Green Data Centers: A Survey, Perspectives, and Future Directions <s> D. Renewable Energy Access <s> Reducing the energy footprint of data centers continues to receive significant attention due to both its financial and environmental impact. There are numerous methods that limit the impact of both factors, such as expanding the use of renewable energy or participating in automated demand-response programs. To take advantage of these methods, servers and applications must gracefully handle intermittent constraints in their power supply. In this paper, we propose blinking---metered transitions between a high-power active state and a low-power inactive state---as the primary abstraction for conforming to intermittent power constraints. We design Blink, an application-independent hardware-software platform for developing and evaluating blinking applications, and define multiple types of blinking policies. We then use Blink to design BlinkCache, a blinking version of memcached, to demonstrate the effect of blinking on an example application. Our results show that a load-proportional blinking policy combines the advantages of both activation and synchronous blinking for realistic Zipf-like popularity distributions and wind/solar power signals by achieving near optimal hit rates (within 15% of an activation policy), while also providing fairer access to the cache (within 2% of a syn- chronous policy) for equally popular objects. <s> BIB001 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> D. Renewable Energy Access <s> This paper argues for "Free Lunch", a computation architecture that exploits otherwise wasted renewable energy by (i) colocating datacentres with these remote energy sources, (ii) connecting them over a dedicated network, and (iii) providing a software framework that supports the seamless execution and migration of virtual machines in the platform according to power availability. This work motivates and outlines the architecture and demonstrates its viability with a case study. Additionally, we discuss the major technical challenges facing the successful deployment of Free Lunch and the limiting factors inherent in its design. <s> BIB002 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> D. Renewable Energy Access <s> In this paper, we propose GreenSlot, a parallel batch job scheduler for a datacenter powered by a photovoltaic solar array and the electrical grid (as a backup). GreenSlot predicts the amount of solar energy that will be available in the near future, and schedules the workload to maximize the green energy consumption while meeting the jobs' deadlines. If grid energy must be used to avoid deadline violations, the scheduler selects times when it is cheap. Our results for production scientific workloads demonstrate that Green-Slot can increase green energy consumption by up to 117% and decrease energy cost by up to 39%, compared to a conventional scheduler. Based on these positive results, we conclude that green datacenters and green-energy-aware scheduling can have a significant role in building a more sustainable IT ecosystem. <s> BIB003 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> D. Renewable Energy Access <s> To reduce the negative environmental implications (e.g., CO 2 emission and global warming) caused by the rapidly increasing energy consumption, many Internet service operators have started taking various initiatives to operate their cloud-scale data centers with renewable energy. Unfortunately, due to the intermittent nature of renewable energy sources such as wind turbines and solar panels, currently renewable energy is often more expensive than brown energy that is produced with conventional fossil-based fuel. As a result, utilizing renewable energy may impose a considerable pressure on the sometimes stringent operation budgets of Internet service operators. Therefore, two key questions faced by many cloud-service operators are 1) how to dynamically distribute service requests among data centers in different geographical locations, based on the local weather conditions, to maximize the use of renewable energy, and 2) how to do that within their allowed operation budgets. ::: ::: In this paper, we propose GreenWare, a novel middleware system that conducts dynamic request dispatching to maximize the percentage of renewable energy used to power a network of distributed data centers, subject to the desired cost budget of the Internet service operator. Our solution first explicitly models the intermittent generation of renewable energy, e.g., wind power and solar power, with respect to varying weather conditions in the geographical location of each data center. We then formulate the core objective of GreenWare as a constrained optimization problem and propose an efficient request dispatching algorithm based on linear-fractional programming (LFP). We evaluate GreenWare with real-world weather, electricity price, and workload traces. Our experimental results show that GreenWare can significantly increase the use of renewable energy in cloud-scale data centers without violating the desired cost budget, despite the intermittent supplies of renewable energy in different locations and time-varying electricity prices and workloads. <s> BIB004 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> D. Renewable Energy Access <s> Interest has been growing in powering datacenters (at least partially) with renewable or "green" sources of energy, such as solar or wind. However, it is challenging to use these sources because, unlike the "brown" (carbon-intensive) energy drawn from the electrical grid, they are not always available. This means that energy demand and supply must be matched, if we are to take full advantage of the green energy to minimize brown energy consumption. In this paper, we investigate how to manage a datacenter's computational workload to match the green energy supply. In particular, we consider data-processing frameworks, in which many background computations can be delayed by a bounded amount of time. We propose GreenHadoop, a MapReduce framework for a datacenter powered by a photovoltaic solar array and the electrical grid (as a backup). GreenHadoop predicts the amount of solar energy that will be available in the near future, and schedules the MapReduce jobs to maximize the green energy consumption within the jobs' time bounds. If brown energy must be used to avoid time bound violations, GreenHadoop selects times when brown energy is cheap, while also managing the cost of peak brown power consumption. Our experimental results demonstrate that GreenHadoop can significantly increase green energy consumption and decrease