Energy Modeling and Management for Data Services in Multi-Tier Mobile Cloud Architectures Dissertation Presented in Partial Fulfillment of the Requirements for the Doctor of Philosophy Degree in the Graduate School of The Ohio State University By Zichen Xu, C.S., M.S. Graduate Program in Department of Electrical and Computer Engineering The Ohio State University 2016 Dissertation Committee: Prof. Xiaorui Wang, Advisor Prof. Füsun Özgüner Prof. Christopher C. Stewart c Copyright by Zichen Xu 2016 Abstract Researchers’ prediction about the emergence of very small and very large comput- ing devices is becoming true. Computer users create personal content from their mobile devices and these contents are processed/stored in the remote server. This mobile cloud computing architecture contains millions of smartphone devices as the edge and high-end servers as the cloud, in order to provide data services worldwide. Unlike data services in traditional architectures, data services in the mobile computing architecture is greatly con- strained by by energy consumption. Data services running in the cloud consume a large amount of electricity that accounts for 4% of the global energy use. Data processing and transmission in mobiles devices, such as smartphones, quickly drain out their batteries. Therefore, energy is one of the most important criterion in the design of these systems. To address this problem, we need to build an energy modeling and management framework to profile, estimate and manage the energy consumption for data processing in the mobile cloud architecture. We first start with energy profiling of data processing in a single node. The study discovers that there exist possibilities of finding energy-efficient execution plans other than fast plans only. Based on the profile, we propose our online estimation tools for ii modeling and estimating energy consumption of relational data operations. Using this tool, we provide power performance control for data processing. The control framework provide service level agreement guarantee while reducing the power consumption of the server at its best effort. The control-theoretic design provide system stability when facing unpredictable workloads. Using the same modeling processing, we expand our research to optimize energy- related objectives, such as carbon footprint and cloud expense, in multiple nodes. We carefully study the processing of data in multiple nodes, and find that the processing (i.e., read/write) significantly affects the objectives when replicating data objects across mul- tiple nodes. In this way, we transform the optimization problem on time-varying load balancing into a semi-static decision problem of data replication. By solving this problem, we build two data storage systems–CADRE and BOSS, to reduce the carbon footprint of serving data, and the cloud expense of processing in-memory data, respectively. The modeling and managing process can also be applied to edge devices, such as smartphones. We first build an energy estimation tool for specific applications on smart- phones using performance counters. Unlike traditional energy modeling work that uses system utilization, using performance counters can provide energy estimation for finer- grained executions and isolate the target energy profile. With understanding of the energy profile of data applications on smartphones, we further model the battery usage of the device. Based on the energy model and the battery model, we propose a dual-battery management system on battery-powered devices. Certain battery is favored by specific iii workloads with their energy demand patterns. Altering the power supply between the two batteries can significantly improve the service time of the device, compared to the de- vice powered with the same amount of battery capacity. Combining all energy modeling and management system designs above, we are able to significantly improve the energy efficiency of data services in each tier of the mobile cloud architecture. iv To my parents, my wife, and my mentors v Acknowledgments I enjoy my long journey to pursue my Ph.D. degree in the United States. I could never make this far without everyone’s help. It is never an easy decision for my parents to encourage and support their only child to travel across continents and study for so many years abroad. I feel guilty that not serving by their side for such a long time and I deeply appreciate their devoted love. I am also especially thankful for my wife Jiangyue (Jane) Li. Marrying her is the most wonderful thing happened in my life. She is always by my side and adores my work. I am givingthis thesisto her as my gratefulness for the relentless support. I am thankful and grateful to have worked with my mentor Dr. Xiaorui Wang, who taught me what research