Auction Framework Research Articles

Should underwriters be trusted? Reducing agency costs through primary market supervision

We examine the mandated introduction of a supervised auction framework within China's primary bond market. This regulatory intervention resulted in a substantial reduction in debt costs for Chinese Bond issuers, primarily attributed to the alleviation of agency conflicts between underwriters and issuers. Leveraging unique bidder-level data obtained from a lead underwriter, our study provides replicable tools to discern collusive bidding behavior. This sheds light on instances of artificially inflated bond yields, highlighting economically burdensome practices. Our findings can assist regulators, issuers, and investors engaged in unsupervised auction mechanisms. Our insights offer potential enhancements to regulatory frameworks and operational efficiency across various sectors, including securities issuance, construction projects, and procurement.

Perbandingan Hukum Lelang Non Eksekusi Sukarela pada E-Market Place Auction antara Indonesia dengan Amerika Serikat

This research aims to compare non-executive voluntary auction laws on e-marketplaces between Indonesia and the United States. In Indonesia, the regulatory framework for non-executive voluntary auctions falls under the jurisdiction of the Ministry of Finance, focusing on transparency, consumer protection, and fairness in the auction process. The study highlights that despite Indonesia's adoption of digital technology to enhance auction accessibility and efficiency, regulations are still evolving to keep pace with technological advancements and market developments. Meanwhile, in the United States, non-executive voluntary auctions are detailed under federal and state laws, emphasizing consumer protection, transaction security, and robust enforcement to prevent harmful business practices. These stringent regulations encompass participant verification, bidding processes, and transaction settlement to ensure fairness and safety for all involved parties. The findings of this research indicate that differences in legal regulations, implementation, and approaches between Indonesia and the United States influence how non-executive voluntary auctions are conducted and governed in both countries. This comparison provides deep insights into the challenges and opportunities in developing legal policies that support digital economic growth in this era of globalization.

A Multi-Dimensional Reverse Auction Mechanism for Volatile Federated Learning in the Mobile Edge Computing Systems

Federated learning (FL) can break the problem of data silos and allow multiple data owners to collaboratively train shared machine learning models without disclosing local data in mobile edge computing. However, how to incentivize these clients to actively participate in training and ensure efficient convergence and high test accuracy of the model has become an important issue. Traditional methods often use a reverse auction framework but ignore the consideration of client volatility. This paper proposes a multi-dimensional reverse auction mechanism (MRATR) that considers the uncertainty of client training time and reputation. First, we introduce reputation to objectively reflect the data quality and training stability of the client. Then, we transform the goal of maximizing social welfare into an optimization problem, which is proven to be NP-hard. Then, we propose a multi-dimensional auction mechanism MRATR that can find the optimal client selection and task allocation strategy considering clients’ volatility and data quality differences. The computational complexity of this mechanism is polynomial, which can promote the rapid convergence of FL task models while ensuring near-optimal social welfare maximization and achieving high test accuracy. Finally, the effectiveness of this mechanism is verified through simulation experiments. Compared with a series of other mechanisms, the MRATR mechanism has faster convergence speed and higher testing accuracy on both the CIFAR-10 and IMAGE-100 datasets.

A flexible combinatorial auction bidding language for supplier selection and order allocation in a supply chain with price sensitive demand

This study addresses the multi-item multi-sourcing supplier selection and order allocation problem in a Combinatorial Auction (CA) framework. First, we propose a new flexible procurement CA bidding language, then prove that it can convey the same information as existing bidding languages while allowing more efficient auction outcomes and reduced computational complexity. Suppliers submit combinatorial bids to a single manufacturer. Each bid carries information about the supplier’s cost structure and potential discounts. The manufacturer purchases the items and assembles one or more product types that experience price-sensitive demand rates by end consumers. We use the realistic logit function to represent the price-sensitive demand rates of finished products. A Mixed Integer Non-Linear Programming (MINLP) model is developed to help the manufacturer make optimal supplier selection, order allocation, and pricing decisions under the proposed bidding language. The MINLP model considers purchasing, transportation, ordering, administrative, holding, and manufacturing costs. These considerations make the results of this study realistic with the potential to be used in practical applications. Also, we derive a set of necessary conditions that must exist in at least one optimal solution to the problem. These conditions can be utilized to design efficient solution mechanisms for this difficult problem in the future. Finally, an extensive numerical analysis is performed to illustrate the bidding language and the key results.

