Characterizing Network Traffic in a Cluster-based, Multi-tier Data Center
ICDCS '07 Proceedings of the 27th International Conference on Distributed Computing Systems
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Pregel: a system for large-scale graph processing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Black-box performance control for high-volume non-interactive systems
USENIX'09 Proceedings of the 2009 conference on USENIX Annual technical conference
Capping the brown energy consumption of Internet services at low cost
GREENCOMP '10 Proceedings of the International Conference on Green Computing
Behavioral model for cloud aware load and power management
Proceedings of the 2013 international workshop on Hot topics in cloud services
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As brown energy costs grow, renewable energy becomes more widely used. Previous work focused on using immediately available green energy to supplement the non-renewable, or brown energy at the cost of canceling and rescheduling jobs whenever the green energy availability is too low [16]. In this paper we design an adaptive data center job scheduler which utilizes short term prediction of solar and wind energy production. This enables us to scale the number of jobs to the expected energy availability, thus reducing the number of cancelled jobs by 4x and improving green energy usage efficiency by 3x over just utilizing the immediately available green energy.