Current large distributed systems allow users to share and trade resources. In cloud computing, users purchase different types of resources from one or more resource providers using a fixed pricing scheme. Federated clouds, a topic of recent interest, allows different cloud providers to share resources for increased scalability and reliability. However, users and providers of cloud resources are rational and maximize their own interest when consuming and contributing shared resources. In this paper, we present a dynamic pricing scheme suitable for rational users requests containing multiple resource types. Using simulations, we compare the efficiency of our proposed strategy-proof dynamic scheme with fixed pricing, and show that user welfare and the percentage of successful requests is increased by using dynamic pricing.

Resource sharing on the Internet is becoming increasingly pervasive. Recently, there is growing interest in distributed systems such as peer-to-peer and grid, with efforts being directed towards resource allocation strategies that incentivize users to share resources. While combinatorial auctions can perform multiple resource type allocations, it is computationally a NPcomplete problem. Thus, allocation in large distributed resource sharing systems focuses mainly on a single resource type. We propose a strategy-proof, VCG-based resource pricing scheme for resource allocation in dynamic markets where users behave rationally in meeting their own interest. Our mechanism is designed to meet the needs of large distributed systems, delivering the following key properties: multiple resource type allocations, individual rationality, incentive compatibility for both buyers and sellers, budget balance and computational efficiency. Simulation evaluation of our prototype based on a centralized implementation demonstrates the viability of our approach, as compared to both traditional and combinatorial auctions.

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Growing concern about the supply of goods under the COVID pandemic due to border restrictions and community lockdown has made us aware of the limitations of the global supply chain. Fertilizers are pivotal for the growth and welfare of humankind, and there is more than a century of history in industrial technology. Ammonia is the key platform chemical here which can be chemically diversified to all kinds of fertilizers. This article puts a perspective on production technologies that can enable a supply of ammonia locally and on-demand in Australia, for the farmers to produce resilient and self-sustained fertilizers. To assess the validity of such a new business model, multiobjective optimization has to be undergone, and computing is the solution to rank the millions of possible solutions. In this lieu, an economic optimization framework for the Australian ammonia supply chain is presented. The model seeks to address the economic potential of distributed ammonia plants across Australia. Different techniques for hydrogen and related ammonia production such as thermal plasma, nonthermal plasma, and electrolysis (all typifying technology disruption), and mini Haber–Bosch (typifying scale disruption) are benchmarked to the central mega plant on a world-scale using conventional technology, verifying that “Moore’s Law” (Mack, C. A. IEEE Trans. 2011, 24 (2), 202–207) of growing bigger and bigger is not the only path to sustainable agriculture. Results show that ammonia can be produced at $317/ton at a regional scale using thermal plasma hydrogen generation which could be competitive to the conventional production model, if credit in terms of lead time and carbon footprint could be taken into account.

Abstract. There is growing interest in large-scale resource sharing with emerging architectures such as cloud computing, where globally distributed and commoditized resources can be shared and traded. Federated clouds, a topic of recent interest, aims to integrate different types of cloud resources from different providers, to increase scalability and reliability. In federated clouds, users are rational and maximize their own interest when consuming and contributing shared resources, while globally distributed resource supply and demand changes as users join and leave the cloud dynamically over time. In this paper, we propose a dynamic pricing scheme for multiple types of shared resources in federated clouds and evaluate its performance. Fixed pricing, currently used by cloud providers, does not reflect the dynamic resource price due to the changes in supply and demand. Using simulations, we compare the economic and computational efficiencies of our proposed dynamic pricing scheme with fixed pricing. We show that the user utility is increased, while the percentage of successful buyer requests and the percentage of allocated seller resources is higher with dynamic pricing.