A truthful double auction framework for security-driven and deadline-aware task offloading in fog-cloud environment

Emerging as an alternative to cloud computing, fog computing is expected to fulfill the resource demand of mobile users and provide low-latency and high-bandwidth services for ever-increasing Internet of Things (IoT) applications. Offloading time-critical and computation-intensive tasks to the fog can significantly reduce response time. Due to dynamicity, heterogeneity, and limited resources, fog may fail to provide services with guaranteed performance. This paper addresses the problem of task offloading and resource allocation in a fog-cloud environment for IoT applications. The proposed Truthful Double Auction Task Offloading (TDATO) framework provides incentives for fog providers to maximize their revenues, execute as many as possible requests with guaranteed performance, and maintain a high reputation. Depending on the user requirements in terms of security level and maximum latency tolerance for task execution, we propose two deadline- and security-related algorithms for winners' determination in the auction: Deadline-Minimum Security (DMS) and Deadline-Preferred Security (DPS). Moreover, two assignment algorithms are analyzed, Reputation-Based (RB) and Random Assignment (RA). The proposed double auction framework assures that all winning users' requirements in terms of deadlines and security level are satisfied while winning providers’ resources are allocated to the users that value them the most. Furthermore, the assignment mechanism aims to maximize resource utilization for fog providers with the highest reputation. We conducted a theoretical analysis to demonstrate that TDATO satisfies truthfulness, individual rationality, and budget balance and has polynomial-time computation complexity. Extensive simulation experiments are performed to evaluate performance.

A Truthful Combinatorial Reverse Auction Mechanism for Crowdshipping

Crowdshipping is an emerging logistics paradigm that leverages excess capacities of occasional couriers to transport goods. This paper investigates the problem that a crowdshipping platform recruits occasional couriers to serve the parcel pickup and delivery requests. A combinatorial reverse auction framework is introduced to model the interactions between the platform and couriers. We design an auction mechanism that implements in polynomial time, which satisfies the computational requirements of crowdshipping. It can also achieve near-optimal social cost with the approximation ratio rigidly proved. Through both rigorous theoretical analyses and extensive simulations, we demonstrate that the proposed auction mechanism is truthful, individual rational and has high computation efficiency.

An Incentive Auction for Heterogeneous Client Selection in Federated Learning

Federated Learning (FL) is a new distributed machine learning (ML) approach which enables thousands of mobile devices to collaboratively train artificial intelligence (AI) models using local data without compromising user privacy. Although FL represents a promising computing paradigm, such training process can not be fully realized without an appropriate economic mechanism that incentivizes the participation of heterogeneous clients. This work targets social cost minimization, and studies the incentive mechanism design in FL through a procurement auction. Different from existing literature, we consider a practical scenario of FL where clients are selected and scheduled at different global iterations to guarantee the completion of the FL job, and capture the distinct feature of FL that the number of global iterations is determined by the local accuracy of all participants to balance between computation and communication. Our auction framework <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> first decomposes the social cost minimization problem into a series of winner determination problems (WDPs) based on the number of global iterations. To solve each WDP, <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> invokes a greedy algorithm to determine the winners, and a payment algorithm for computing remuneration to winners. Finally, <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> returns the best solution among all WDPs. We carried out theoretical analysis to prove that <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> is truthful, individual rational, computationally efficient, and achieves a near-optimal social cost. We further extend our model to consider multiple FL jobs with corresponding budgets and propose another efficient algorithm <inline-formula><tex-math notation="LaTeX">$A_{FL-M}$</tex-math></inline-formula> to solve the extended problem. We conduct large-scale simulations based on the real-world data and testbed experiments by adopting FL frameworks FAVOR and CoCoA. Simulation and experiment results show that both <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$A_{FL-M}$</tex-math></inline-formula> can reduce the social cost by up to 55&#x0025; compared with state-of-the-art algorithms.

