Market-oriented reverse auction is an efficient and cost-effective method for resource allocation in cloud workflow systems since it can dynamically allocate resources depending on the supply-demand relationship of the cloud market. However, during the auction the price of cloud resource is usually fixed, and the current resource allocation mechanisms cannot adapt to the changeable market properly which results in the low efficiency of resource utilization. To address such a problem, a dynamic pricing reverse auction-based resource allocation mechanism is proposed. During the auction, resource providers can change prices according to the trading situation so that our novel mechanism can increase the chances of making a deal and improve efficiency of resource utilization. In addition, resource providers can improve their competitiveness in the market by lowering prices, and thus users can obtain cheaper resources in shorter time which would decrease monetary cost and completion time for workflow execution. Experiments with different situations and problem sizes are conducted for dynamic pricing-based allocation mechanism (DPAM) on resource utilization and the measurement of Time⁎Cost (TC). The results show that our DPAM can outperform its representative in resource utilization, monetary cost, and completion time and also obtain the optimal price reduction rates.

Carcinoembryonic antigen (CEA) is a biomarker that is highly expressed in cancer patients. Label-free, highly sensitive, and specific detection of CEA biomarkers can therefore greatly aid in the early detection and screening of cancer. This study presents a toroidal metamaterial biosensor integrated with functionalized gold nanoparticles (AuNPs) that demonstrated highly sensitive and specific detection of CEA using terahertz (THz) time-domain spectroscopy. In the biosensor, a closed-loop magnetic field formed an electrical confinement, resulting in a high sensitivity (287.8 GHz/RIU) and an ultrahigh quality factor (15.04). In addition, the integrated AuNPs with high refractive indices significantly enhanced the sensing performance of the biosensor. To explore the quantitative and qualitative detection of CEA, CEA biomarkers with various concentrations and four types of proteins were measured by the designed biosensor, achieving a limit of detection of 0.17 ng and high specificity. Even more significant, the proposed AuNP-integrated THz toroidal metamaterial biosensor demonstrates exceptional potential for use in technologies for cancer diagnosis and monitoring.

Multi-tenant service-oriented systems (SOSs) have become a major software engineering paradigm in the cloud environment. Instead of serving a single end-user, a multi-tenant SOS provides multiple tenants with similar and yet customized functionalities and potentially different quality-of-service (QoS) values. Multiple tenants' differentiated multi-dimensional quality constraints for the SOS further complicates the NP-hard problem of quality-aware service selection. Existing quality aware service selection approaches suffer from poor success rates of finding a solution, especially in scenarios where tenants' quality constraints are stringent, due to the lack of systematic consideration of three critical issues: 1) the need to fulfil multiple tenants' differentiated quality constraints; 2) the competition among service providers; and 3) the complementarity between services. This paper proposes a novel approach called combinatorial auctionbased service selection for multi-tenant SOSs (CASSMT) to support effective quality-aware service selection for multi-tenant SOSs. CASSMT allows service providers to bid for the services of an SOS expressively. Based on received bids (i.e., QoS offers), CASSMT attempts to find a solution that achieves the system developer's optimization goal while fulfilling all tenants' quality constraints for the SOS. When no solution can be found based on the current bids, service providers can improve their bids to increase their chances of winning, which in the meantime, increases the chances of finding a solution. The experimental results show that CASSMT outperforms representative approaches in the success rate of finding a solution and system optimality. Meanwhile, its efficiency, measured by the number of auction rounds and computation time, is demonstrated to be satisfactory in scenarios on different scales.

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