The large amount of energy consumed by Internet services represents significant and fast-growing financial and environmental costs. Increasingly, services are exploring dynamic methods to minimize energy costs while respecting their service-level agreements (SLAs). Furthermore, it will soon be important for these services to manage their usage of "brown energy" (produced via carbon-intensive means) relative to renewable or "green" energy. This paper introduces a general, optimization-based framework for enabling multi-data-center services to manage their brown energy consumption and leverage green energy, while respecting their SLAs and minimizing energy costs. Based on the framework, we propose a policy for request distribution across the data centers. Our policy can be used to abide by caps on brown energy consumption, such as those that might arise from Kyoto-style carbon limits, from corporate pledges on carbon-neutrality, or from limits imposed on services to encourage brown energy conservation. We evaluate our framework and policy extensively through simulations and real experiments. Our results show how our policy allows a service to trade off consumption and cost. For example, using our policy, the service can reduce brown energy consumption by 24% for only a 10% increase in cost, while still abiding by SLAs.

Cloud service providers operate multiple geographically distributed data centers. These data centers consume huge amounts of energy, which translate into high operating costs. Interestingly, the geographical distribution of the data centers provides many opportunities for cost savings. For example, the electricity prices and outside temperatures may differ widely across the data centers. This diversity suggests that intelligently placing load may lead to large cost savings. However, aggressively directing load to the cheapest data center may render its cooling infrastructure unable to adjust in time to prevent server overheating. In this paper, we study the impact of load placement policies on cooling and maximum data center temperatures. Based on this study, we propose dynamic load distribution policies that consider all electricity-related costs as well as transient cooling effects. Our evaluation studies the ability of different cooling strategies to handle load spikes, compares the behaviors of our dynamic cost-aware policies to cost-unaware and static policies, and explores the effects of many parameter settings. Among other interesting results, we demonstrate that (1) our policies can provide large cost savings, (2) load migration enables savings in many scenarios, and (3) all electricity-related costs must be considered at the same time for higher and consistent cost savings.

The purpose of this study was to evaluate how the impacts from the 2022 World Cup preparations in Qatar influenced local residents’ attitudes, personal and community quality of life perceptions, excitement about hosting the event, and support toward the event. The examination of the way mega sport event impacts influence residents’ perceptions of personal and community quality of life is lacking in the literature. Data were collected using systematic sampling in October 2014 from Qatari nationals and white-collar expatriates. Overall, 2,163 interviews with Qatari nationals (1,058) and white-collar expatriates (1,105) were completed. The results revealed that eight years before the event, sociocultural impacts were the most influential type of impact for residents’ attitudes toward the event, community and personal quality of life, excitement about the event, and support of the FIFA decision to host the event in Qatar.

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Abstract-Popular Internet services are hosted by multiple geographically distributed datacenters. The location of the datacenters has a direct impact on the services' response times, capital and operational costs, and (indirect) carbon dioxide emissions. Selecting a location involves many important considerations, including its proximity to population centers, power plants, and network backbones; the source of the electricity in the region; the electricity, land, and water prices at the location; and the average temperatures at the location. As there can be many potential locations and many issues to consider for each of them, the selection process can be extremely involved and time-consuming. In this paper, we focus on the selection process and its automation. Specifically, we propose a framework that formalizes the process as a non-linear cost optimization problem, and approaches for solving the problem. Based on the framework, we characterize areas across the United States as potential locations for datacenters, and delve deeper into seven interesting locations. Using the framework and our solution approaches, we illustrate the selection tradeoffs by quantifying the minimum cost of (1) achieving different response times, availability levels, and consistency times, and (2) restricting services to green energy and chiller-less datacenters. Among other interesting results, we demonstrate that the intelligent placement of datacenters can save millions of dollars under a variety of conditions. We also demonstrate that the selection process is most efficient and accurate when it uses a novel combination of linear programming and simulated annealing.