Using Analytical Hierarchy Process for Double Auction to Optimize Financial Performance of Private Higher Education Institutions

Internal factors and external factors are used to measure the performance of higher education institutions. Internal factors include those whose criteria come from within the organization, while external factors are those whose criteria come from outside the organization. Of these two factors, measurement criteria that can help in decision-making about the development and expansion of higher education competence, including financial criteria, are needed. This study examines the optimization of the financial performance of private higher education institutions by applying the Analytic Hierarchy Process method and a proposed double auction framework. Financial criteria are critical to supporting the development and enhancing the competency of higher education institutions. The Analytical Hierarchy Process is used to prioritize 19 sub-criteria, with the Teaching Process Operational Cost Development sub-criterion scoring highest for each operational criterion. The double auction framework is proposed to facilitate decision-making for the development and further competence of private higher education institutions. This paper represents an important contribution to the evaluation of the financial performance of private higher education institutions by using a novel approach that incorporates a double auction framework.

Carbon allowance auction design of China's ETS: A comprehensive hierarchical system based on blockchain

This study proposes a blockchain-based optimization framework for carbon allowance allocation auctions. First, the study assesses the status quo of carbon trading, pinpointing barriers, future development trends, and identifying carbon quota auction standards and methods pertinent to the Chinese context. Second, based on China's carbon market needs and the technical characteristics of blockchain, this study reconfigures the functions and responsibilities of each participant and creates a framework for a carbon allowance auction system to optimize the carbon trading process. Third, an algorithm for the carbon allowance auction system is constructed, and the corresponding smart contract is created and simulated. The results illustrate that our proposed system framework facilitates universal carbon allowance auctions, efficiently addresses potential disputes during the auction process, enhances market transparency and credibility, and offers comprehensive privacy protection with flexible information disclosure options for corporations. Blockchain's role in fostering paid distribution and incentivizing participation in the carbon market is further recognized. A blockchain-based carbon trading system safeguards transaction security and efficiency, maintains precise transaction records, and provides automated settlement. Ultimately, this paper presents policy suggestions for the development of a national carbon market in China.

A Blockchain-Based Auction Framework for Location-Aware Services

As a critical factor in ensuring the growth of the electronic auction (e-auction) domain, the privacy and security of the participants (sellers and buyers) must always be guaranteed. Traditionally, auction data, including participant details, are stored in a third party (auctioneer) database. This leads to a high risk of a single point of failure in terms of privacy and security. Toward this end, blockchain technology has emerged as a promising decentralized communication paradigm to address such risks. This paper presents a blockchain-based auction framework as a decentralized e-auctioning framework for location-aware services. In particular, the framework consists of three components: pre-auctioning, main auctioning, and post-auctioning processes with four algorithms. Our primary focus is on location-aware services, such as storage space rental, apartment rental, transport hire, and parking space rental. The trading volumes are expected to be high; hence, simplifying the design is a crucial requirement. In addition to the benefits of eliminating the centralized entity (the auctioneer), fees are redistributed among participants as rewards. Further, we incorporate a service quality monitoring method that ranks the services provided by participants. This ranking allows participants to determine the rank of other participants with whom they wish to trade. We have conducted several experiments to evaluate the proposed framework’s cost feasibility and to ensure the ease of the business flow.

Rippling effect of liquidity risk in the sovereign term structure

PurposeThe study attempts to identify the linkages in the term structure of illiquidity and the impact of global and domestic factors on sovereign bonds in emerging Asia. The objective of the study ensues on defining the direction of illiquidity spillover across bonds of varying tenors.Design/methodology/approachThis study explores the joint dynamics of contemporary liquidity risk premia and its time-varying effect on the term structure spectrum using the Diebold and Yilmaz (2012) spillover framework.FindingsA substantial relationship was found to exist between the liquidity of bonds with closer terms to maturity. The macroeconomic environment primarily impacts the liquidity of 10-year bonds, and they spiral down to the subsequent bond liquidity, exhibiting a rippling effect. The authors further show that the direction of liquidity shock transmission is from long- to medium- and thence to short-term bonds. Among the global factors, foreign investments and S &amp; P 500 VIX significantly affect the liquidity of 10-year bonds.Research limitations/implicationsThe study has several implications for academicians, policymakers and domestic and global investment professionals. The drivers of liquidity risk and the transmission across the term structure help investors in designing efficient portfolio diversification strategies. The results are relevant for cross-border investors in the valuation of emerging Asian sovereign bonds while deciding on asset allocations and hedging strategies. The monetary regulators strive on a continuous basis to improve the liquidity in sovereign bond markets in order to ensure efficient funding of development activities. This study finds that short-term bonds are more liquid than long-term bonds. Their auction framework with higher series of short-term bond issues helps to provide the required liquidity in the markets.Practical implicationsThe term structure of illiquidity is upward sloping, inferring a higher underlying liquidity risk of long-term bonds compared to short-term bonds. This finding suggests that a higher representation of short-term bonds in the auction framework helps to enhance the overall market liquidity.Originality/valueThis study offers insights into the debate on the shape of the term structure of illiquidity and the point of origination of liquidity shocks. Further, the direction of spillover across a wide spectrum of bonds is also demonstrated.

