Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


LiLi_Dissertation_Final.pdf (3.81 MB)

ETD Abstract Container

Abstract Header

Improving the Energy Efficiency for Mobile and Cloud Computing

Li , Li
http://rave.ohiolink.edu/etdc/view?acc_num=osu1522883821175401

Abstract Details

2018, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
Mobile and cloud computing has significantly impacted people’s life. In recent years, smartphones have greatly improved in different aspects including processing speed and memory capacity. This improvement has allowed developers to create apps with different functionalities. In the meantime, cloud computing has improved as an efficient computing paradigm. Different services created with mobile and cloud computing have become an essential part of human life. However, energy issue has become a critical bottleneck for the mobile and cloud system. For instance, the services running in the cloud consume a large amount of energy which accounts for about 7% of the total commercial electric cost. At the same time, mobile apps can drain the battery fast which negatively impacts the user experience. Thus, improving the energy efficiency is an important task for both mobile and cloud computing. In this dissertation, we improve the energy efficiency of mobile and cloud computing in different aspects. For cloud computing, we first introduce a new cooling technique (e.g., liquid cooling) into the traditional air-cooled data centers to improve the cooling efficiency. We propose an intelligent placement algorithm, SmartPlace, which deploys liquid-cooled servers to minimize power consumption of the data center cooling system. We compare SmartPlace with a state-of-the-art cooling optimization solution. The results show that SmartPlace achieves up to 26.7% less total power consumption with dynamically guaranteed application response time. Moreover, we propose SmartCool, a power optimization scheme that effectively coordinates different cooling techniques and dynamically manages workload allocation for jointly optimizing cooling and server power of data centers. The evaluation results show that SmartCool outperforms two state-of-the-art baselines by having a 38% more power savings. For mobile computing, we improve the energy efficiency of smartphones from different levels. In the framework level, we propose an energy optimization framework, SceneMan, which makes the energy manager aware of app-level usage information and performs energy optimization according to corresponding usage scenarios. We achieve up to 33.2% energy savings with a worst-case performance loss of 5.1%. In the app level, we first try to solve the abnormal battery drain (ABD) problem in mobile apps. We propose eDelta, a framework that assists developers in pinpointing the APIs with high energy deviation, which usually have a high probability of being relevant to the non-deterministic ABD. Our results show that eDelta reduces, on average, 94.6% of the amount of code that the developers would need to search for ABD root causes. Besides the ABD problem, we find that, a phone user commonly has to restart, from the very beginning, the apps he or she is using when the phone gets rebooted or unexpected shutdown, because all the app states would be lost which costs a lot of recovery energy and effort. We propose an intelligent checkpointing methodology, SmartCP, in order to significantly reduce the amount of energy and effort to recover the app states after that smartphone restarts. The results show that SmartCP outperforms two alternative app selection schemes by saving 28% more energy and 39% more recovery efforts on average.
Xiaorui Wang (Advisor)
Feng Qin (Committee Member)
Fusun Ozguner (Committee Member)
159 p.
Computer Engineering

Recommended Citations

Citations

  • Li , L. (2018). Improving the Energy Efficiency for Mobile and Cloud Computing [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1522883821175401

    APA Style (7th edition)

  • Li , Li . Improving the Energy Efficiency for Mobile and Cloud Computing . 2018. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1522883821175401.

    MLA Style (8th edition)

  • Li , Li . "Improving the Energy Efficiency for Mobile and Cloud Computing ." Doctoral dissertation, Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1522883821175401

    Chicago Manual of Style (17th edition)

osu1522883821175401
417
© 2018, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.