attitude is and the philosophy of doing research, provided in- sightful guidance, and offered many advice. His advice allowed me to rethink the essence of research, find the real key problem, and dig deeper to explore the effective solution to address the real life challenge. His encouragement carried me through the most desperate period in my research. He is THE pattern that I want to follow in my rest academic career life. Along this way, I want to thank Dr. Füsun Özgüner for serving on my committee. I also want to show my gratitude to my dear friend/life advisor, Dr. Christopher Stewart and Dr. Yicheng Tu. I learnt so many from their advice on life. Talking to them, even vi within a short coffee break, can unchain me from the narrow aspect of the problem in research and life, then move on to solve the key issue. It is my pleasure and my life treasure to have so many friends supported me along this career path. I want to thank their help and support: Siwen Sun, Deng Nan, Kai Ma, Xiaodong Wang, Zhezhe Chen, Mai Zheng, Li Li, Marco Brocanelli, Kuangyu Zheng, Wenli Zheng, and Ziqi Huang. vii Vita 2007 ......................................B.S.Computer Technology 2009 ......................................M.S.Computer Science 2011-present ...............................Graduate Research Associate, The Ohio State University. Publications Research Publications Zichen Xu, Yi-cheng Tu and Xiaorui Wang, “Online Energy Estimation of Relational Operations in Database Systems”. IEEE Transactions on Computers, 4(11): 3223-3236, November 2015. Yi-cheng Tu, Xiaorui Wang, Bo Zeng, and Zichen Xu, “A System for Energy-Efficient Data Management”. ACM SIGMOD Record, 43(1): 21-26, March 2014. Zichen Xu, Nan Deng, Christopher Stewart, and Xiaorui Wang, “Blending On-Demand and Spot Instances to Lower Costs for In-Memory Storage”. in proceedings of the 35th IEEE International Conference on Computer Communications, April 2016. Nan Deng, Zichen Xu, Christopher Stewart, and Xiaorui Wang, “Tell-Tale Tails: Decom- posing Response Times for Live Internet Services”. in proceedings of the 6th International Green and Sustainable Computing Conference, December 2015. viii Zichen Xu, Nan Deng, Christopher Stewart, and Xiaorui Wang, “CADRE: Carbon-Aware Data Replication for Geo-Diverse Services”. in proceedings of the 35th IEEE Interna- tional Conference on Computer Communications, July 2015. Zhang Xu, Haining Wang, Zichen Xu, and Xiaorui Wang, “Power Attack: An Increasing Threat to Data Centers”. in proceedings of the 21st Network and Distributed System Security Symposium, February 2014. Zichen Xu, Yi-cheng Tu, and Xiaorui Wang, “Dynamic Energy Estimation of Query Plans in Database Systems”. in proceedings of the 33rd International Conference on Distributed Computing Systems, July 2013. Zichen Xu, Xiaorui Wang, and Yi-cheng Tu, “Power-Aware Throughput Control for Database Management”. in proceedings of the 10th International Conference on Auto- nomic Computing, June 2013. Zichen Xu, Xiaorui Wang, and Yi-cheng Tu, “PET: Reducing Database Energy Cost via Query Optimization”. in proceedings of the 38th International Conference on Very Large Data Bases, September 2012. Fields of Study Major Field: Department of Electrical and Computer Engineering ix Table of Contents Page Abstract........................................ ii Dedication....................................... v Acknowledgments................................... vi Vita .......................................... viii ListofTables ..................................... xiii ListofFigures..................................... xiv Chapters: 1. Introduction................................... 1 1.1 ThesisStatement ............................. 2 1.2 Contributions............................... 3 1.3 Organization ............................... 8 2. Energy Modeling and Management for Data Services on a SingleNode ... 9 2.1 DynamicEnergyEstimationforDataProcessing . .... 10 2.1.1 Energy Profiling for Relational Operations in Modern Servers . 13 2.1.2 Observationson DBMS Workload Characteristics . .. 16 2.1.3 EnergyModelingforRelationalOperations . .. 19 x 2.1.4 OnlineEstimationScheme. 23 2.1.5 Evaluation ............................ 25 2.2 Power-Aware Throughput Control for Database Operations....... 32 2.2.1 Power Performance Optimization for Database Operations... 35 2.2.2 Power/Performance Controller Design for Database Operations 42 2.2.3 ExperimentalResults . 48 2.3 Discussion ................................ 55 3. Two Applications for Optimizing Data Services on MultipleNodes. 57 3.1 CADRE: Carbon-Aware Data Replication for Geo-Diverse Services . 58 3.1.1 Redistributing Data Replications for Reducing Carbon Footprints 61 3.1.2 CADRE Design: Data Replication for Carbon Reduction in Geo-DiverseDataServices