Auction-based Resource Allocation Research Articles

Truthful Auction-Based Resource Allocation Mechanisms With Flexible Task Offloading in Mobile Edge Computing

Truthful Auction-Based Resource Allocation Mechanisms With Flexible Task Offloading in Mobile Edge Computing

Multi-attribute Auction-Based Resource Allocation for Twins Migration in Vehicular Metaverses: A GPT-Based DRL Approach

Multi-attribute Auction-Based Resource Allocation for Twins Migration in Vehicular Metaverses: A GPT-Based DRL Approach

An auction-based resource allocation in Cloud Radio Access Network (C-RAN)

An auction-based resource allocation in Cloud Radio Access Network (C-RAN)

A Comparative Study of Auction Theory Applications: Insights from India and Global Perspectives

This research paper presents a comprehensive comparative study of auction theory applications, exploring insights from both the Indian context and global practices. Auction theory plays a vital role in resource allocation, market efficiency, and economic decision-making across various industries. The study analyses different auction mechanisms used in India and other countries, evaluating their effectiveness and suitability in diverse economic environments. The paper delves into specific sectors where auction theory is applied, such as spectrum auctions for telecom operators, infrastructure project auctions, and public procurement auctions for goods and services. By examining auction outcomes, bidder strategies, and their impact on industries, the study provides valuable insights into the adaptation and effectiveness of auction theory in addressing unique challenges and opportunities. The research investigates challenges faced in implementing auction-based systems in India and other economies, offering potential solutions and policy measures to address issues like information asymmetry and regulatory complexities. It compares the economic implications of auction outcomes in India and other countries, including revenue generation and market efficiency. Moreover, the paper explores government initiatives that promote auction-based resource allocation and assesses policy considerations and regulatory frameworks governing auctions in different sectors. Emphasis is placed on the importance of inclusive auction practices in promoting equitable outcomes and fostering financial inclusion. By showcasing international case studies of successful and challenging auctions, the study draws lessons for India's future auction initiatives. The research contributes to the understanding of effective auction-based resource allocation strategies and their potential impact on diverse economic landscapes. In conclusion, this comparative study provides valuable insights into the adaptation, effectiveness, and economic implications of auction theory applications, benefiting policymakers, businesses, and economists seeking to optimize resource allocation and market efficiency. The findings offer valuable guidance for shaping auction mechanisms and promoting fair market practices in both Indian and global contexts. As the study enhances the understanding of auction theory's applications, it contributes to evidence-based decision-making, fostering efficient resource allocation and economic growth.

A truthful dynamic combinatorial double auction model for cloud resource allocation

Dynamic auction-based resource allocation models require little global price information, are decentralized and suitable for the distributed systems like cloud computing. For the cloud computing market, we proposed a Truthful Dynamic Combinatorial Double Auction (TDCDA) model to improve the social welfare and resource utilization. In our model, multiple cloud service providers and cloud users bid for various resources in a dynamic environment. We adopted a payment scheme to ensure truthfulness for all participants, which motivates bidders to reveal their true preferences. Since the combinatorial auction allocation with goal of economic efficiency is NP-hard, we developed a greedy mechanism to achieve the approximately efficient solution. Considering both parties’ interests and the resource scarcity, this model also ensures fairness and balances resource allocation. The proposed model is proven to be approximately efficient, incentive compatible, individually rational and budget-balanced. Simulation results show that the model not only achieves economic efficiency, but also improves resource allocation and meets resource needs for more cloud users.

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.

Resource Provision for Cloud-Enabled Automotive Vehicles With a Hierarchical Model

Cloud computing is an emerging paradigm to enable computation and data-intensive automotive systems for improved safety and drivability. In this article, we propose a hierarchical, decentralized, and auction-based resource allocation model for cloud-enabled automotive vehicles. In this model, cloud-enabled vehicles bid for resources at a high level, inducing a multiplayer game; at a low level, each vehicle performs an onboard resource optimization to allocate its obtained resources to its cloud-based applications. The Nash equilibrium of the induced game is defined, and we show the existence and uniqueness of the equilibrium. A constrained optimization problem is solved for onboard resource allocation. A distributed update mechanism is considered: asynchronized update where only a subset of vehicles updates their bid at each iteration. This mechanism shares desired features of requiring little communication and being secure. Convergence to Nash equilibrium is proved for the proposed update mechanism. Furthermore, the robustness to stochastic task arrival rate is characterized in terms of total variance distance. Numerical simulations are presented to demonstrate the efficacy of the proposed framework.

