Efficient Response Time Predictions by Exploiting Application and Resource State Similarities
GRID '05 Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing
Appliance-Based Autonomic Provisioning Framework for Virtualized Outsourcing Data Center
ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
Resource overbooking and application profiling in a shared Internet hosting platform
ACM Transactions on Internet Technology (TOIT)
In Cloud, Can Scientific Communities Benefit from the Economies of Scale?
IEEE Transactions on Parallel and Distributed Systems
Application-specific Cloud Provisioning Model Using Job Profiles Analysis
HPCC '12 Proceedings of the 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems
SCC '12 Proceedings of the 2012 SC Companion: High Performance Computing, Networking Storage and Analysis
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Recent cloud computing enables numerous scientists to earn advantages by serving on-demand and elastic resources whenever they desire computing resources. This science cloud paradigm has been actively developed and investigated to satisfy requirements of the scientists such as performance, feasibility and so on. However, effective allocation and provisioning virtual machines on clouds are still considered as a challenging issue in scientists using high throughput computing, since it determines whether they can earn benefits from economy of scale in clouds or not. Moreover, allocating the "right" provisioned cloud resources on an optimal data center is very important as performance can vary widely depending on where and under what circumstances it actually runs. In these reasons, it is required that an appropriate and suitable model for science cloud to support increasing scientists and computations. In this paper, we present an allocation and provisioning model of science cloud, especially for high throughput computing applications. In this model, we utilize job traces where statistical method is applied to pick the most influential features for improving application performance. With the feature, the system determines where VM is deployed (allocation) and which instance type is proper (provisioning). An adaptive evaluation step which is subsequent to the job execution enables our model to adapt to dynamical computing environments. We show performance achievements as comparing the proposed model with other policies through experiments. Finally, we expect that improvement on performance as well as reduction of cost from resource consumption through our model.