An Online Mechanism for Resource Allocation and Pricing in Clouds

Cloud providers provision their various resources such as CPUs, memory, and storage in the form of virtual machine (VM) instances which are then allocated to the users. The users are charged based on a pay-as-you-go model, and their payments should be determined by considering both their incentives and the incentives of the cloud providers. Auction markets can capture such incentives, where users name their own prices for their requested VMs. We design an auction-based online mechanism for VM provisioning, allocation, and pricing in clouds that considers several types of resources. Our proposed online mechanism makes no assumptions about future demand of VMs, which is the case in real cloud settings. The proposed online mechanism is invoked as soon as a user places a request or some of the allocated resources are released and become available. The mechanism allocates VM instances to selected users for the period they are requested for, and ensures that the users will continue using their VM instances for the entire requested period. In addition, the mechanism determines the payment the users have to pay for using the allocated resources. We prove that the mechanism is incentive-compatible, that is, it gives incentives to the users to reveal their actual requests. We investigate the performance of our proposed mechanism through extensive experiments

Measurements of Atmospheric Proteinaceous Aerosol in the Arctic Using a Selective UHPLC/ESI-MS/MS Strategy

In this article, an analytical methodology to investigate the proteinaceous content in atmospheric size-resolved aerosols collected at the Zeppelin observatory (79 °N, 12 °E) at Ny Ålesund, Svalbard, from September to December 2015, is proposed. Quantitative determination was performed after acidic hydrolysis using ultrahigh-performance liquid chromatography in reversed-phase mode coupled to electrospray ionization tandem mass spectrometry. Chromatographic separation, as well as specificity in the identification, was achieved by derivatization of the amino acids with N-butyl nicotinic acid N-hydroxysuccinimide ester prior to the analysis. The chromatographic run was performed within 11 min and instrumental levels of detection (LODs) were between 0.2 and 8.1 pg injected on the column, except for arginine which exhibited an LOD of 37 pg. Corresponding method LODs were between 0.01 and 1.9 fmol/m3, based on the average air sampling volume of 57 m3. The sum of free amino acids and hydrolyzed polyamino acids was shown to vary within 6-2914 and 0.02-1417 pmol/m3 for particles in sizes < 2 and 2-10 μm in equivalent aerodynamic diameter, respectively. Leucine, alanine, and valine were the most abundant among the amino acids in both aerosol size fractions. In an attempt to elucidate source areas of the collected aerosols, 5- to 10-day 3D backward trajectories reaching the sampling station were calculated. Overall, the method described here provides a first time estimate of the proteinaceous content, that is, the sum of free and polyamino acids, in size-resolved aerosols collected in the Arctic. Graphical Abstract ᅟ.status: Published onlin

Amyloid-β Peptide Interactions with Amphiphilic Surfactants: Electrostatic and Hydrophobic Effects

The amphiphilic nature of the amyloid-β (Aβ) peptide associated with Alzheimer's disease facilitates various interactions with biomolecules such as lipids and proteins, with effects on both structure and toxicity of the peptide. Here, we investigate these peptide-amphiphile interactions by experimental and computational studies of Aβ(1-40) in the presence of surfactants with varying physicochemical properties. Our findings indicate that electrostatic peptide-surfactant interactions are required for coclustering and structure induction in the peptide and that the strength of the interaction depends on the surfactant net charge. Both aggregation-prone peptide-rich coclusters and stable surfactant-rich coclusters can form. Only Aβ(1-40) monomers, but not oligomers, are inserted into surfactant micelles in this surfactant-rich state. Surfactant headgroup charge is suggested to be important as electrostatic peptide-surfactant interactions on the micellar surface seems to be an initiating step toward insertion. Thus, no peptide insertion or change in peptide secondary structure is observed using a nonionic surfactant. The hydrophobic peptide-surfactant interactions instead stabilize the Aβ monomer, possibly by preventing self-interaction between the peptide core and C-terminus, thereby effectively inhibiting the peptide aggregation process. These findings give increased understanding regarding the molecular driving forces for Aβ aggregation and the peptide interaction with amphiphilic biomolecules