Precise and realistic utility functions for user-centric performance analysis of schedulers
Proceedings of the 16th international symposium on High performance distributed computing
On the User-Scheduler Dialogue: Studies of User-Provided Runtime Estimates and Utility Functions
International Journal of High Performance Computing Applications
Cutting the electric bill for internet-scale systems
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling
Proceedings of the 5th European conference on Computer systems
Greening geographical load balancing
Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
The Case for Evaluating MapReduce Performance Using Workload Suites
MASCOTS '11 Proceedings of the 2011 IEEE 19th Annual International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems
SLA-based resource provisioning for heterogeneous workloads in a virtualized cloud datacenter
ICA3PP'11 Proceedings of the 11th international conference on Algorithms and architectures for parallel processing - Volume Part I
Resource provisioning framework for mapreduce jobs with performance goals
Middleware'11 Proceedings of the 12th ACM/IFIP/USENIX international conference on Middleware
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
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Distributed computing resources in a cloud computing environment provides an opportunity to reduce energy and its cost by shifting loads in response to dynamically varying availability of energy. This variation in electrical power availability is represented in its dynamically changing price that can be used to drive workload deferral against performance requirements. But such deferral may cause user dissatisfaction. In this paper, we quantify the impact of deferral on user satisfaction and utilize flexibility from the service level agreements (SLAs) for deferral to adapt with dynamic price variation. We differentiate among the jobs based on their requirements for responsiveness and schedule them for energy saving while meeting deadlines and user satisfaction. Representing utility as decaying functions along with workload deferral, we make a balance between loss of user satisfaction and energy efficiency. We model delay as decaying functions and guarantee that no job violates the maximum deadline, and we minimize the overall energy cost. Our simulation on MapReduce traces show that energy consumption can be reduced by ~15%, with such utility-aware deferred load balancing. We also found that considering utility as a decaying function gives better cost reduction than load balancing with a fixed deadline.