB004 have provided data center energy-aware simulation environments, the primary disadvantage of these platforms is that most of them are coarse grained and focused only on certain components or functions. For example, when a simulation of virtual machine migration considers only CPU resources, the results for virtual machine allocation may suffer from a failure to consider the bandwidth requirements. Therefore, the task of designing a system-wide experimental and simulation platform that integrates all components of a data center, such as the CPU, memory, cache, I/O, disk and communication network, requires further exploration.