A blockchain-based privacy-preserving and collusion-resistant scheme (PPCR) for double auctions

Electronic auctions (e-auctions) remove the physical limitations of traditional auctions and bring this mechanism to the general public. However, most e-auction schemes involve a trusted auctioneer, which is not always credible in practice. Some studies have applied cryptography tools to solve this problem by distributing trust, but they ignore the existence of collusion. In this paper, a blockchain-based Privacy-Preserving and Collusion-Resistant scheme (PPCR) for double auctions is proposed by employing both cryptography and blockchain technology, which is the first decentralized and collusion-resistant double auction scheme that guarantees bidder anonymity and bid privacy. A two-server-based auction framework is designed to support off-chain allocation with privacy preservation and on-chain dispute resolution for collusion resistance. A Dispute Resolution agreement (DR) is provided to the auctioneer to prove that they have conducted the auction correctly and the result is fair and correct. In addition, a Concise Dispute Resolution protocol (CDR) is designed to handle situations where the number of accused winners is small, significantly reducing the computation cost of dispute resolution. Extensive experimental results confirm that PPCR can indeed achieve efficient collusion resistance and verifiability of auction results with low on-chain and off-chain computational overhead.

Theoretical design of decentralized auction framework under mobile crowdsourcing environment

With the rapid popularization of mobile devices, the mobile crowdsourcing has become a hot topic in order to make full use of the resources of mobile devices. To achieve this goal, it is necessary to design an excellent incentive mechanism to encourage more mobile users to actively undertake crowdsourcing tasks, so as to achieve maximization of certain economic indicators. However, most of the reported incentive mechanisms in the existing literature adopt a centralized platform, which collects the bidding information from workers and task requesters. There is a risk of privacy exposure. In this paper, we design a decentralized auction framework where mobile workers are sellers and task requesters are buyers. This requires each participant to make its own local and independent decision, thereby avoiding centralized processing of task allocation and pricing. Both of them aim to maximize their utilities under the budget constraint. We theoretically prove that our proposed framework is individual rational, budget balanced, truthful, and computationally efficient, and then we conduct a group of numerical simulations to demonstrate its correctness and effectiveness.

Timeliness-Aware Incentive Mechanism for Vehicular Crowdsourcing in Smart Cities

Vehicular crowdsourcing is a promising paradigm that takes advantage of powerful onboard capabilities of vehicles to perform various tasks in smart cities. To fulfill this vision, a well-designed incentive mechanism is essential to stimulate the participation of vehicles. In this paper, we propose a timeliness-aware incentive mechanism for vehicular crowdsourcing by taking vehicle’s uncertain travel time into account. In view of the stochastic nature of traffic conditions, we derive a tractable expression for the probability distribution of task delay based on a discrete-time traffic model. By leveraging reverse auction framework, we model the utility of a service requester as a function in terms of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">uncertain</i> task delay and incurred payment. To maximize the requester’s utility under a budget constraint, we cast the mechanism design as a non-monotone submodular maximization problem over a knapsack constraint. Based on this formulation, we develop a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</u> ruthful <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</u> udgeted <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">u</u> tility <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</u> aximization <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> uction (TBUMA), which is truthful, budget feasible, profitable, individually rational and computationally efficient. Through extensive trace-based simulations, we demonstrate the effectiveness of our proposed incentive mechanism.