electricity cost, compared to Hadoop. <s> BIB005 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> D. Renewable Energy Access <s> Large-scale computing systems such as data centers are facing increasing pressure to cap their carbon footprint. Integrating emerging clean energy solutions into computer system design therefore gains great significance in the green computing era. While some pioneering work on tracking variable power budget show promising energy efficiency, they are not suitable for data centers due to lack of performance guarantee when renewable generation is low and fluctuant. In addition, our characterization of wind power behavior reveals that data centers designed to track the intermittent renewable power incur up to 4X performance loss due to inefficient and redundant load matching activities. As a result, mitigating operational overhead while still maintaining desired energy utilization becomes the most significant challenge in managing server clusters on intermittent renewable energy generation. In this paper we take a first step in digging into the operational overhead of renewable energy powered data center. We propose iSwitch, a lightweight server power management that follows renewable power variation characteristics, leverages existing system infrastructures, and applies supply/load cooperative scheme to mitigate the performance overhead. Comparing with state-of-the-art renewable energy driven system design, iSwitch could mitigate average network traffic by 75%, peak network traffic by 95%, and reduce 80% job waiting time while still maintaining 96% renewable energy utilization. We expect that our work can help computer architects make informed decisions on sustainable and high-performance system design. <s> BIB006 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> D. Renewable Energy Access <s> Large-scale Internet applications, such as content distribution networks, are deployed across multiple datacenters and consume massive amounts of electricity. To provide uniformly low access latencies, these datacenters are geographically distributed and the deployment size at each location reflects the regional demand for the application. Consequently, an application's environmental impact can vary significantly depending on the geographical distribution of end-users, as electricity cost and carbon footprint per watt is location specific. In this paper, we describe FORTE: Flow Optimization based framework for request-Routing and Traffic Engineering. FORTE dynamically controls the fraction of user traffic directed to each datacenter in response to changes in both request workload and carbon footprint. It allows an operator to navigate the three-way tradeoff between access latency, carbon footprint, and electricity costs and to determine an optimal datacenter upgrade plan in response to increases in traffic load. We use FORTE to show that carbon taxes or credits are impractical in incentivizing carbon output reduction by providers of large-scale Internet applications. However, they can reduce carbon emissions by 10% without increasing the mean latency nor the electricity bill. <s> BIB007 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> D. Renewable Energy Access <s> Internet-scale data centers (IDCs) have rapidly proliferated to such an extent that their energy consumption and GreenHouse Gas (GHG) emissions have become an important concern to society. As a result, many IDC operators have started using renewable energy, e.g., wind power, to power their data centers. Unfortunately, the utilization of wind energy has stayed at a low ratio due to the intermittent nature of wind. This paper makes the case that it is in fact possible for a distributed IDC system to exploit multiple uncorrelated wind energy sources to significantly reduce the effect of intermittency and nearly achieve “entirely green” cloud-scale services. This result is obtained based on the analysis of real-world wind power traces from 69 wind farms. The idea is to leverage the front-end load dispatching server to send work to the location where wind power is available. We propose a wind-power-aware (WPA) policy that routes jobs based only on the current states of workloads and wind power availabilities in the data centers. We show that with the WPA policy more than 95% of energy consumption in IDCs can in fact be satisfied by wind power, and, secondly, that achieving this does not require the delaying of processing of jobs due to wind availability. We also show that the locations where data centers are placed play an important role in achieving high wind power utilization. Our analysis shows that wind power utilization can generally lie in a range from 44% to 96%, depending on how the locations of wind farms are selected. We propose a method for location selection that uses the coefficient of variation instead of the correlation coefficient, and show that with this method the utilization can lie in the high end of the above range. Finally, we verify these results by simulations that are based on real-world traces for both workloads and wind power generations. <s> BIB008 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> D. Renewable Energy Access <s> Energy expenditure has become a significant fraction of data center operating costs. Recently, "geographical load balancing" has been proposed to reduce energy cost by exploiting the electricity price differences across regions. However, this reduction of cost can paradoxically increase total energy use. We explore whether the geographical diversity of Internet-scale systems can also provide environmental gains. Specifically, we explore whether geographical load balancing can encourage use of "green" renewable energy and reduce use of "brown" fossil fuel energy. We make two contributions. First, we derive three distributed algorithms for achieving optimal geographical load balancing. Second, we show that if the price of electricity is proportional to the instantaneous fraction of the total energy that is brown, then geographical load balancing significantly reduces brown energy use. However, the benefits depend strongly on dynamic energy pricing and the form of pricing used. <s> BIB009
