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Dynamic forms of resource pricing have recently been introduced by cloud providers that offer Infrastructure as a Service (IaaS) capabilities in order to maximize profits and balance resource supply and demand. The design of a mechanism that efficiently prices perishable cloud resources in line with a provider’s profit maximization goal remains an open research challenge, however. In this article, we propose the Online Extended Consensus Revenue Estimate mechanism in the setting of a recurrent, multiunit and single price auction for IaaS cloud resources. The mechanism is envy-free, has a high probability of being truthful, and generates a near optimal profit for the provider. We combine the proposed auction design with a scheme for dynamically calculating reserve prices based on data center Power Usage Effectiveness (PUE) and electricity costs. Our simulation-based evaluation of the mechanism demonstrates its effectiveness under a broad variety of market conditions. In particular, we show how it improves on the classical uniform price auction, and we investigate the value of prior knowledge on the execution time of virtual machines for maximizing profit. We also developed a system prototype and conducted a small-scale experimental study with a group of 10 users that confirms the truthfulness property of the mechanism in a real test environment.

Economic forms of resource management in which users can express their valuations for service, offer new possibilities for optimizing resource allocations in Grids. If users are to correctly express these valuations, quality of service guarantees need to be given with respect to the turnaround time of their workloads. Market mechanisms that support bidding and allocations in future time are crucial for delivering such guarantees. To deal with the significant delays that these mechanisms introduce in the allocation process, we present a hybrid market approach in which a low-latency spot market coexists with a higher latency futures market. Based on simulated market scenarios, we show how this combination can significantly increase the total value realized by the Grid infrastructure. We also demonstrate how providers can react to price dynamics in such a hybrid market setting.

Special conditions are required for genetic differentiation to arise at a local geographical scale in the face of gene flow. The Natal multimammate mouse, Mastomys natalensis, is the most widely distributed and abundant rodent in sub-Saharan Africa. A notorious agricultural pest and a natural host for many zoonotic diseases, it can live in close proximity to humans and appears to compete with other rodents for the synanthropic niche. We surveyed its population genetic structure across a 180-km transect in central Tanzania along which the landscape varied between agricultural land in a rural setting and natural woody vegetation, rivers, roads and a city (Morogoro). We sampled M. natalensis across 10 localities and genotyped 15 microsatellite loci from 515 individuals. Hierarchical STRUCTURE analyses show a K-invariant pattern distinguishing Morogoro suburbs (located in the centre of the transect) from nine surrounding rural localities. Landscape connectivity analyses in Circuitscape and comparison of rainfall patterns suggest that neither geographical isolation nor natural breeding asynchrony could explain the genetic differentiation of the urban population. Using the isolation-with-migration model implemented in IMa2, we inferred that a split between suburban and rural populations would have occurred recently (<150 years ago) with higher urban effective population density consistent with an urban source to rural sink of effective migration. The observed genetic differentiation of urban multimammate mice is striking given the uninterrupted distribution of the animal throughout the landscape and the high estimates of effective migration (2N M = 3.0 and 29.7), suggesting a strong selection gradient across the urban boundary.