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among multiple instances. It is important to note that no fully virtualised infrastructure services, such as EC2, provide QoS guarantees on the performance of hardware sharing.
With this information, we can project the lower bound cost of running the full-quality experiment in a similar situation, again using EC2 as the overflow resource. We scale MCMC iterations and hence job run-time by a factor of 100, and similarly, we scale the deadline to 50 days. We assume that the local resource pool has not changed. In 50 days, East can deliver us 50 × 24 × 120 = 144,000 CPU hours, the equivalent of 945 jobs. We need EC2 to complete the remaining 486 jobs, which requires at least 54,197 EC2-core-hours at US$0.10 an hour, resulting in a potential charge of US$5,420. Clearly, investing that amount of money into capital expenditure (e.g. to extend the capacity of East) would have made little difference to the overall completion time.
13.4.5 Scientific Results
Using Nimrod/G and associated computer clusters and clouds enabled us to explore a greater variety of SPM in a shorter, feasible, time frame without compromising the quality of the results. For instance, we tested the variants of the original model [26], which use different ways of measuring distances between museum exhibits [30]. This led to insights regarding the suitability of the different model variants for certain application scenarios.
In the future, we intend to investigate other ways of incorporating exhibit features into SPM. We also plan to extend our model to fit non-Gaussian data.
13.5 Conclusions
This chapter demonstrates the potential for cloud computing in high-throughput science. We showed that Nimrod/G scales to both freely available resources as well as commercial services. This is significant because it allows users to balance deadlines and budgets in ways that were not previously possible.
We discussed the additions to Nimrod/G required for it to use Amazon’s EC2 as an execution mechanism, and showed that the Nimrod/G architecture is well suited to computational clouds. As a result, Nimrod/G’s cloud actuator allows higher level tools to exploit clouds as a computational resource. Hence, Nimrod/G can be classified as providing a ‘Platform as a Service’ to job producers/schedulers, and becomes both a cloud client and cloud service in its own right.
The case study showed that computational clouds provide ideal platforms for high-throughput science. Using a mix of grid and cloud resources provided timely results within the budget for the research under discussion.
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The economies of scale employed by commercial cloud computing providers make them an attractive platform for HTC. However, questions of national interest and policy issues in spending public research funding with commercial providers remain, especially when they are based overseas. Commercial offerings are motivated by profit, and hence it should be possible to provide a non-profit equivalent more cheaply to better utilise government and university funding, while ensuring the prioritisation of researcher requirements. There is clearly scope for the adoption of similar operational techniques in order to provide HTC resources to the research community.
Commercial computing infrastructure requirements also deviate somewhat from typical HTC requirements1. The commercial cloud provider must have sufficient data-centre capacity to meet fluctuating demand, while providing high QoS with regard to reliability and lead time to service. This necessitates reserving capacity at extra expense, passed on to the consumer. On the other hand, HTC workloads are typically not so sensitive. Waiting some time for a processor is of little significance when tens of thousands to millions of processor hours are required. Such considerations may enable higher utilisation and lower capital overhead for dedicated HTC clouds.
Future work will focus on providing accurate cost accounting by implementing a time-slice scheduler and considering data-transfer charges. We also plan to investigate the use of EC2 Spot Instance pricing. This could prove ideal for cost minimisation biased scheduling, given the spot price for a particular machine type is typically less than half of the standard cost.
Acknowledgements This work has been supported by the Australian Research Council under the Discovery grant scheme. We thank the Australian Academy of Technological Sciences and Engineering (ATSE) Working Group on Cloud Computing for discussions that were used as input to Section 1. We thank Ian Foster for his helpful discussions about the role of high-throughput science and for his contribution to Section 2.
We acknowledge the work of Benjamin Dobell, Aidan Steele, Ashley Taylor and David Warner, Monash University Faculty of I.T. students who worked on the initial Nimrod EC2 actuator prototype. We also thank Neil Soman for assistance in using the Eucalyptus Public Cloud.
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Part III
Cloud Breaks