D2 3 Computing e-Infrastructure cost calculations and business _models_vam1-final

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e-‐FISCAL: www.efiscal.eu EC Contract Number: 283449 5. Discussion and Recommendations 5.1 State of the art The state of the art review brought significant inputs and insights into the e-‐FISCAL project, from costing models, categories and methodologies to business and pricing models. Most importantly it provided clear case studies in the US and UK whose numbers could be easily be compared with the e-‐FISCAL ones, as long as the fact that these case studies were ranging from single sites to a national infrastructure was kept in mind. The state of the art became a focal point of e-‐FISCAL and gathered considerable part of the web site traffic. 5.2 Methodology and cost model As a follow-‐up from the first point (state of the art review), it is evident that e-‐FISCAL has been a pioneer effort trying to capture overall picture of the highly distributed and heterogeneous European environment. The e-‐FISCAL hybrid methodology and cost model were realistic so as to be able to provide the expected results. The pros and cons of the e-‐FISCAL model are summarised below. The merits: • It is a hybrid model that builds on FCA and TCO and adapts to real case constrains. • It includes all cost categories identified in literature are relevant to e-‐Infrastructures costing. • It balances easiness of information collection with precision in results. • It is easy to apply as it only has a few input requirements. • It provides acceptable cost estimations precision. It does not aim at providing detailed costing data but is good enough to approximate cost. It is also suitable for cross site comparisons and cost assessments through time. • It is transparent and auditable. • It is suitable to perform sensitivity analysis. • It can be used by stakeholders outside the organization. However the limitations of the model are as follows: • It cannot generate as precise cost estimations for a specific centre as Full Cost Accounting (provided that all costs are accounted for in accounting books). • The simulation of e-‐Infrastructures is mainly based on current or recent procurement costs. As prices go down (our evidence confirms that, see Del. 2.2), we may have underestimated CAPEX (if infrastructure in place has been procured in higher prices). Nevertheless, old machines consume more energy which balances things. OPEX has been correctly estimated regarding electricity consumption. • There is no differentiation in the calculated cost per core between in-‐house machines. We treat core hour as a homogenous service regardless of the machine. • The accuracy of findings is reliant on the completeness and reliability of inputs. Nevertheless, the interviews provided clarifications where issues have been identified and we have also isolate outliers. • The model concentrates on computing cost not on storage. However, part of the storage cost is included (as with other similar studies). e-‐FISCAL : Financial Study for Sustainable Computing e-‐Infrastructures Deliverable D2.3 – Computing e-‐Infrastructures cost estimation and analysis – Pricing and Business models 70
e-‐FISCAL: www.efiscal.eu EC Contract Number: 283449 5.3 e-FISCAL sample and main findings e-‐FISCAL received 28 high quality answers from 16 countries which constitute a diverge sample from different types of centres in terms of size (small and large), in terms of nature (HPC and HTC), with a low or high number of staff, etc. e-‐FISCAL was able to calculate several metrics, including cost per core hour, CAPEX/OPEX ratio, depreciation rates, cost distribution, electricity PUE. The main findings can be summarised as follows: • Cost effectiveness: In-‐house HPC/HTC e-‐Infrastructures are cost-‐effective with the high average utilisation rates (65-‐75%) and depreciation rates (5 years) recorded. However, per application cost analysis is needed and e-‐FISCAL did not have the resources to execute several comparative application case studies. • Cost structure: OPEX dominates the costs (around 70%) – Personnel costs are around half of the total costs, while personnel costs vary a lot around Europe. • Comparison with state of the art: The e-‐FISCAL findings are in-‐line with the results of similar studies identified in the state of the art through literature review. • HPC-‐ HTC comparison: It appears that the HPC sites of the e-‐FISCAL sample (national Tier-‐1 centres) are in average cheaper than corresponding HTC ones. Possible reasons for this are the higher number of cores per CPU, the state of the art energy systems, and the fact that larger sites have in most cases less FTEs per core. • Cost on the basis of size (no of cores) -‐ Big vs. small sites: It appears that sites with 1000 cores. Indicative values for cost per core hour are around € 0.09 for the first category and € 0.05 for the latter. • Cost structure heterogeneity: there is heterogeneity in the cost structure of sites/centres (in terms of cost categories). However, it is rather evident that in the vast majority of cases OPEX is not less than 40% of total costs and in the average case OPEX dominates costs. • In-‐house HTC and HPC vs. commercial Clouds; Benchmarking: Cloud service providers usually have a wide portfolio of instances and services. o Comparisons between in-‐house and commercial clouds have to be made very carefully, trying to compare like with like. Benchmarking is thus very relevant in this exercise as it identifies comparable performances between in-‐house configurations and cloud instances, especially because the cloud instance description might not always be easily comparable. o Performance degradation of cloud virtual machines with the in-‐house e-‐Infrastructure have to be accounted for through appropriate weight factors, especially in cases where virtual machines are compared to bare metal. o With the small-‐scale benchmarking exercise a 27% and 43% degradation has been averaged for HTC and HPC respectively. o Taking these degradations into account and comparing Amazon EC2 with the e-‐FISCAL HTC and HPC costs, it is obvious that: Amazon standard reserved instance prices are comparable with e-‐FISCAL ones and sometimes cheaper (the 3-‐year reserved ones) Amazon standard On-‐demand ones are always more expensive than e-‐FISCAL Amazon cluster compute ones (HPC in the cloud) are always much more expensive (1.5 to around 5 times) • Several disclaimers have to be taken into account though as analysed in the previous section such as comparing 2012-‐2013 prices with 2011 costs, the exclusion of e-‐FISCAL : Financial Study for Sustainable Computing e-‐Infrastructures Deliverable D2.3 – Computing e-‐Infrastructures cost estimation and analysis – Pricing and Business models 71
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D2 3 Computing e-Infrastructure cost calculations and business _models_vam1-final

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