An auction framework for assessing the tendering of subsidised routes in air transportation

Governments offer subsidies along routes that are deemed commercially non-viable but economically and socially essential. Subsidised routes are often criticised for inefficiencies and excessive subsidies, which partly result from restrictions defined by transportation authorities during the tendering process, such as a maximum airfare and minimum number of daily flights. We develop an integrated auction framework—referred to as Single-round Combinatorial Auction for Subsidised routes (SCAS) —to provide decision support to transportation authorities when designing tendering processes for subsidised routes. The framework includes two main models as ingredients. First, the Airline Bid Preparation Model (ABPM), which replicates the airline’s behaviour when preparing bids for subsidised routes. Second, the Winner Determination Problem (WDP), which is used to select the bids based on a given evaluation criterion. We capture the responsive relationship between passenger demand and supply of air services by including passenger utility as an endogenous variable in the ABPM. Additionally, as input to the ABPM, we estimate the route operating cost for small aircraft that typically operate subsidised routes. The usefulness of the approach is demonstrated with an application to the network of subsidised routes in Sweden, for which we provide policy guidelines. Our analysis suggests that having a restriction on the airfare but not the number of flights is an effective way to design the tendering process, which strikes a good balance between passengers, government and airlines goals. Additionally, we demonstrate that the transportation authorities can compensate not having a requirement on the number of daily flights through ensuring a higher number of passengers, i.e., by including maximisation of the number of passengers in the bid evaluation criterion or using passenger discounts. • An integrated auction framework for the tendering of subsidised routes. • Policy guidelines for the tendering of subsidised routes. • A restriction on the airfare but not the number of flights strikes a good balance between passenger surplus, airline profits and goverment’s cost of subsidisation. • Serve more passengers by changing the bid evaluation criteria or using passenger discounts.

Auction-based profit maximization offloading in mobile edge computing

Offloading Mobile Devices (MDs) computation tasks to Edge Nodes (ENs) is a promising solution to overcome computation and energy resources limitations of MDs. However, there exists an unreasonable profit allocation problem between MDs and ENs caused by the excessive concern on MD profit. In this paper, we propose an auction-based computation offloading algorithm, inspiring ENs to provide high-quality service by maximizing the profit of ENs. Firstly, a novel cooperation auction framework is designed to avoid overall profit damage of ENs, which is derived from the high computation delay at the overloaded ENs. Thereafter, the bidding willingness of each MD in every round of auction is determined to ensure MD rationality. Furthermore, we put forward a payment rule for the pre-selected winner to effectively guarantee auction truthfulness. Finally, the auction-based profit maximization offloading algorithm is proposed, and the MD is allowed to occupy the computation and spectrum resources of the EN for offloading if it wins the auction. Numerical results verify the performance of the proposed algorithm. Compared with the VA algorithm, the ENs profit is increased by 23.8%, and the task discard ratio is decreased by 7.5%.

AFSOS: An Auction Framework and Stackelberg Game Oriented Optimal Network’s Resource Selection Technique in Cognitive Radio Networks

The upcoming 5G and beyond wireless networking technologies are intended to coexist with different communication networks to solve the spectrum scarcity problem. The seamless roaming across integrated wireless networks and an optimal network&#x2019;s resource allocation would be a doable challenge for designing futuristic networks. The cognitive radio (CR) networks, with the inclusion of game theory, can handle spectrum scarcity issues by permitting spectrum access to the secondary user (SU) in the absence of a primary user (PU). This work proposes an AFSOS (an Auction Framework and Stackelberg game oriented Optimal network&#x2019;s resource Selection) technique to achieve a maximum payoff and acceptable interference power restraint in CR networks. The contest among candidate networks can be modeled as a Stackelberg game, and the contest between CR nodes and candidate networks can be modeled as an auction framework. As a schema for the realization and capabilities evaluations, the CR networks consisting of LTE cellular network interworking with Wi-Fi network for multiple access points (APs)/single vehicle (low traffic environment) is analyzed. This framework is further analyzed for single AP/multiple CR nodes (high traffic environment). The equilibrium for the Stackelberg game is achieved, and the proposed AFSOS technique is analyzed with numerical examples. The results show that the proposed AFSOS technique is more operative to obtain maximum payoff.

