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Systems Support for Carbon-Aware Cloud Applications

Deng, Nan
http://orcid.org/0000-0002-5243-4199
http://rave.ohiolink.edu/etdc/view?acc_num=osu1439855103

Abstract Details

2015, Doctor of Philosophy, Ohio State University, Computer Science and Engineering.
Datacenters, which are large server farms, host cloud applications, providing services ranging from search engines to social networks and video streaming services. Such applications may belong to the same owner of the datacenter or from third party developers. Due to the growth of cloud applications, datacenters account for a larger fraction of worldwide carbon emissions each year. To reduce the carbon emissions, many datacenter owners are slowly but gradually adopting clean, renewable energy, like solar or wind energy. To encourage datacenter owners to invest into renewable energy, the usage of renewable energy should lead to profit. However, in most cases, renewable energy supply is intermittent and may be limited. Such fact makes renewable energy more expensive than traditional dirty energy. On the other hand, not all customers have the need of using renewable energy for their applications. Our approach is to devise accountable and effective mechanisms to deliver renewable energy only to users that will pay for renewable-powered services. According to our research, datacenter owners could make profit if they could concentrate the renewable energy supply to carbon-aware applications, who prefer cloud resources powered by renewable energy. We develope two carbon-aware applications as use cases. We conclude that if an application take carbon emissions as a constraint, it will end up with using more resources from renewable powered datacenters. Such observation helps datacenter owners to wisely distribute renewable energy within their systems. Our first attempt of concentrating renewable energy focuses on architectural level. Our approach requires datacenters have on-site renewable energy generator using grid ties to integrate renewable energy into their power supply system. To measure the concentration of renewable energy, we introduce a new metric, the renewable-powered instance. Using this metric, we found that grid-tie placement has first-order effects on renewable-energy concentration. On-site renewable energy requires an initial investment to install renewable generator. Although this cost could be gradually amortized over time, some people prefer renewable energy credit, which could be bought from utility companies by paying premium for the renewable energy transmitted through the grid and produced in other locations. To let datacenters, with or without on-site renewable energy generator, attract more carbon-aware customers, we designed a system for Adaptive Green Hosting. It identifies carbon-aware customers by signaling customers’ applications when renewable energy is available and observing their behaviors. Since carbon-aware applications would tend to use more resources in a datacenter with low emission rates, datacenter owners could make profit by attributing more renewable energy to carbon-aware applications, so that could encourage them to use more resources. Our experiments show that adaptive green hosting can increase profit by 152% for one of todays larger green hosts. Although it is possible for cloud applications to maintain a low carbon footprint while make profit, most existing applications are not carbon-aware. The carbon footprint for most existing workloads is large. Without forcing them to switch to renewable energy, we believe responsible end users could take a step forward first. We propose a method to help end users to discover implementation-level details about a cloud application by extracting its internal software delays. Such details are unlikely to be exposed to third-party users. Instead, our approach probes target application from outside, and extract normalized software delay distributions using only response times. Such software delay distributions are not only useful to reveal normalized energy footprint of an application, but could also be used to diagnose root causes of tail response times for live applications.
Christopher Stewart, Dr. (Advisor)
Xiaorui Wang, Dr. (Committee Member)
Gagan Agrawal, Dr. (Committee Member)
124 p.
Computer Engineering; Computer Science
Datacenter; Renewable Energy; Performance Analysis; Black-box Analysis; Cloud Computing

Recommended Citations

Citations

  • Deng, N. (2015). Systems Support for Carbon-Aware Cloud Applications [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1439855103

    APA Style (7th edition)

  • Deng, Nan. Systems Support for Carbon-Aware Cloud Applications. 2015. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1439855103.

    MLA Style (8th edition)

  • Deng, Nan. "Systems Support for Carbon-Aware Cloud Applications." Doctoral dissertation, Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1439855103

    Chicago Manual of Style (17th edition)

osu1439855103
892
© 2015, some rights reserved.Creative Commons License Systems Support for Carbon-Aware Cloud Applications by Nan Deng is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 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