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  • 1
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2013
    In:  ACM SIGPLAN Notices Vol. 48, No. 4 ( 2013-04-23), p. 51-64
    In: ACM SIGPLAN Notices, Association for Computing Machinery (ACM), Vol. 48, No. 4 ( 2013-04-23), p. 51-64
    Abstract: Several companies have recently announced plans to build "green" datacenters, i.e. datacenters partially or completely powered by renewable energy. These datacenters will either generate their own renewable energy or draw it directly from an existing nearby plant. Besides reducing carbon footprints, renewable energy can potentially reduce energy costs, reduce peak power costs, or both. However, certain renewable fuels are intermittent, which requires approaches for tackling the energy supply variability. One approach is to use batteries and/or the electrical grid as a backup for the renewable energy. It may also be possible to adapt the workload to match the renewable energy supply. For highest benefits, green datacenter operators must intelligently manage their workloads and the sources of energy at their disposal. In this paper, we first discuss the tradeoffs involved in building green datacenters today and in the future. Second, we present Parasol, a prototype green datacenter that we have built as a research platform. Parasol comprises a small container, a set of solar panels, a battery bank, and a grid-tie. Third, we describe GreenSwitch, our model-based approach for dynamically scheduling the workload and selecting the source of energy to use. Our real experiments with Parasol, GreenSwitch, and MapReduce workloads demonstrate that intelligent workload and energy source management can produce significant cost reductions. Our results also isolate the cost implications of peak power management, storing energy on the grid, and the ability to delay the MapReduce jobs. Finally, our results demonstrate that careful workload and energy source management can minimize the negative impact of electrical grid outages.
    Type of Medium: Online Resource
    ISSN: 0362-1340 , 1558-1160
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2013
    detail.hit.zdb_id: 2079194-X
    detail.hit.zdb_id: 282422-X
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  • 2
    Online Resource
    Online Resource
    Institute of Electrical and Electronics Engineers (IEEE) ; 2014
    In:  IEEE Micro Vol. 34, No. 3 ( 2014-5), p. 8-16
    In: IEEE Micro, Institute of Electrical and Electronics Engineers (IEEE), Vol. 34, No. 3 ( 2014-5), p. 8-16
    Type of Medium: Online Resource
    ISSN: 0272-1732 , 1937-4143
    RVK:
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2014
    detail.hit.zdb_id: 2027750-7
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  • 3
    In: Ad Hoc Networks, Elsevier BV, Vol. 25 ( 2015-02), p. 520-534
    Type of Medium: Online Resource
    ISSN: 1570-8705
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2015
    detail.hit.zdb_id: 2121401-3
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  • 4
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2010
    In:  ACM SIGMETRICS Performance Evaluation Review Vol. 38, No. 1 ( 2010-06-12), p. 357-358
    In: ACM SIGMETRICS Performance Evaluation Review, Association for Computing Machinery (ACM), Vol. 38, No. 1 ( 2010-06-12), p. 357-358
    Abstract: The large amount of energy consumed by Internet services represents significant and fast-growing financial and environmental costs. This paper introduces a general, optimization-based framework and several request distribution policies that enable multi-data-center services to manage their brown energy consumption and leverage green energy, while respecting their service-level agreements (SLAs) and minimizing energy cost. Our policies can be used to abide by caps on brown energy consumption that might arise from various scenarios such as government imposed Kyoto-style carbon limits. Extensive simulations and real experiments show that our policies allow a service to trade off consumption and cost. For example, using our policies, a service can reduce brown energy consumption by 24% for only a 10% increase in cost, while still abiding by SLAs.
    Type of Medium: Online Resource
    ISSN: 0163-5999
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2010
    detail.hit.zdb_id: 199353-7
    detail.hit.zdb_id: 2089001-1
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  • 5
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2013
    In:  ACM SIGARCH Computer Architecture News Vol. 41, No. 1 ( 2013-03-29), p. 51-64
    In: ACM SIGARCH Computer Architecture News, Association for Computing Machinery (ACM), Vol. 41, No. 1 ( 2013-03-29), p. 51-64
    Abstract: Several companies have recently announced plans to build "green" datacenters, i.e. datacenters partially or completely powered by renewable energy. These datacenters will either generate their own renewable energy or draw it directly from an existing nearby plant. Besides reducing carbon footprints, renewable energy can potentially reduce energy costs, reduce peak power costs, or both. However, certain renewable fuels are intermittent, which requires approaches for tackling the energy supply variability. One approach is to use batteries and/or the electrical grid as a backup for the renewable energy. It may also be possible to adapt the workload to match the renewable energy supply. For highest benefits, green datacenter operators must intelligently manage their workloads and the sources of energy at their disposal. In this paper, we first discuss the tradeoffs involved in building green datacenters today and in the future. Second, we present Parasol, a prototype green datacenter that we have built as a research platform. Parasol comprises a small container, a set of solar panels, a battery bank, and a grid-tie. Third, we describe GreenSwitch, our model-based approach for dynamically scheduling the workload and selecting the source of energy to use. Our real experiments with Parasol, GreenSwitch, and MapReduce workloads demonstrate that intelligent workload and energy source management can produce significant cost reductions. Our results also isolate the cost implications of peak power management, storing energy on the grid, and the ability to delay the MapReduce jobs. Finally, our results demonstrate that careful workload and energy source management can minimize the negative impact of electrical grid outages.
    Type of Medium: Online Resource
    ISSN: 0163-5964
    RVK:
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2013
    detail.hit.zdb_id: 2088489-8
    detail.hit.zdb_id: 186012-4
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