GRID '11 Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing
A GridWay-based autonomic network-aware metascheduler
Future Generation Computer Systems
Improving Grid Resource Usage: Metrics for Measuring Fragmentation
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
On the Improvement of Grid Resource Utilization: Preventive and Reactive Rescheduling Approaches
Journal of Grid Computing
Improving cloud infrastructure utilization through overbooking
Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference
Cloudy with a Chance of Load Spikes: Admission Control with Fuzzy Risk Assessments
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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Grid computing involves the coordinated use of disperse heterogeneous computing resources. This heterogeneity and dispersion makes Quality of Service (QoS) still an open issue requiring attention from the research community. One way of contributing to the provision of QoS in Grids is by performing meta-scheduling of jobs in advance, that is, the computing resource where a job will be executed is decided some time before jobs are actually executed. But this way of scheduling needs to do predictions about the future status of resources, including network. The main aim of this work is to provide QoS in Grid environments through network-aware job scheduling in advance. In our case, QoS means the fulfillment of a deadline for the completion of jobs. For this, predictions about future status of computing and network resources are made by using exponential smoothing functions. This paper presents a performance evaluation using a real testbed that illustrates the efficiency of this approach to meet the QoS requirements of users. This evaluation highlights the effects of using Exponential Smoothing (ES) to tune predictions in order to deliver the requested QoS.