Expanding the use of renewable energy is another route toward green data centers. Some data center owners have already investigated and accepted alternative energies, such as solar, geothermal and wind energy  ,  . However, utilizing these green energies is challenging because of their restricted, intermittent and unstable nature. Therefore, the main question that must be addressed is how to exploit renewable energy and overcome the associated hindrances by exploiting the unique features of data centers. Sharma et al. BIB001 developed Blink to address intermittent power constraints. Based on workload characteristics and energy constraints, Blink determined the power state of the servers and minimized performance degradation. Because the power varied and the energy stored in the batteries was for short-term use, Blink's policy had potential benefits for distributed applications. The authors constructed a cluster of 10 low-power motherboards powered by an array of micro wind turbines and solar panels to evaluate the performance under intermittent power constraints. They demonstrated that an asymmetric load-proportional policy could increase fairness without significantly sacrificing the cache's hit rate to achieve a blinking version of memcached. Considering the location restrictions of renewable energy, Akoush et al. BIB002 proposed a computation architecture to avoid the necessity of expensive infrastructure investment. Their design consisted of three relevant components: 1) data centers collocated with renewable energy sources, 2) data centers linked together over a dedicated communication network, and 3) a software framework that supported the seamless execution and migration of virtual machines depending on power availability. Although they demonstrated the viability of the design in a case study, the authors also noted the technical challenges and inherent limitations of their approach, such as the efficiency of virtual machine migration between data centers and suitable workload concerns. In  , the authors leveraged tunable energy, such as that provided by fuel cells and gas turbines, to propose a power management approach that enabled data center operation with high performance and low overhead. Unlike previous work, in which the workload had been forced to track the variable power budget, their approach explored the possibility of achieving a renewable energy supply to follow the data center power demand. To overcome issues of performance degradation, they proposed two adaptive load tuning schemes, named DGR Boost and UPS Boost, based on mature computer tuning approaches (such as DVFS and UPS batteries). Evaluations performed on real-world data center traces and industry data on distributed generation systems indicated that their technique achieved a 37% improvement in performance over existing supply-tracking-based designs and saved 100 metric tons of carbon emissions annually, given that the power consumption of the data center was 10MW. If powered only by variable sources of green energy, a data center will experience unpredictable performance for applications. The most reasonable approach is to incorporate renewable energy into the electrical grid. In BIB003 and BIB005 , the authors considered a data center powered by a solar array and the electrical grid. Two scheduler frameworks, named GreenSlot and GreenHadoop, were proposed for parallel batch jobs and data-processing jobs, respectively. Both schedulers used historical data and weather forecasts to predict the amount of solar energy that would be available in the future, with the intent of maximizing the use of green energy. Because of the inherent natural variability in the availability of solar energy, grid energy was used to avoid deadline violations. The results obtained using a real-world trace demonstrated that GreenSlot could increase green energy consumption by up to 117% compared with a conventional backfilling scheduler, whereas GreenHadoop could increase green energy consumption by up to 31% compared with the Hadoop algorithm. However, the inefficient and redundant load-matching activities of these algorithms can incur performance losses. iSwith BIB006 explored the design trade-offs between energy utilization and load tuning (e.g., DVFS and CPU power states) for a data center partially powered by intermittent renewable energy. iSwith combined optimization of the supply-side fluctuation with minimization of the load-fluctuation-induced overhead to mitigate performance loss. This system could reduce job waiting time by 80% and also mitigate peak network traffic by 95% and average network traffic by 75% while still maintaining 96% renewable energy utilization. To avoid transmitting renewable energy over long distances, which leads to significant attenuation losses BIB002 , several proposed solutions have suggest dispatching traffic to multiple data centers in different geographical locations to use the renewable energy available nearby BIB009 , BIB008 , BIB007 , BIB004 . In BIB009 , the authors noted that geographical traffic routing could significantly reduce the use of brown energy if the price of energy was dynamically determined in proportion to the instantaneous fraction of the total energy that was brown. Moreover, the degree of use of renewable energy was dependent on the form of the pricing model and the acceptability of this dynamic energy pricing. In BIB008 , the authors exploited multiple uncorrelated wind energy sources to significantly reduce the effects of intermittency and to achieve almost entirely green Internet-scale data centers (IDCs). They proposed WPA, a policy that routed jobs based on the current states of the workloads and wind power availabilities at different data centers. The authors showed that more than 95% of the energy consumption in IDCs could be satisfied by wind power without delaying the processing of jobs. Similarly, Gao et al. BIB007 dynamically controlled the fraction of user traffic directed to each geographically distributed data center with the objective of navigating the three-way trade-off among carbon footprint, access latency and electricity cost. The authors showed that carbon emissions could be reduced by 10% without increasing either the mean latency or the electricity cost. However, at present, renewable energy is more expensive to use than is brown energy produced with fossil-based fuel. Zhang et al. BIB004 considered the problem of maximizing the percentage of renewable energy used to reduce the negative environmental implications given certain operation budgets. They proposed a novel middleware system that dynamically distributed requests among different data centers. The authors formulated their objective as a constrained optimization problem and solved it via linear-fractional programming. The authors experimentally demonstrated that their system could significantly increase the use of renewable energy without violating the desired operational cost budget, despite the intermittent nature of the supplies of renewable energy at different locations and the time variation in electricity prices and workload traces.