Energy aware resource management for MapReduce jobs with service level agreements in cloud data centers
Clouds which continue to garner interest from practitioners in industry and academia require effective energy aware resource managers to leverage processing power of underlying resources while minimizing energy consumption in global data centers. We devise a novel Energy Aware MapReduce Resource Manager for an open system, called EAMR-RM, that can effectively perform matchmaking and scheduling of MapReduce jobs each of which is characterized by a Service Level Agreement (SLA) for performance that includes a client specified execution time and a deadline such that energy consumption is minimized. Results of the discrete event simulation-based performance analysis demonstrate that the proposed technique can effectively satisfy SLA requirements while achieving up to a 45% reduction in energy consumption compared to approaches which do not consider energy in resource management decisions.
|Keywords||Constraint programming, Energy management, Job turnaound time, MapReduce with deadlines, Resource management on clouds|
|Conference||16th IEEE International Conference on Computer and Information Technology, CIT 2016|
Gregory, A. (Adam), & Majumdar, S. (2017). Energy aware resource management for MapReduce jobs with service level agreements in cloud data centers. In Proceedings - 2016 16th IEEE International Conference on Computer and Information Technology, CIT 2016, 2016 6th International Symposium on Cloud and Service Computing, IEEE SC2 2016 and 2016 International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2016 (pp. 568–577). doi:10.1109/CIT.2016.42