Resource Buyer Research Articles

Multi-Round Auction-Based Resource Allocation in Multi-Access Edge Computing Assisted Satellite Networks

In this paper, we study the resource allocation problem of multi-access edge computing (MEC) assisted satellite networks in ocean areas. Considering the demand heterogeneity of users and the limited resources of satellites, we propose an online multi-round auction-based resource allocation (OMARA) approach for resource trading between satellites and users, where satellites are the resource sellers and users are the related resource buyers. The proposed approach can effectively determine the price for the trading of resources and can match the service relationships accordingly between satellites and ground users. Finally, it is shown by the simulation experimental results that the proposed approach outperforms the other existing algorithms in maximizing service satisfaction.

Computing Resource Provisioning at the Edge: An Overbooking-Enabled Trading Paradigm

The arrival of the Internet of Things (IoT) era calls for cost-effective resource provisioning techniques to secure necessary computing resources in supporting dynamic and computation-intensive applications on diverse IoT devices, where resource trading in edge networks brings a viable prospect. While conventional onsite spot trading may facilitate this paradigm, the inherent extra latency of onsite decision making and unreliable network significantly limit resource trading efficiency and the eventual resource utilization rate. More importantly, the “no show” of confirmed resource buyers incurred by unreliable network conditions, can result in negative impacts on trading performance and resource usage. To this end, resource overbooking is adopted that provides an effective solution in handling “no shows” while substantially improving resource utilization rate. In this article, an interesting overbooking-enabled resource trading paradigm in mobile edge networks is proposed, allowing pre-negotiated forward contracts between the edge server (seller) and a number of overbooked smart devices (buyers), which will be fulfilled during future trading. Specifically, a framework associated with the proposed trading paradigm is introduced first to provide a comprehensive view of the relevant key issues. Then a case study is presented that elaborates how the proposed trading can be implemented in practice. Extensive simulation results and analysis demonstrate that the overbooking-enabled trading paradigm achieves both time and energy efficiency, as well as substantial resource utilization and profit improvements.

An Effective Data-Driven Cloud Resource Procurement Scheme With Personalized Reserve Prices

The selection of cloud resources is important to users which influences their utility directly. To improve the users’ utility of purchased resources, according to the historical bidding information, we present data-driven cloud resource procurement (CRP) auctions which can help the resource buyer make an optimized selection for cloud resources. First, we design two procurement auction mechanisms with personalized reserve prices for CRP: Lazy-CRP and Eager-CRP, in which the reserve prices are set by the broker before the auction based on the broker’s knowledge about costs of cloud providers. Then, given the assumption that each cloud provider is myopic, we propose the optimal learning algorithms of personalized reserve prices for the Lazy-CRP approach, and 2-approximation learning algorithm for the Eager-CRP approach. Both of two algorithms can be performed even if the cost distribution functions of cloud providers are nonidentical. By comparing our mechanisms with Vickery–Clark–Groves CRP (VCG-CRP) in simulation, the results show that our proposed mechanism is very suitable for occasions where the number of providers is very small. According to the results of the simulation, the total utilities obtained by the data-driven procurement schemes are significantly higher than VCG-CRP when the number of cloud providers is less than four. When there are only two providers, the increased total utility of our mechanism can be up to 80% of that obtained by VCG-CRP.

Optimal data offloading via an ADMM algorithm in mobile ad hoc cloud with malicious resource providers

Offloading has been widely adopted as an effective technique to overcome the processing and computation limitation in mobile networks. In this paper, a utility maximization model of mobile ad hoc environment with some malicious resource providers (RPs) is built. The price incentive mechanism obtained by solving the model can effectively promote RPs to share their idle resources to resource buyer (RB). To identify the malicious RPs, the mechanism of identification and processing the inaccurate data is designed and completed by RB. Combined with this mechanism, we propose a distributed optimization algorithm based on modified alternating direction method of multipliers (ADMM) algorithm to realize the maximum utility of the overall system. Finally, through comparative analysis, the simulation results verify the feasibility of the proposed distributed optimization algorithm and the necessity of identifying malicious RPs.

An Optimal Real-Time Distributed Algorithm for Utility Maximization of Mobile Ad Hoc Cloud

In this letter, we investigate utility maximization of mobile ad hoc cloud with an incentive mechanism to encourage mobile devices to share their idle resources. Considering that at different time slots the amount of resources demanded by the resource buyer (RB) is different and the revenue of per unit resource obtained by resource providers (RPs) is different, a real-time distributed algorithm is developed. First, by analyzing the preferences of the RB and RPs, the utility function and cost function are developed for them, respectively. Then, we propose a real-time distributed algorithm to find the maximum utility of the overall system under the price incentive mechanism, where the obtained optimal pricing can align the individual optimality with the overall system optimality. Simulation results confirm that the proposed algorithm can maximize the utility of the overall system compared with the state-of-the-art schemes.

Resource Allocation in Grid Computing Environment Using Genetic–Auction Based Algorithm

The main core functionality of Grid Computing is resource allocation and scheduling. With the idea of genetic algorithms and microeconomics, it is proposed a Resource allocation method called a genetic-auction based algorithm [GAAB]. This algorithm contains two modules, auction module and genetic approach. Auction module find outs resource-trading price between resource provider and resource buyer, and the resource allocation carried out by Genetic algorithm by considering both time and cost constraints simultaneously. In this article, evaluations are made in the simulation environment and the results show the effectiveness of the proposed model.

How do strategic factor markets respond to rivalry in the product market?

This paper explores the interplay between product market, strategic factor market, and resource development. More competition in the product market makes resource buyers bid higher for resources, as the value of trying to preempt the resources is higher. Holding other initial conditions constant, resources are developed more in industries with factor markets than in industries without. When buyers of resources cannot integrate more than one resource, developers choose to develop either at a low or high level, generating a type of heterogeneity that would not arise otherwise. Changes in the intensity of competition in the product market can have the opposite effect on resource development efforts depending on the presence or absence of factor markets. Copyright © 2013 John Wiley & Sons, Ltd.

Trade with Polluting Nonrenewable Resources

The paper considers nonrenewable resource extraction in a situation where the resource buyers have formed a government which applies emission taxation for slowing pollution accumulation and the sellers are competitive or a resource cartel. The noncompetitive situation is studied as a Stackelberg differential game which the cartel leads. It is first assumed that extraction costs do not depend on the resource stock level and that the pollution stock does not decay. In the time-consistent and Markov-perfect equilibrium the sellers' monopoly power vanishes asymptotically. The sellers' market power reduces the buyers' emission tax at each point in time. In the Stackelberg equilibrium the emission tax is below the marginal present value of pollution damage. If pollution decays and extraction costs depend on the resource stock level, buyers have monopsony power and the ability of sellers to receive emission tax revenues is weakened. When the rate of pollution decay is high the producer price falls below the Pareto optimal level.