Multi-round auction-based resource allocation for edge computing: Maximizing social welfare

5G NR-Lite technology still suffers from inadequate response to low latency, high speed and mass connectivity scenarios in high mobility conditions. Building on 4G and 5G, 6G networks will work to more fully enable the Internet of Things (IoT) paradigm, working to provide secure wireless computing for the digital world. In order to ensure the resource utilization of edge computing, it is usually necessary to allocate resources among mobile devices. Many scholars have applied auction theory to resource allocation in mobile edge networks and achieved specific results in recent years. However, most existing studies unilaterally consider the utility of users or edge servers, and few jointly consider social welfare and user selection priorities, which cannot maximize the overall utility of mobile edge networks. This paper proposes a multi-round auction algorithm that pursues maximizing social welfare in a multi-user–multi-server network. The algorithm combines combinatorial auction with double auction, allowing users and servers to adjust their bids and asking prices in each auction round. To enhance the generalizability of the study, the algorithm takes full advantage of the flexibility of the auction mechanism and considers the users’ demand for different servers, the variability of bidding strategies, and the variability of the total resources of edge servers so that the users can obtain resources with higher priority and improve social welfare. To ensure that social welfare is maximized, we adopt the Vickrey–Clarke–Groves (VCG) payment mechanism and consider the effect of network externalities. Extensive comparative experiments show that the algorithm can effectively improve social welfare and maximize the overall benefits of the mobile edge network.

BARA: A blockchain-aided auction-based resource allocation in edge computing enabled industrial internet of things

The recent emergence of Industrial Internet of Things (IIoT), a novel subset of Internet of Things (IoT), integrates sensors and intelligent devices with industry applications to develop a self-organizing system for creating enhanced and adaptive industrial environments. IIoT devices usually have limited computational power, while IIoT applications are mostly mission-critical and/or safety-critical. Therefore, these applications need computing resources that are closer to the devices. This work proposes a decentralized auction-based resource allocation mechanism in edge computing enabled IIoT using consortium blockchain and smart contract that shelves the involvement of a trusted third party, i.e. auctioneer. Various quality parameters are considered during resource allocation to address the mobility of both IIoT devices and edge resources, heterogeneity of edge servers, false assurance of edge servers, reliability, delay in results, and responsiveness of edge servers. Utility functions for IIoT devices are designed to calculate their degree of satisfaction depending on various quality parameters. The proposed blockchain-aided auction based mechanism fulfills various auction-based resource allocation requirements such as seal bidding, no impersonation of the bidder, no modification in any bid or result of allocation by the auctioneer, and proof for winners. The proposed work encourages edge servers for truthful bidding and furnishes the allocation results in polynomial time. Performance evaluation of the proposed model exhibits encouraging results.

Auction-Based Resource Allocation Mechanism in Federated Cloud Environment: TARA

The growing market of cloud computing resulted in increased demand for cloud resources and it will become difficult for individual service providers (SPs) to fulfill all resource requests. That leads to a situation where two or more SPs may form a group (federation) and share the resources in order to fulfill the cloud users’ demand and gain economic advantage. Now, due to the formation of more than one federations by different cloud providers, it may be difficult for users to select a suitable federation who can deliver cloud services at a fair price. In this context, it is necessary to have a framework that will efficiently allocate resources of cloud federations to the users at a fair price and stop market manipulation. In this article, we propose a multi-unit double auction mechanism called TARA ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> ruthful Double <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> uction for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> esource <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> llocation) that can be used to efficiently choose cloud federations for users from which they can get resources. Here, we consider a multi-seller and multi-buyer double auction mechanism for heterogeneous resources, where every buyer submits their bids and every seller places their ask (the price of a resource that is offered by a federation). TARA achieves some important properties like truthfulness (also known as incentive compatibility), individual rationality and budget balance for both buyers and sellers. TARA is also computationally efficient and posses high system efficiency. The simulation results also show that total utility of buyer is more than some existing double auction mechanisms.

Online Auction-Based Resource Allocation for Service-Oriented Network Slicing

Future mobile communication networks (5G) are envisioned to support a wide range of use cases with diverse service requirements. Network slicing as a key architectural enabling technology for 5G can provide users with customized services and meet the requirement of economic efficiency, where resource allocation is of vital importance. In this paper, we propose an online auction-based resource allocation scheme for network slicing (ORANS) to meet the diverse service requirements and improve network economic efficiency. We first propose a bidding information construction method to provide customized bidding information for users based on service types. Next, we model the slicing resource allocation problem as an online winner determination problem, with aim to maximize the social welfare of auction participants. We then implement a resource allocation strategy and payment rule to accomplish the service-oriented optimal resource allocation. Extensive simulation experiments are conducted to evaluate the performance of the proposed ORANS and the numerical results show that ORANS significantly outperforms the greedy algorithm and the primal-dual approximation algorithm in network economic efficiency while satisfying the diverse service requirements of users.