A cloud-based framework for verifiable privacy-preserving spectrum auction

Spectrum auction is one of the most effective ways to achieve dynamic spectrum allocation in cognitive radio networks, and it provides one effective way to manage the spectrum demands of IoT devices with limited resources. Most spectrum auctions focus on protecting bidder privacy and achieving excellent social efficiency, but few tackles the verification of auction results that are controlled by the auctioneer. In this paper, we propose a cloud-based framework for verifiable privacy-preserving spectrum auctions. Our framework adopts a modified AFGH re-encryption algorithm that achieves both bid privacy protection and auction results verification at the same time. The cloud server helps to compute auction results based on homomorphic encryption, and an auctioneer decrypts the encrypted data from the server to obtain auction results. Meanwhile, the property of re-encryption makes it possible for any bidder to verify the auction results without compromising other bidders’ privacy.

A Quality Aware Multiunit Double Auction Framework for IoT-Based Mobile Crowdsensing in Strategic Setting

Crowdsourcing with the intelligent agents carrying smart devices is becoming increasingly popular in recent years. It has opened up meeting an extensive list of real-life applications such as measuring air pollution levels, road traffic information, etc. In literature, this is known as <i>mobile crowdsourcing</i> or <i>mobile crowdsensing</i>. In this paper, the discussed set-up consists of multiple task requesters (or task providers) and multiple IoT devices (as <i>task executors</i>), where each of the task providers has multiple homogeneous sensing tasks. Each task requester reports a bid and the number of homogeneous sensing tasks to the platform. On the other side, multiple IoT devices report the ask (the charge for imparting its services) and the number of sensing tasks they can execute. The valuations of task requesters and IoT devices are <i>private</i> information, and both might act <i>strategically</i>. One assumption that is made in this paper is that the bids and asks of the agents (<i>task providers</i> and <i>IoT devices</i>) follow <i>decreasing marginal returns</i> criteria. Given the above-discussed scenario, the objectives are: (1) to determine a set of quality IoT devices for each of the tasks held by the task requesters, and (2) to select a subset of quality IoT devices from among the available quality IoT devices for each of the sensing tasks. In this paper, a truthful mechanism is proposed for allocating the IoT devices to the sensing tasks carried by task requesters that also keep into account the quality of IoT devices. Through theoretical analysis, it is shown that the mechanism is <i>truthful</i>, <i>budget balanced</i>, <i>individually rational</i>, <i>computationally efficient</i>, <i>correct</i>, and <i>prior-free</i>. Further, probabilistic analysis is carried out to estimate the average number of tasks that get executed for any task requester. The simulations are carried out to measure the performance of the proposed mechanism against the benchmark mechanisms on the ground of <i>truthfulness</i>, <i>budget balance</i>, <i>satisfaction level</i>, <i>average incentive</i>, and <i>computational efficiency</i>.

Rebate Auction Mechanisms for Bidirectional Grid Balancing Using Cloud Workload Migrations

Increasing the power-grid&#x2019;s flexibility is essential for expanding the integration of renewable energy sources in modern power grids. This work presents a new rebate auction framework that allows power grids to use cloud datacenters as managed loads to provide upward/downward grid flexibility. Since the energy consumption of datacenters is proportional to their computational workload, this paper presents a rebate auction framework that can induce cloud workload migrations between datacenters to correct energy imbalances. Unlike existing datacenter-based power grid balancing approaches that have only focused on providing downward flexibility and only considered owner-operated datacenters, the proposed framework provides bidirectional flexibility and accommodates both owner-operated and public cloud datacenters. Thus, providing a general framework for creating workload migrations between datacenters to balance the power grid. Because workloads on public cloud datacenters are managed by end-users, the proposed framework uses monetary incentives (auctioned rebates) to encourage large-scale end-users to migrate their cloud workloads between datacenters to correct energy imbalances. The use of monetary rewards as an incentive hides the complexity of grid-balancing from auction participants, who only participate in the auction to lower their cost, while grid-balancing happens as a result of workload migrations. This paper presents and compares two auction implementations under the proposed framework, a strategy-proof implementation that guarantees truthful bidding as a dominant strategy, but has NP-hard computational complexity, and an alternative implementation that does not guarantee truthful bidding, but has polynomial time complexity. Simulation results show that the proposed framework is effective in incentivizing cloud workload migrations to achieve the grid balancing goal and provides positive utility to all participants.