A material social view on data center waste heat: Novel uses and metrics

Today's data centers use substantial amounts of the world's electrical supply. However, in line with circular economy concepts, much of this energy can be reused. Such reuse includes the heating of buildings, but also commodity dehydration, electricity production and energy storage. This multi-disciplinary paper presents several novel applications for data center waste heat. Next, the paper accounts for three case studies, taken from three different societal contexts: urban Malaysia, rural Costa Rica and semi-urban Sweden. A discussion on data center energy metrics leads to the development of a new metric, Datacenter Energy Sustainability Score (DESS), which is evaluated within the three use cases. Last, it is shown how a material social view on metrics provides a way past a problem that has haunted the data center industry for the last 15 years, whilst benefitting both data center owners who want to compete through sustainability as well as stakeholders from governments on local, regional and national levels. The paper makes clear that a sustainability strategy should be based on a material social view and stretch beyond the building itself. In fact, and as demonstrated by the relevance of DESS, modern data centers are so energy-efficient that data center sustainability is no longer mainly an engineering issue, but a matter requiring multi-disciplinary insights, approaches and collaboration

Datascape: speculative city for data to inhabit

La creciente exponencial de datos está afectando profundamente el entorno físico, y su proliferación descontrolada continuará reconfigurando y alterando el paisaje. Sin embargo, estas afectaciones pueden no ser inmediatamente aparentes. Este proyecto tiene como objetivo explorar y representar visualmente el impacto de la producción masiva de datos en el paisaje físico, diseñando una ciudad especulativa para que los datos la habiten para ilustrar las posibles implicaciones de esta posibilidad en el futuro. El objetivo es estudiar el estado actual de la producción de datos, comprender sus efectos en el medio ambiente y crear una representación visual que resalte las posibles consecuencias del crecimiento de los datos. A través de la investigación, el análisis y el diseño, este proyecto pretende contribuir al discurso sobre las implicaciones ambientales de la producción de datos y ofrecer ideas sobre los posibles escenarios futuros que esperan a nuestro entorno construido. Se invita a realizar un examen crítico de nuestras prácticas digitales y se fomenta un enfoque responsable y sostenible en la producción y consumo de datos.The exponential rise in data is profoundly affecting the physical environment, and its uncontrolled proliferation will continue to reshape and alter the landscape. However, these impacts may not be immediately apparent. This project aims to explore and visually represent the impact of massive data production on the physical landscape by designing a speculative city for data to inhabit, illustrating the potential implications of this impact in the future. The objective is to study the current state of data production, understand its effects on the environment, and create a compelling visual representation that highlights the potential consequences of data growth. Through research, analysis, and design, this project aims to contribute to the discourse on the environmental implications of data production and offer insights into potential future scenarios that await our built environment. It calls for a critical examination of our digital practices and encourages responsible and sustainable approaches to data production and consumption

Toward Energy Efficient Systems Design For Data Centers

Surge growth of numerous cloud services, Internet of Things, and edge computing promotes continuous increasing demand for data centers worldwide. Significant electricity consumption of data centers has tremendous implications on both operating and capital expense. The power infrastructure, along with the cooling system cost a multi-million or even billion dollar project to add new data center capacities. Given the high cost of large-scale data centers, it is important to fully utilize the capacity of data centers to reduce the Total Cost of Ownership. The data center is designed with a space budget and power budget. With the adoption of high-density rack designs, the capacity of a modern data center is usually limited by the power budget. So the core of the challenge is scaling up power infrastructure capacity. However, resizing the initial power capacity for an existing data center can be a task as difficult as building a new data center because of a non-scalable centralized power provisioning scheme. Thus, how to maximize the power utilization and optimize the performance per power budget is critical for data centers to deliver enough computation ability. To explore and attack the challenges of improving the power utilization, we have planned to work on different levels of data center, including server level, row level, and data center level. For server level, we take advantage of modern hardware to maximize power efficiency of each server. For rack level, we propose Pelican, a new power scheduling system for large-scale data centers with heterogeneous workloads. For row level, we present Ampere, a new approach to improve throughput per watt by provisioning extra servers. By combining these studies on different levels, we will provide comprehensive energy efficient system designs for data center

On Power Consumption Profiles for Data Intensive Workloads in Virtualized Hadoop Clusters

IEEE International Conference on Computer Communications (INFOCOM 2017). 1 to 4, May, 2017, Workshop Big Data and Cloud Performance. Atlanta, U.S.A..Although reduction in operating costs remains to be a key motivation for migration to Cloud environments, Power consumption is a big concern for data centers and cloud service providers. Many big data applications execute on Hadoop MapReduce framework for processing large workloads. In this paper, we investigate the tradeoff between energy consumption and workload running on Hadoop clusters using multiple virtual machines. We characterize power consumption profiles for various data intensive workloads and correlate these to quality of service (QoS) metrics such as job execution time. Based on experiments, we ascertain that power consumption profiles for big data applications can be used to optimize energy efficiency in data centers. We infer that these profiles can be used by Cloud service providers and consumers to specify green metrics in Service Level Agreements (SLA).info:eu-repo/semantics/publishedVersio

Using data centre waste heat to dry coffee whilst supplying small-scale farmers with ICT:a novel idea and a case study based on a systems approach

In our research, we address energy consumption of data centres, more than one per cent of the world’s future electrical power. Based on a systems approach, we currently explore commodity drying using data centre waste heat, an idea here presented for the first time to the research community. Many low- and mid-income countries are producing coffee, which sometimes needs mechanical drying. Using waste heat to dry coffee would be financially appealing. Conversely, if an existing drying facility may be powered by waste heat, this may call for small-scale data centre construction, in turn increasing ICT availability locally or regionally. Thus, there is a bond between environmental gains and sustainable growth of a community. We therefore investigate both environmental and societal benefits of this idea. Through a site selection based on a new index, we have chosen Costa Rica for our case study, and arrived to an estimate for its data centre waste heat drying capability. We also discuss our findings in relation to the UN Sustainable Development Goals (SDGs)

Green IT Model for Gulf Cooperation Council Organisations

This research aims to develop a Green IT model that suits the needs of the Gulf Cooperation Council (GCC) countries. A mix-methods approach that combines interviews with a survey was implemented to assess the model critically. The initial model developed for evaluating various Green models to assess the Governance, Social and Cultural, Information Technology and Green Management in GCC. The Green IT model aims to raise sustainability awareness in GCC countries based on their visions