Double Auction-Based Resource Allocation for Mobile Edge Computing in Industrial Internet of Things

Mobile edge computing (MEC) yields significant paradigm shift in industrial Internet of things (IIoT), by bringing resource-rich data center near to the lightweight IIoT mobile devices (MDs). In MEC, resource allocation and network economics need to be jointly addressed to maximize system efficiency and incentivize price-driven agents, whereas this joint problem is under the locality constraints, i.e., an edge server can only serve multiple IIoT MDs in the vicinity constrained by its limited computing resource. In this paper, we investigate the joint problem of network economics and resource allocation in MEC where IIoT MDs request offloading with claimed bids and edge servers provide their limited computing service with ask prices. Particularly, we propose two double auction schemes with dynamic pricing in MEC, namely a breakeven-based double auction (BDA) and a more efficient dynamic pricing based double auction (DPDA), to determine the matched pairs between IIoT MDs and edge servers, as well as the pricing mechanisms for high system efficiency, under the locality constraints. Through theoretical analysis, both algorithms are proved to be budget-balanced, individual profit, system efficient, and truthful. Extensive simulations have been conducted to evaluate the performance of the proposed algorithms and the simulation results indicate that the proposed DPDA and BDA can significantly improve the system efficiency of MEC in IIoT.

Auction‐based resource allocation mechanisms in the cloud environments: A review of the literature and reflection on future challenges

SummaryCloud computing is an Internet‐based computing and networking model, with elasticity and scalability capabilities where the services are delivered to its users in a non‐demand style. In this computing paradigm, the request and response between users and providers must be managed using the resource allocation strategies. Therefore, allocating the provided resources to the users based on their needs is the important challenge in this environment. Also, an auction in the cloud is a process of buying and vending the cloud services by offering them up for bid and then selling the service to the highest bidder. However, to the best of our knowledge, there has not been any comprehensive and detailed paper about reviewing the state‐of‐the‐art mechanisms on this important topic and providing open issues as well. Hence, this paper provides a comprehensive survey and review of the auction‐based resource allocation mechanisms, which have been employed in the cloud environments up to now. Also, we classified the important cloud resource allocation mechanisms into four categories: one‐sided, double‐sided, combinatorial, and other types of auction‐based mechanisms. Moreover, we reviewed the main progress in these four categories and defined the new issues. Finally, the paper offers the differences among reviewed mechanisms as well as guidelines for future investigation.

Auction-Based Resource Allocation for Sharing Cloudlets in Mobile Cloud Computing

Driven by pervasive mobile devices and ubiquitous wireless communication networks, mobile cloud computing emerges as an appealing paradigm to accommodate demands for running power-hungry or computation-intensive applications over resource-constrained mobile devices. Cloudlets that move available resources closer to the network edge offer a promising architecture to support real-time applications, such as online gaming and speech recognition. To stimulate service provisioning by cloudlets, it is essential to design an incentive mechanism that charges mobile devices and rewards cloudlets. Although auction has been considered as a promising form for incentive, it is challenging to design an auction mechanism that holds certain desirable properties for the cloudlet scenario. In this paper, we propose an incentive-compatible auction mechanism (ICAM) for the resource trading between the mobile devices as service users (buyers) and cloudlets as service providers (sellers). ICAM can effectively allocate cloudlets to satisfy the service demands of mobile devices and determine the pricing. Both the theoretical analysis and the numerical results show that the ICAM guarantees desired properties with respect to individual rationality, budget balance and truthfulness (incentive compatibility) for both the buyers and the sellers, and computational efficiency.

Combinatorial double auction-based resource allocation mechanism in cloud computing market

The cloud computing environment may be considered as market for computing and storage resources. Providers rent their available resources in the form of Virtual Machines (VM) and charge the users accordingly. One of the challenges in this market is providing a mechanism for the allocation of resources and their pricing, such that the proper benefit of both users and providers are guaranteed. In this paper, a combinatorial double auction-based market is studied in which a broker performs the allocation of the providers’ VMs according to the users’ requests. The proposed allocation problem is formulated as an integer linear programming model aiming at maximizing the total profit of users and providers. It is proved that the proposed model satisfies the desirable properties including: truthfulness, fairness, economic efficiency and allocation efficiency. Furthermore, due to the high complexity of the proposed model, a heuristic resource allocation algorithm with a quasi linear time complexity is presented. The results of evaluations confirm the good agreement of the heuristic algorithm with the optimization model in terms of allocation performance. Moreover, simulation results using CloudSim indicate that, compared to the previous works in literature, the proposed algorithm increases the profit of providers and users and reduces the resource wastage.

