Client classification policies for SLA negotiation and allocation in shared cloud datacenters
GECON'11 Proceedings of the 8th international conference on Economics of Grids, Clouds, Systems, and Services
Client Classification Policies for SLA Enforcement in Shared Cloud Datacenters
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
On the Anticipation of Resource Demands to Fulfill the QoS of SaaS Web Applications
GRID '12 Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing
Cheat-Proof trust model for cloud computing markets
GECON'12 Proceedings of the 9th international conference on Economics of Grids, Clouds, Systems, and Services
Solidifying the foundations of the cloud for the next generation Software Engineering
Journal of Systems and Software
Hi-index | 0.00 |
For a non IT expert to use services in the Cloud is more natural to negotiate the QoS with the provider in terms of service-level metrics --e.g. job deadlines-- instead of resource-level metrics --e.g. CPU MHz. However, current infrastructures only support resource-level metrics --e.g. CPU share and memory allocation-- and there is not a well-known mechanism to translate from service-level metrics to resource-level metrics. Moreover, the lack of precise information regarding the requirements of the services leads to an inefficient resource allocation --usually, providers allocate whole resources to prevent SLA violations. According to this, we propose a novel mechanism to overcome this translation problem using an online prediction system which includes a fast analytical predictor and an adaptive machine learning based predictor. We also show how a deadline scheduler could use these predictions to help providers to make the most of their resources. Our evaluation shows: i) that fast algorithms are able to make predictions with an 11% and 17% of relative error for the CPU and memory respectively; ii) the potential of using accurate predictions in the scheduling compared to simple yet well-known schedulers.