Thermal Profiling and Modeling of Hadoop Clusters Using BigData Applications

In this paper, we propose a thermal model called tModel, which projects outlet temperatures from inlet temperatures as well as directly measured multicore temperatures rather than deploying a utilization model. We perform extensive experimentation by varying applications types, their input data sizes, and cluster sizes. We collect inlet, outlet, and multicore temperatures of cluster nodes running a group of big-data applications. The proposed thermal model estimates the outlet air temperature of the nodes to predict cooling costs. We validate the accuracy of our model against data gathered by thermal sensors in our cluster. Our results demonstrate that tModel estimates outlet temperatures of the cluster nodes with much higher accuracy over CPU-utilization based models. We further show that tModel is conducive of estimating the cooling cost of data centers using the predicted outlet temperatures.

[1]  Massoud Pedram,et al.  Minimizing data center cooling and server power costs , 2009, ISLPED.

[2]  D. Zarefsky.  The U.S. and the world , 2014 .

[3]  Wei Huang,et al.  Cooling-Aware Job Scheduling and Node Allocation for Overprovisioned HPC Systems , 2017, 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

[4]  Martin Schulz,et al.  Beyond DVFS: A First Look at Performance under a Hardware-Enforced Power Bound , 2012, 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum.

[5]  Ricardo Bianchini,et al.  C-Oracle: Predictive thermal management for data centers , 2008, 2008 IEEE 14th International Symposium on High Performance Computer Architecture.

[6]  Sandeep K. S. Gupta,et al.  Energy-Efficient Thermal-Aware Task Scheduling for Homogeneous High-Performance Computing Data Centers: A Cyber-Physical Approach , 2008, IEEE Transactions on Parallel and Distributed Systems.

[7]  Shen Li,et al.  Joint Optimization of Computing and Cooling Energy: Analytic Model and a Machine Room Case Study , 2012, 2012 IEEE 32nd International Conference on Distributed Computing Systems.

[8]  Ayan Banerjee,et al.  Energy Efficiency of Thermal-Aware Job Scheduling Algorithms under Various Cooling Models , 2009, IC3.

[9]  Jie Huang,et al.  The HiBench benchmark suite: Characterization of the MapReduce-based data analysis , 2010, 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010).

[10]  Athanasios V. Vasilakos,et al.  Thermal-Aware Scheduling of Batch Jobs in Geographically Distributed Data Centers , 2014, IEEE Transactions on Cloud Computing.

[11]  Wolfgang Schott,et al.  Thermal-aware workload scheduling for energy efficient data centers , 2010, ICAC '10.

[12]  Qinghui Tang,et al.  Sensor-Based Fast Thermal Evaluation Model For Energy Efficient High-Performance Datacenters , 2006, 2006 Fourth International Conference on Intelligent Sensing and Information Processing.

[13]  Klara Nahrstedt,et al.  T*: A data-centric cooling energy costs reduction approach for Big Data analytics cloud , 2012, 2012 International Conference for High Performance Computing, Networking, Storage and Analysis.

[14]  Jeffrey S. Chase,et al.  Making Scheduling "Cool": Temperature-Aware Workload Placement in Data Centers , 2005, USENIX Annual Technical Conference, General Track.

[15]  Jean-Luc Gaudiot,et al.  PETS: Performance, energy and thermal aware scheduler for job mapping with resource allocation in heterogeneous systems , 2016, 2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC).

[16]  Suman Nath,et al.  ThermoCast: a cyber-physical forecasting model for datacenters , 2011, KDD.

[17]  Bruno Sinopoli,et al.  A Cyber–Physical Systems Approach to Data Center Modeling and Control for Energy Efficiency , 2012, Proceedings of the IEEE.

[18]  Laxmikant V. Kalé,et al.  A ‘cool’ load balancer for parallel applications , 2011, 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC).