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Cloud computing is a growing technology where lot of heterogeneous resources are available and large amount of requests are submitted by the customers simultaneously. So it is difficult to match the requests and resources based on the expectations of customers and providers. This paper proposes the resource allocation using auction based technique to reduce the complexity of providing the resources for customers job execution and fulfill the expectations of both customers and providers in cloud environment. In the proposed work the Enhanced Multi-attribute Combinative Double Auction (EMCDA) resource allocation algorithm is used to conduct the auction to the customers bids with the providers bids by the cloud auctioneer for finding the best customer-providers pairs and achieves the customers and providers satisfaction using the normalization factors during price calculation in the cloud computing environment. The experimental result demonstrates that the proposed Enhanced Multi-attribute Combinative Double Auction (EMCDA) resource allocation algorithm performs efficiently than the existing Combinatorial Double Auction Resource Allocation (CDARA) model. The proposed EMCDA model is incentive-compatible, which encourage the participants of an auction to reveal their true valuation during bidding.

As one of promising distributed multi-robot system, Unmanned Aerial Vehicles (UAVs) can collaborate to offload complex tasks in edge networks. A Smart and Trust Multi-UAV Task Offloading (STMTO) system is established to offload tasks from Internet of Thing (IoT) devices to edge severs through UAVs with a trust style. In STMTO system, first, a group of UAVs is dispatched to relay tasks from devices to edge servers with rich computing resource. A collaborative task collection scheme is proposed to minimize energy consumption and the task processing delay by dividing working area for each UAV and designing the flight trajectory . Secondly, a many-to-many task double auction model is established for devices and edge servers to maximize the offloading utility, where devices act as buyers, edge servers as sellers, and UAVs as auctioneers. Last, to resist attack of malicious edge servers and ensure the task security, a novel trust evaluation method based on the comparison of true utility and expected utility is integrated in auction mechanism. The theoretical analysis and implementation results show that the proposed STMTO system not only achieve the best utility for devices and edge servers simultaneously, but also identify the malicious edge servers and protect task from attacks.

An E-auction mechanism has been considered as the most popular activity in E-commerce model. However, it relies on the assumption that a fully trusted third party is required to guarantee the system, which is hard to be implemented in the real-world. In this article, we aim to design an ad-hoc combinatorial double auction framework based on the blockchain technique. The properties of decentralization and nontempering can be used to realize a fair transaction and enhance the flexibility without the third party. A participant can follow his/her will to launch an auction play with the adoption of smart contract, and an improved genetic algorithm is designed to figure out an optimal pairing solution within a reasonable period. Experiments have demonstrated that the new framework can fulfill most transactions to benefit both seller and buyer no matter how many players and commodities have involved into the auction.

In this paper, we propose a Peer-to-Peer (P2P) energy trading scheme between EVs and the SPs to manage the demand response in V2G environment. Unlike the traditional schemes, which consists of complex energy-transportation meshes, the proposed scheme achieves a balance between demand and response by providing incentives to EVs out of their self-interests. However, the online transactions security and privacy protection of EVs poses challenges with respect to confidentiality and integrity preservation. To cope up with the issues, we design a consortium blockchain-based scheme to ensure secure energy transactions between EVs and the SPs without trusted third-party intervention. Moreover, the energy pricing and the amount of traded energy problems for demand response are solved by a double auction mechanism to maximize the social welfare. Numerical results based on a real-time implementation on a private Ethereum network having smart contract demonstrated that the auction-based mechanism achieves social welfare maximization with privacy protection of online transactions between EVs and the SPs. The results obtained show that the rate of convergence, scalability metric average latency, and standard deviation of the energy transactions have been improved by approximately 18.18%, 15%, and 28.1%, respectively in comparison to the existing schemes. Moreover, security and privacy analysis of the proposed scheme shows that it improves the transaction security substantially in comparison to the existing state-of-the-art proposals.

Distributed energy resources, together with information and communication technologies, have transformed the traditional electricity consumers into proactive consumers, namely prosumers. Prosumers can exchange their surplus energy with consumers through peer-to-peer (P2P) energy sharing. In this paper, the framework of P2P energy exchange in active distribution networks is developed using a multi-round double auction (MRDA) with average pricing mechanism (APM) integrated with distributional locational marginal price. The advantages of the proposed P2P framework include, 1) modeling and integration of the costs of voltage regulation, congestion, and power loss into the payments of agents for each transaction; 2) the entire distribution network clustered into multiple zones with transactions cleared at different levels, which decreases the additional costs for successful transactions, reduces the computational time, and increases the number of successful transactions; and 3) the matching algorithm encourages more prosumers and consumers to participate in P2P energy sharing and increases the efficiency and benefit from P2P market. The proposed MRDA-APM framework is validated by testing on the 33-node and 141-node distribution test systems. Simulation results demonstrate the effectiveness of the proposed mechanism for P2P energy exchange from both technical and computational viewpoints.

Cloud computing is a business model where having users and providers are competing to sell and buy services. Due to diverse software applications and dynamic cloud market, customers’ requirements are ever changing. Dynamic auction-based resource allocation models, namely: combinatorial double auction resource allocation (CDARA) model attracted the researchers’ attention. In existing work services are allocated to users based on less price, which may lead to service-level agreement (SLA) violation and increase user’s dissatisfaction in the cloud marketplace. Decision on single attribute price may create genuine and ungenuine providers issue, as the providers will try to provide low-quality services. To solve these issues, we proposed adaptive market-oriented combinatorial double auction resource allocation (AMO-CDARA) model that allocates services to users based on multiple parameters such as less price, QoS, and providers ranking. In the proposed model, bids are received by auctioneer from multiple providers and users. Auctioneer calculates users and providers bid densities and allocates services to the most efficient users from most efficient providers according to their required request. After successful running of tasks, the auctioneer asks for feedback of the used services from users and calculates the final ranking of providers for future auctions. The simulation results show that the proposed model guaranteed SLA violation up to 99% due to twice punishment penalty and ranking of providers. The guaranteed QoS users/brokers will provide extra payment for that, and the extra payment will be from 1 to 10% of the total price, according to the final price. Furthermore, we solved the bidder drop problem up to 10% by increasing service price by adding QoS price.

Peer-to-Peer (P2P) resource sharing promotes local resource-hungry task offloading to other mobile devices and balances the resource consumption between mobile devices. Most of existing P2P task offloading systems aims to solve the resource sharing between one pair exclusively without considering the cost of resource supply and the strategic behaviors of mobile users. In this paper, we propose two user models for the P2P task offloading system: honest user model and strategy user model. For the honest user model, we formulate the resource allocation maximization problem with latency and energy consumption constraints as an Integer Linear Programming. We show that the solution for honest user model can output 189% resource transactions of that for the strategic users. For the strategy user model, we propose a double auction-based P2P task offloading system, and design a truthful multi-resource transaction mechanism to maximize the number of resource transactions. We first group the mobile users based on the connected components to improve the efficiency of double auction. Then we utilize the McAfee Double Auction to price the resource transactions. Finally, we split each winning mobile user of double auction into multiple virtual mobile users, and use the matching approach to calculate the resource allocation. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the designed multi-resource transaction mechanism satisfies the desirable properties of computational efficiency, individual rationality, budget balance, truthfulness for resource request/supply, and general truthfulness for bid/ask price.

This paper investigates the existence of a nondegenerate pure-strategy Bayesian-Nash equilibrium in a double auction between one seller and several heterogeneous buyers in the independent private value framework. It begins with three examples describing some of the model’s particular features. After studying a number of continuity-related properties of the interim payoff functions, we construct a sequence of strategy profiles that converges to a nondegenerate monotone Bayesian-Nash equilibrium of the game under some strict monotonicity conditions. The equilibrium existence result is applied to a double auction with risk-averse bidders.

Blockchain has been widely applied in various fields, such as finance, Internet of Things, law, etc. However, it is a challenge to apply blockchain to mobile applications, as some mobile devices cannot afford computing resources required by the mining processes. Therefore, edge computing is introduced to provide computing resources for mobile devices. We propose an allocation mechanism based on combinatorial double auction to offload the mining tasks to the edge servers. The corresponding allocation and payment schemes are proposed to generate allocation results and calculate clearing prices, respectively. Moreover, we prove that the proposed mechanism is computation efficient, and it satisfies three auction properties: budget balance, individual rationality and truthfulness. Experimental results show that the proposed mechanism is able to yield higher total utility, together with good scalability. For the case of 500 miners in the auction, the total utility increases by 6 times, and utilization ratio of edge server increases by 1.5 times, in comparison to the existing benchmark approaches.

This article proposes a double auction-based mechanism that captures the interaction within a community energy sharing market consisting of distributed solar power prosumers and consumers. All agents are assumed to have battery energy storage systems, and can use battery for demand response. Agents can optimize the charging/discharging schedules of their battery systems for community sharing to reduce electricity costs. To determine the double-side auction market spot price, a non-cooperative game is formulated among all participants involved in the community sharing. An iterative algorithm is first designed to clear the market and mitigate the uncertainty in supply and demand. Then, an adaptive pricing strategy is designed to assist agents better estimate the market and predict the future price. A case study with 10 agents is provided to evaluate the effectiveness of the proposed community sharing market.

Double auction is becoming the preferred negotiation protocol for cloud service negotiation due to its economic efficiency, capability in facilitating dynamic pricing, and suitability for handling a large number of customers and service providers. However, as far as this research work is concerned, there is no framework using double auction that simultaneously and comprehensively addresses the heterogeneity of cloud services in multi-attributes negotiation. Before such a framework can be designed, suitable multi-attributes negotiation techniques and its attributes should be identified. Therefore, this paper’s objective is to distinguish and provide an overview of multi-attributes techniques used in double auction negotiations. The sources are from the Scopus Database. It is found that the current multi-attributes techniques lacked in addressing the preferential dependency, selective attributes and utility optimization simultaneously in double auction frameworks. These concerns need to be addressed in order to devise a practical framework. The future direction for double auction framework in multi-attributes negotiation is suggested.