SEED: solar energy‐aware efficient scheduling for data centers

It 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%and35.3% 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. Copyright © 2013 John Wiley & Sons, Ltd.

[1]  Jordi Torres,et al.  Energy-Aware Scheduling in Virtualized Datacenters , 2010, 2010 IEEE International Conference on Cluster Computing.

[2]  Haitao Wu,et al.  BCube: a high performance, server-centric network architecture for modular data centers , 2009, SIGCOMM '09.

[3]  Emmanuel Jeannot,et al.  Optimizing performance and reliability on heterogeneous parallel systems: Approximation algorithms and heuristics , 2012, J. Parallel Distributed Comput..

[4]  Yves Robert,et al.  Energy-aware scheduling under reliability and makespan constraints , 2011, 2012 19th International Conference on High Performance Computing.

[5]  Dzmitry Kliazovich,et al.  DENS: data center energy-efficient network-aware scheduling , 2010, Cluster Computing.

[6]  Lizhe Wang,et al.  Thermal aware workload placement with task-temperature profiles in a data center , 2011, The Journal of Supercomputing.

[7]  Yanpei Chen,et al.  Integrating Renewable Energy Using Data Analytics Systems: Challenges and Opportunities , 2011, IEEE Data Eng. Bull..

[8]  Emmanuel Jeannot,et al.  Bi-objective Approximation Scheme for Makespan and Reliability Optimization on Uniform Parallel Machines , 2008, Euro-Par.

[9]  Xiaodong Wang,et al.  CARPO: Correlation-aware power optimization in data center networks , 2012, 2012 Proceedings IEEE INFOCOM.

[10]  Lizhe Wang,et al.  Software Design and Implementation for MapReduce across Distributed Data Centers , 2013 .

[11]  Vasileios Pappas,et al.  Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement , 2010, 2010 Proceedings IEEE INFOCOM.

[12]  Albert G. Greenberg,et al.  The cost of a cloud: research problems in data center networks , 2008, CCRV.

[13]  Adam Wierman,et al.  Renewable and cooling aware workload management for sustainable data centers , 2012, SIGMETRICS '12.

[14]  Shaolei Ren,et al.  Provably-Efficient Job Scheduling for Energy and Fairness in Geographically Distributed Data Centers , 2012, 2012 IEEE 32nd International Conference on Distributed Computing Systems.

[15]  Margaret Martonosi,et al.  Capping the brown energy consumption of Internet services at low cost , 2010, International Conference on Green Computing.

[16]  Sandeep K. S. Gupta,et al.  Thermal aware server provisioning and workload distribution for internet data centers , 2010, HPDC '10.

[17]  Tajana Rosing,et al.  Utilizing green energy prediction to schedule mixed batch and service jobs in data centers , 2011, OPSR.

[18]  Aaron Hula,et al.  Light-Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel Economy Trends: 1975 Through 2015 , 2015 .

[19]  Lizhe Wang,et al.  Review of performance metrics for green data centers: a taxonomy study , 2011, The Journal of Supercomputing.

[20]  Prashant J. Shenoy,et al.  Blink: managing server clusters on intermittent power , 2011, ASPLOS XVI.

[21]  Kang-Won Lee,et al.  Application-aware virtual machine migration in data centers , 2011, 2011 Proceedings IEEE INFOCOM.

[22]  Bruce M. Maggs,et al.  Cutting the electric bill for internet-scale systems , 2009, SIGCOMM '09.

[23]  Ladislau Bölöni,et al.  A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems , 2001, J. Parallel Distributed Comput..

[24]  Karam S. Chatha,et al.  Approximation algorithm for the temperature-aware scheduling problem , 2007, 2007 IEEE/ACM International Conference on Computer-Aided Design.

[25]  Lachlan L. H. Andrew,et al.  Dynamic Right-Sizing for Power-Proportional Data Centers , 2011, IEEE/ACM Transactions on Networking.

[26]  Jordi Torres,et al.  GreenHadoop: leveraging green energy in data-processing frameworks , 2012, EuroSys '12.

[27]  Lachlan L. H. Andrew,et al.  Greening Geographical Load Balancing , 2015, IEEE/ACM Transactions on Networking.

[28]  Sascha Hunold,et al.  Evolutionary Scheduling of Parallel Tasks Graphs onto Homogeneous Clusters , 2011, 2011 IEEE International Conference on Cluster Computing.

[29]  Sujata Banerjee,et al.  A Power Benchmarking Framework for Network Devices , 2009, Networking.

[30]  Jordi Torres,et al.  GreenSlot: Scheduling energy consumption in green datacenters , 2011, 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

[31]  Andrea Lodi,et al.  Two-dimensional packing problems: A survey , 2002, Eur. J. Oper. Res..

[32]  Xue Liu,et al.  Minimizing Electricity Cost: Optimization of Distributed Internet Data Centers in a Multi-Electricity-Market Environment , 2010, 2010 Proceedings IEEE INFOCOM.

[33]  Austin Donnelly,et al.  Sierra: practical power-proportionality for data center storage , 2011, EuroSys '11.

[34]  Tao Yuan,et al.  Distributed data structure templates for data‐intensive remote sensing applications , 2013, Concurr. Comput. Pract. Exp..

[35]  Prashant J. Shenoy,et al.  Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems , 2010, 2010 7th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON).

[36]  Srinivasan Keshav,et al.  It's not easy being green , 2012, CCRV.

[37]  Chao Li,et al.  SolarCore: Solar energy driven multi-core architecture power management , 2011, 2011 IEEE 17th International Symposium on High Performance Computer Architecture.

[38]  Jordi Torres,et al.  Intelligent Placement of Datacenters for Internet Services , 2011, 2011 31st International Conference on Distributed Computing Systems.

[39]  Thu D. Nguyen,et al.  Parasol and GreenSwitch: managing datacenters powered by renewable energy , 2013, ASPLOS '13.