Cloud Computing presents a novel way for businesses to procure their IT needs. Its elasticity and on-demand provisioning enables a shift from capital expenditures to operating expenses, giving businesses the technological agility they need to respond to an ever-changing marketplace. The rapid adoption of Cloud Computing, however, poses a unique challenge to Cloud providers—their already very large electricity bill and carbon footprint will get larger as they expand; managing both costs is therefore essential to their growth. This thesis squarely addresses the above challenge. Recognizing the presence of Cloud data centers in multiple locations and the differences in electricity price and emission intensity among these locations and over time, we develop an optimization framework that couples workload distribution with time-varying signals on electricity price and emission intensity for financial and environmental benefits. The framework is comprised of an optimization model, an aggregate cost function, and 6 scheduling heuristics. To evaluate cost savings, we run simulations with 5 data centers located across North America over a period of 81 days. We use historical data on electricity price, emission intensity, and workload collected from market operators and research data archives. We find that our framework can produce substantial cost savings, especially when workloads are distributed both geographically and temporally—up to 53.35% on electricity cost, or 29.13% on carbon cost, or 51.44% on electricity cost and 13.14% on carbon cost simultaneously.

Cloud Computing presents a novel way for businesses to procure their IT needs. Its elasticity and on-demand provisioning enables a shift from capital expenditures to operating expenses, giving businesses the technological agility they need to respond to an ever-changing marketplace. The rapid adoption of Cloud Computing, however, poses a unique challenge to Cloud providers—their already very large electricity bill and carbon footprint will get larger as they expand; managing both costs is therefore essential to their growth. This thesis squarely addresses the above challenge. Recognizing the presence of Cloud data centers in multiple locations and the differences in electricity price and emission intensity among these locations and over time, we develop an optimization framework that couples workload distribution with time-varying signals on electricity price and emission intensity for financial and environmental benefits. The framework is comprised of an optimization model, an aggregate cost function, and 6 scheduling heuristics. To evaluate cost savings, we run simulations with 5 data centers located across North America over a period of 81 days. We use historical data on electricity price, emission intensity, and workload collected from market operators and research data archives. We find that our framework can produce substantial cost savings, especially when workloads are distributed both geographically and temporally—up to 53.35% on electricity cost, or 29.13% on carbon cost, or 51.44% on electricity cost and 13.14% on carbon cost simultaneously.

Cloud Computing presents a novel way for businesses to procure their IT needs. Its elasticity and on-demand provisioning enables a shift from capital expenditures to operating expenses, giving businesses the technological agility they need to respond to an ever-changing marketplace. The rapid adoption of Cloud Computing, however, poses a unique challenge to Cloud providers—their already very large electricity bill and carbon footprint will get larger as they expand; managing both costs is therefore essential to their growth. This thesis squarely addresses the above challenge. Recognizing the presence of Cloud data centers in multiple locations and the differences in electricity price and emission intensity among these locations and over time, we develop an optimization framework that couples workload distribution with time-varying signals on electricity price and emission intensity for financial and environmental benefits. The framework is comprised of an optimization model, an aggregate cost function, and 6 scheduling heuristics. To evaluate cost savings, we run simulations with 5 data centers located across North America over a period of 81 days. We use historical data on electricity price, emission intensity, and workload collected from market operators and research data archives. We find that our framework can produce substantial cost savings, especially when workloads are distributed both geographically and temporally—up to 53.35% on electricity cost, or 29.13% on carbon cost, or 51.44% on electricity cost and 13.14% on carbon cost simultaneously.

abstract: The combination of rapid urban growth and climate change places stringent constraints on multisector sustainability of cities. Green infrastructure provides a great potential for mitigating anthropogenic-induced urban environmental problems; nevertheless, studies at city and regional scales are inhibited by the deficiency in modelling the complex transport coupled water and energy inside urban canopies. This dissertation is devoted to incorporating hydrological processes and urban green infrastructure into an integrated atmosphere-urban modelling system, with the goal to improve the reliability and predictability of existing numerical tools. Based on the enhanced numerical tool, the effects of urban green infrastructure on environmental sustainability of cities are examined. Findings indicate that the deployment of green roofs will cool the urban environment in daytime and warm it at night, via evapotranspiration and soil insulation. At the annual scale, green roofs are effective in decreasing building energy demands for both summer cooling and winter heating. For cities in arid and semiarid environments, an optimal trade-off between water and energy resources can be achieved via innovative design of smart urban irrigation schemes, enabled by meticulous analysis of the water-energy nexus. Using water-saving plants alleviates water shortage induced by population growth, but comes at the price of an exacerbated urban thermal environment. Realizing the potential water buffering capacity of urban green infrastructure is crucial for the long-term water sustainability and subsequently multisector sustainability of cities. Environmental performance of urban green infrastructure is determined by land-atmosphere interactions, geographic and meteorological conditions, and hence it is recommended that analysis should be conducted on a city-by-city basis before actual implementation of green infrastructure. / Dissertation/Thesis / Doctoral Dissertation Civil and Environmental Engineering 2016