Auction-based resource allocation in OpenFlow multi-tenant networks

In this paper, we investigate the allocation of network resources (such as FlowTable entries and bandwidth) in multi-tenant Software-Defined Networks (SDNs) that are managed by a FlowVisor. This resource allocation problem is modeled as an auction where the FlowVisor acts as the auctioneer and the network Controllers act as the bidders. The problem is analyzed by means of non-cooperative game theory, and it is shown that the auction admits a unique Nash Equilibrium (NE) under suitable conditions. Furthermore, a novel distributed learning procedure is provided that allows each Controller to reach the game’s unique NE in a few iterations by exploiting only locally available information. An implementation in OpenFlow-compliant SDNs is also proposed in a way that exploits native procedures already offered by OpenFlow. Finally, simulation results show that the proposed auction-based resource management scheme leads to significant improvements in network performance (for instance, achieving gains of up to 5 × reduction in transmission delays).

Efficient mobile cloud service allocation for mobile commerce

This paper presents efficient mobile cloud service allocation for mobile commerce in mobile cloud. Mobile cloud service allocation scheme considers both mobile clients and cloud datacentre providers. The mobile commerce service scenario is taken as an example in mobile cloud environment, and our service allocation approach is applied to mobile cloud environment. The paper proposes a distributed mobile cloud service allocation algorithm for mobile commerce (SAAMC). From the simulation results, the execution success ratio of our SAAMC is better than that of game-theoretic mobile cloud resource allocation (GMCRA) (Ge et al., 2012) and auction-based resource allocation algorithm (ARAA) (Zhang et al., 2013). The allocation efficiency of SAAMC is better than that of GMCRA and ARAA. The energy consumption ratio of SAAMC is not good as that of GMCRA.

ARAM: A New Auction-based Resource Allocation Model in Cloud Computing

Cloud computing is an on-demand network access model to a shared pool of configurable computing resources. One of the main challenges in cloud computing is the efficiency of the pricing and resource allocation models adopted by cloud providers. In this paper, we proposed a new resource allocation model called ARAM, which is based on reverse auctions and provides dynamic pricing policies. In this model, independent reverse auctions are hold for each cloud service request. ARAM has been compared with other previously proposed approaches, considering several issues such as the types of the services it supports, the necessity for configuring service instances, and also those related to load balancing. The main advantage of ARAM, compared to previous forward auction based models, is that it eliminates the necessity for configuring service instances. Therefore, the utilities of both consumers and providers are enhanced, in terms of cost and time. Also by adopting a dynamic pricing approach, it moderates the shortcomings of fixed pricing strategies in cloud computing, such as resource wastage and lack of fairness. Furthermore, contrary to most of the previous approaches, ARAM supports dynamic pricing for all types of services and considers load balancing issues in allocating resources. Today, most of the providers in the industry use fixed pricing models ignoring the market condition. Therefore, it seems necessary to use efficient dynamic pricing and resource allocation models, such as ARAM, in cloud computing.

Auction-based resource allocation for cooperative cognitive radio networks

Cooperative cognitive radio network (CCRN) is a promising paradigm to increase spectrum utilization and exploit spatial diversity. The allocation of two coupled resources, i.e. spectrum and secondary relay nodes, plays a fundamental role in the performance of CCRNs. However, previous studies either lack of incentives for both primary users (PUs) and relay nodes to participate in or consider spectrum auction and relay auction separately. In this paper, we consider a static cooperative cognitive radio network scenario with several PUs and multiple secondary user coteries, each of which consists of a set of secondary users who are interested in sharing the same secondary relay node. We model the problem of joint spectrum allocation and relay allocation as a hierarchical auction and propose two auction schemes, i.e. TERA and UERA. TERA is the first Truthful auction mechanism for Efficient Resource Allocation in CCRNs which satisfies critical economic properties. UERA is an approximate truthful scheme which adopts a uniform price for fairness in the same secondary user coteries. Furthermore, we theoretically prove TERA and UERA can achieve near-optimal revenue. Finally, extensive simulation results show that TERA and UERA are efficient and able to improve the utility of PUs and relay nodes significantly.

Online Auction-Based Resource Allocation in Grids: Model and Protocols

Online Auction-Based Resource Allocation in Grids: Model and Protocols