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Energy Modeling and Management for Data Services in Multi-Tier Mobile Cloud Architectures

Xu, Zichen
http://orcid.org/0000-0002-9489-0862
http://rave.ohiolink.edu/etdc/view?acc_num=osu1468272637

Abstract Details

2016, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
Researchers' prediction about the emergence of very small and very large computing 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 constrained 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 modeling and estimating energy consumption of relational data operations. Further, we provide power performance control for data processing. The control framework provide service level agreement guarantee while reducing the power consumption. The control-theoretic design provide system stability when facing unpredictable workloads. Using the 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 multiple nodes. 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 start with building an energy estimation tool for specific applications on smartphones using performance counters. Unlike traditional modeling work, using performance counters can provide energy estimation for fine-grained executions and isolate the target energy profile. Based on the energy/battery model, we propose a dual-battery management system on battery-powered devices. Altering the power supply between the two batteries can significantly improve the service time of the device. 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.
Xiaorui Wang (Advisor)
Fusun Ozguner (Committee Member)
Christopher Stewart (Committee Member)
198 p.
Computer Engineering; Computer Science
Energy modeling, energy management, distributed data services, cost-effective optimization, computing system design

Recommended Citations

Citations

  • Xu, Z. (2016). Energy Modeling and Management for Data Services in Multi-Tier Mobile Cloud Architectures [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1468272637

    APA Style (7th edition)

  • Xu, Zichen. Energy Modeling and Management for Data Services in Multi-Tier Mobile Cloud Architectures. 2016. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1468272637.

    MLA Style (8th edition)

  • Xu, Zichen. "Energy Modeling and Management for Data Services in Multi-Tier Mobile Cloud Architectures." Doctoral dissertation, Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1468272637

    Chicago Manual of Style (17th edition)

osu1468272637
928
© 2016, some rights reserved.Creative Commons License Energy Modeling and Management for Data Services in Multi-Tier Mobile Cloud Architectures by Zichen Xu is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.
Release 3.2.12