Table 21.1  Prices for Amazon AWS showing different classes of priced instances with different (virtualised) hardware specifications (prices as on January 2010)

a One EC2 computer unit provides equivalent to 1.0~1.2 GHz Intel Opteron or Xeon processor

If we have good understanding of the requirements of an application such that we are able to find matching resources at the right price, then we may begin to search through the options on offer. Here, the consumer is attempting to achieve the best approximate fit. However, the best value may have come if a wider variety of configurations were available or could be specifiable. The consumer would outline their needs, and a range of providers would make offers to the consumer in order to secure their business. Consumers may get better pricing depending on a variety of factors, and the service for comparability would offer opportunities for markets in computational equivalents of financial instruments – where these may be contracts of different values based on the SLAs – and even derivatives of such instruments. These SLAs may need to reference a portfolio of computational resources, introducing some notion of risk into the SLA itself (see, for example, [9]). This would further suggest that organisations may offer variable SLAs in which price accounts for risk – cheaper resources imply more risk and less liability in the event of failure. Here, we have been inspired by the notions of tranches and subordination in financial CDO models such that higher-value SLAs are those that shall be satisfied first [10–12]. We believe that such a framework might assist providers or brokers to optimise system utilisation and offer the best value for money with dynamically configured systems. As such, cloud markets may emerge based on such considerations and others made previously in relation to grid economics [8]. However, much of the work of understanding applications in order to derive the required service description terms and guarantee terms for the SLAs is still needed, and initial comparability across resources, as described in the remainder of this chapter, is a vital step towards this.

21.3  Experiment

21.3.1  Target Application: Value at Risk

Value at Risk (VaR) typically computes a value from a distribution of returns (profit or loss against the previous day) of financial instruments. The value obtained from this analysis is the largest expected loss at a specific confidence