SolarCore: Solar energy driven multi-core architecture power management

Li, Chao; Zhang, Wangyuan; Cho, Chang-Burm; Li, Tao

doi:10.1109/hpca.2011.5749729

Cited by 68 publications

(46 citation statements)

References 23 publications

(24 reference statements)

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“…Furthermore, for a high-quality solar cell, it typically has a low series resistance R s but a high parallel resistance R sh . As a result, the solar model in this work only takes into considerations the series resistance (i.e., R sh = ∞), which is consistent with the prior study [31]. We thus have the fact that I ph can be approximated by I sc for simplicity, where I sc is the short-circuit current.…”

Section: Solar Power Modelsupporting

confidence: 58%

“…Namely, p solar = v(G, T )·i(G, T ). It has been demonstrated that the power output p solar generated by a PV panel shows a unique maximum value under uniform irradiation and temperature [31,41]. In order to achieve the maximum efficiency of solar plants, some researchers have already put efforts in extracting the maximum power point from solar plants [19,27].…”

Section: Solar Power Modelmentioning

confidence: 99%

“…A variety of meteorological data, including irradiances, temperature, and wind speed, is covered in those records from the MIDC. Moreover, prior studies have shown that the data from the MIDC is sufficiently accurate [31]. In particular, we use meteorological data from the four stations, e.g., Sun Spot One, Loyola Marymount University Rotating Shadowband Radiometer, Oak Ridge National Laboratory and La Ola Lanai, since they have consistent time periods with available meteorological data, beginning from June 1st, 2010 to June 30th, 2010.…”

Section: Renewable Energy Availabilitymentioning

confidence: 99%

“…Greening data centers is becoming an increasingly important topic in operating cloud-scale data center networks for Internet service operators, due to (1) data centers having become major energy consumers [44] and (2) the global energy crisis and environmental concerns (e.g., global warming) [31]. To our best knowledge, our study is the first one that proposes to maximally use the renewable energy supplied by the local power utilities for Internet service operators, while being aware of the time-varying electricity price and enforcing a desired cost budget.…”

Section: Related Workmentioning

confidence: 99%

“…We first model the intermittent generation of renewable energy, i.e., wind power and solar power, with respect to the varying weather conditions in the geographical location of each data center. For example, the available wind power generated from wind turbines is modeled based on the ambient wind speed [35,9], while the available solar power from solar plants is estimated by modeling the maximum power point on irradiance (i.e., solar energy per unit area of the solar panel's face) and temperature [31,41]. Based on the models, we formulate the core objective of GreenWare as a constrained optimization problem, in which the constraints capture the Quality of Service (QoS, e.g., response time) requirements from customers, the intermittent availabilities of renewable energy in different locations, the peak power limit of each data center, and the monthly cost budget of the Internet service operator.…”

Section: Introductionmentioning

confidence: 99%

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GreenWare: Greening Cloud-Scale Data Centers to Maximize the Use of Renewable Energy

Zhang

Wang

2011

Lecture Notes in Computer Science

141

111

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Abstract. To reduce the negative environmental implications (e.g., CO2 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 realworld 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.

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Section: Solar Power Modelsupporting

confidence: 58%

Section: Solar Power Modelmentioning

confidence: 99%

Section: Renewable Energy Availabilitymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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GreenWare: Greening Cloud-Scale Data Centers to Maximize the Use of Renewable Energy

Zhang

Wang

2011

Lecture Notes in Computer Science

141

111

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SEED: solar energy‐aware efficient scheduling for data centers

Jing

Zhu

2013

Concurrency and Computation

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SUMMARYIt is well known that data centers are consuming a large amount of energy that incurs significant financial and environmental costs. Recently, there has been an increasing interest in utilizing green energy for data centers, where green energy sources include solar and wind. This paper studies the crucial problem of maximizing the utilization of green energy through scheduling complex jobs in data centers in order to reduce the use of traditional brown energy. However, it is highly challenging for data centers to make use of green energy. First, the availability of typical green energy is variable to dynamic changes of natural environments, for example, weather. Second, although predictions can be made for the future availability of green energy, it is inevitable that such predictions have errors. Third, jobs are associated with strict deadlines, and it is required that jobs are completed before their deadlines. Finally, because the reliability in a data center relies upon temperature, the awareness of temperature should be taken into account while maximizing the green energy. In this paper, we consider online scheduling of jobs whose arrivals to the data center system dynamically. In addition, we explicitly take the power consumption of switches into account when scheduling jobs onto computing nodes. Two solar energy-aware algorithms called SEEDMin and SEEDMax have been proposed. Then, we extend SEED to RSEED with the awareness of reliability. To evaluate the effectiveness of the proposed algorithms, comprehensive simulations have been conducted, and the proposed algorithms are compared with other state-of-art algorithms. Experimental results demonstrate that both SEEDMin and SEEDMax can significantly increase the utilization of solar energy without violating job deadlines and overall energy budget. The amount of solar energy utilized by SEEDMin and SEEDMax is 33.4%and 35.3% larger than that of two traditional scheduling algorithms, MinMin and MinMax, respectively. Also, it can be seen that RSEED greatly improves the reliability by decreasing the temperature.

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