A workload-specific performance brokerage for infrastructure clouds

• Main Author: O'Loughlin, John
• Other Authors: Gillam, Lee
• Published: University of Surrey 2018
• Subjects: 004
• Online Access: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.736919

Major Cloud providers offer a seemingly unlimited supply of compute resources for rent on-demand, with instances (virtual machines) being particularly popular amongst a range of service offerings. However, variation in performance across supposedly identical instances, supported by heterogeneous hardware, sold at the same price leads to variation in workload execution costs. Indeed, users pay more to have work delivered slower whilst missing out on the concomitant benefits of completing work faster and receive no performance assurances. To address price, performance, and assurance, we propose and evaluate a broker that re-prices cloud provider instances according to measured performance levels to offer performance-assured instances. Such a broker does not impose changes on Cloud provider business models, but to be viable the broker must be profitable and yet profitable Cloud brokers seem not to exist in the literature. We investigate broker profitability through simulations that model a commodity exchange analogously to extant financial exchanges, with sellers characterised in accordance with extant major Cloud providers and the broker modelled as a so-called Zero Intelligence (ZI) trading agent. Instance performance data are drawn from extensive benchmarking of Amazon’s Elastic Compute Cloud (EC2), and a Google workload trace comprising some 650,000 jobs provides for buyer willingness. The maximum profit margin that could be achieved by the proposed broker across multiple demand profiles is 4%, and achieving this requires 4 different types of hardware that exhibit an average performance degrade of 52% from best to worst. A loss is made under a variety of other conditions. At best, such a broker would support a low margin high volume business, leaving it sensitive to market competition, vagaries in demand, exchange transaction fees and gaming strategies that clients may be able to employ. As such, we question the viability of brokers proposed elsewhere which claim to offer performance services of various kinds, despite profitability not having been evaluated and, worse, without operational costs addressed. Original contributions from this research include: (1) quantification and characterisation of performance variation amongst Cloud instances; (2) a model of instance performance that is qualitatively similar to results found empirically in both cross-sectional and longitudinal studies; (3) an exposition of the underestimation of risk in extant performance improvement strategies; (4) a Cloud broker offering performance-assured instances and the set of assumptions to be met for profitability; and (5) a strategy for minimising risks to performance due to correlated instance usage.