Green Data Centers: A Survey, Perspectives, and Future Directions <s> A. Cooling and Workload Distribution <s> A cooling system is configured to adjust cooling fluid flow to various racks located throughout a data center based upon the detected or anticipated temperatures at various locations throughout the data center. In one respect, by substantially increasing the cooling fluid flow to those racks dissipating greater amounts of heat and by substantially decreasing the cooling fluid flow to those racks dissipating lesser amounts of heat, the amount of energy required to operate the cooling system may be relatively reduced. Thus, instead of operating the devices, e.g., compressors, fans, etc., of the cooling system at substantially 100 percent of the anticipated heat dissipation from the racks, those devices may be operated according to the actual cooling needs. In addition, the racks may be positioned throughout the data center according to their anticipated heat loads to thereby enable computer room air conditioning (CRAC) units located at various positions throughout the data center to operate in a more efficient manner. In one respect, the positioning of the racks may be determined through implementation of numerical modeling and metrology of the cooling fluid flow throughout the data center. In addition, the numerical modeling may be implemented to control the volume flow rate and velocity of the cooling fluid flow through each of the vents. <s> BIB001 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> A. Cooling and Workload Distribution <s> Internet-based applications and their resulting multitier distributed architectures have changed the focus of design for large-scale Internet computing. Internet server applications execute in a horizontally scalable topology across hundreds or thousands of commodity servers in Internet data centers. Increasing scale and power density significantly impacts the data center's thermal properties. Effective thermal management is essential to the robustness of mission-critical applications. Internet service architectures can address multisystem resource management as well as thermal management within data centers. <s> BIB002 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> A. Cooling and Workload Distribution <s> Trends towards consolidation and higher-density computing configurations make the problem of heat management one of the critical challenges in emerging data centers. Conventional approaches to addressing this problem have focused at the facilities level to develop new cooling technologies or optimize the delivery of cooling. In contrast to these approaches, our paper explores an alternate dimension to address this problem, namely a systems-level solution to control the heat generation through temperature-aware workload placement. ::: ::: We first examine a theoretic thermodynamic formulation that uses information about steady state hot spots and cold spots in the data center and develop real-world scheduling algorithms. Based on the insights from these results, we develop an alternate approach. Our new approach leverages the non-intuitive observation that the source of cooling inefficiencies can often be in locations spatially uncorrelated with its manifested consequences; this enables additional energy savings. Overall, our results demonstrate up to a factor of two reduction in annual data center cooling costs over location-agnostic workload distribution, purely through software optimizations without the need for any costly capital investment. <s> BIB003 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> A. Cooling and Workload Distribution <s> Power densities have been increasing rapidly at all levels of server systems. To counter the high temperatures resulting from these densities, systems researchers have recently started work on softwarebased thermal management. Unfortunately, research in this new area has been hindered by the limitations imposed by simulators and real measurements. In this paper, we introduce Mercury, a software suite that avoids these limitations by accurately emulating temperatures based on simple layout, hardware, and componentutilization data. Most importantly, Mercury runs the entire software stack natively, enables repeatable experiments, and allows the study of thermal emergencies without harming hardware reliability. We validate Mercury using real measurements and a widely used commercial simulator. We use Mercury to develop Freon, a system that manages thermal emergencies in a server cluster without unnecessary performance degradation. Mercury will soon become available from http://www.darklab.rutgers.edu. <s> BIB004 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> A. Cooling and Workload Distribution <s> Explosion in Big Data has led to a surge in extremely large-scale Big Data analytics platforms, resulting in burgeoning energy costs. Big Data compute model mandates strong data-locality for computational performance, and moves computations to data. State-of-the-art cooling energy management techniques rely on thermal-aware computational job placement/migration and are inherently data-placement-agnostic in nature. T* takes a novel, data-centric approach to reduce cooling energy costs and to ensure thermal-reliability of the servers. T* is cognizant of the uneven thermal-profile and differences in thermal-reliability-driven load thresholds of the servers, and the differences in the computational jobs arrival rate, size, and evolution life spans of the Big Data placed in the cluster. Based on this knowledge, and coupled with its predictive file models and insights, T* does proactive, thermal-aware file placement, which implicitly results in thermal-aware job placement in the Big Data analytics compute model. Evaluation results with one-month long real-world Big Data analytics production traces from Yahoo! show up to 42% reduction in the cooling energy costs with T* courtesy of its lower and more uniform thermal-profile and 9x better performance than the state-of-the-art data-agnostic cooling techniques. <s> BIB005 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> A. Cooling and Workload Distribution <s> Power shaving has recently been proposed to dynamically shave the power peaks of a data center with energy storage devices (ESD), such that more servers can be safely hosted. In addition to the reduction of capital investment (cap-ex), power shaving also helps cut the electricity bills (op-ex) of a data center by reducing the high utility tariffs related to peak power. However, existing work on power shaving focuses exclusively on electrical ESDs (e.g., UPS batteries) to shave the server-side power demand. In this paper, we propose TE-Shave, a generalized power shaving framework that exploits both UPS batteries and a new knob, thermal energy storage (TES) tanks equipped in many data centers. Specifically, TE-Shave utilizes stored cold water or ice to manipulate the cooling power, which accounts for 30–40% of the total power cost of a data center. Our extensive evaluation with real-world workload traces shows that TE-Shave saves cap-ex and op-ex up to $2,668/day and $825/day, respectively, for a data center with 17,920 servers. Even for future data centers that are projected to have more efficient cooling and thus a smaller portion of cooling power, e.g., a quarter of today's level, TE-Shave still leads to 28% more savings than existing work that focuses only on the server-side power. TE-Shave is also coordinated with traditional TES solutions for further reduced op-ex. <s> BIB006 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> A. Cooling and Workload Distribution <s> Lately, for geo-distributed data centers, a workload management approach that routes user requests to locations with cheaper and cleaner electricity has been developed to reduce energy consumption and cost. We consider two key aspects that have not been explored in this approach. First, through empirical studies, we find that the energy efficiency of cooling systems depends critically on the ambient temperature, which exhibits significant geographical diversity. Temperature diversity can be used to reduce the cooling energy overhead. Second, energy consumption comes from not only interactive workloads driven by user requests, but also delay tolerant batch workloads that run at the back-end. The elastic nature of batch workloads can be exploited to further reduce the energy cost. In this paper, we propose to make workload management temperature aware . We formulate the problem as a joint optimization of request routing for interactive workloads and capacity allocation for batch workloads. We develop a distributed algorithm based on an $m$ -block alternating direction method of multipliers (ADMM) algorithm that extends the classical two-block algorithm. We prove the convergence and rate of convergence results under general assumptions. Through trace-driven simulations, we find that our approach consistently provides 15-20 percent cooling energy reduction, and 5-20 percent overall cost reduction over existing methods. <s> BIB007
Optimization of the delivery of cooling is one strategy for temperature control BIB001 . Patel et al. BIB001 proposed a system to fully control a data center environment by linking all its attributes together, including distributed sensing, variable air conditioning and data aggregation. In addition, they exploited variable opening plenum tiles to assist in achieving more flexible control of the cooling distribution and in directing cooling resources where and when needed. The authors reported an energy savings of 50%. In BIB006 , the authors proposed TE-Shave, a generalized framework that exploited thermal energy storage (TES) tanks to reduce data center power. TE-Shave switched to the TES for stored cold water or ice to supplement the chillers and for heat exchange during peak power periods. Moreover, TE-Shave charged the TES through the preparation of cold water or ice by increasing the cooling power when the data center experienced power valleys. The authors demonstrated that their proposed framework could lead to a 28% savings in operational expenses compared with existing work that focused only on reducing the server-side power demand. Another approach to addressing temperature modulation is based on workload assignment and migration between servers to achieve thermal balance BIB002 , BIB003 , BIB004 . In BIB003 , the authors presented temperature-aware workload assignment algorithms to minimize the energy expended by the cooling infrastructure. Their algorithms produced the recommended theoretical heat distribution to maximize cooling efficiency and minimize the amount of heat recirculating within a data center. The proposed algorithms achieved cooling savings of more than 25%. Heath et al. BIB004 developed Freon, a user-space system to achieve thermal management without introducing unnecessary performance degradation. The main policy driving the operation of Freon was periodic temperature monitoring and the exertion of feedback control over the utilization of the servers by distributing requests based on load weights. Generally speaking, Freon dynamically shifted load away from hot servers and increased the load on other, "cool" servers. An extended version named Freon-EC was proposed to combine energy conservation and thermal management. Freon-EC turned off servers based on their temperatures and physical locations in the computer room. In fact, Freon-EC turned hot servers off and replaced them with servers from different regions that were unaffected by the thermal threat. Through experiments, Freon was proven to manage temperature with as little potential throughput degradation as possible. In BIB007 , the authors considered the problem of temperature-aware workload distribution in geo-distributed data centers. They explored two key aspects: 1) utilizing the geographical diversity of temperature to reduce cooling energy consumption and 2) exploiting the elastic nature of batch workloads to dynamically adjust capacity allocation. The problem was formulated as a joint optimization of request routing for interactive workloads and capacity allocation for batch workloads, and it was solved using a distributed m-block Alternating Direction Method of Multipliers (ADMM) algorithm. Extensive simulations using real-world traces revealed that their approach saved 15-20% in cooling energy and 5-20% in overall energy cost. In addition to these job-allocation-centric techniques, T * BIB005 provided a data-centric approach to reducing cooling energy costs in big data analytics cloud computing. Files were proactively allocated in a thermal-and energy-aware manner based on knowledge of server-profile, data-semantics, and cluster information. T * proposed predictive and non-predictive temperature-aware file allocation algorithms to reduce cooling energy costs and ensure high data-local performance. Evaluations using onemonth-long real-world traces demonstrated a reduction of up to 42% in cooling energy costs and a 9× better average response time compared with techniques that were data-allocationagnostic in nature.