An improved genetic algorithm is proposed to solve the problem of bad real-time performance or inability to get a global optimal/better solution when applying single-item auction （SIA） method or combinatorial auctio...An improved genetic algorithm is proposed to solve the problem of bad real-time performance or inability to get a global optimal/better solution when applying single-item auction （SIA） method or combinatorial auction method to multi-robot task allocation. The genetic algorithm based combinatorial auction （GACA） method which combines the basic-genetic algorithm with a new concept of ringed chromosome is used to solve the winner determination problem （WDP） of combinatorial auction. The simulation experiments are conducted in OpenSim, a multi-robot simulator. The results show that GACA can get a satisfying solution in a reasonable shot time, and compared with SIA or parthenogenesis algorithm combinatorial auction （PGACA） method, it is the simplest and has higher search efficiency, also, GACA can get a global better/optimal solution and satisfy the high real-time requirement of multi-robot task allocation.展开更多
Cloud computing is a demanding business platform for services related to the field of IT.The goal of cloud customers is to access resources at a sustainable price,while the goal of cloud suppliers is to maximize their...Cloud computing is a demanding business platform for services related to the field of IT.The goal of cloud customers is to access resources at a sustainable price,while the goal of cloud suppliers is to maximize their services utilization.Previously,the customers would bid for every single resource type,which was a limitation of cloud resources allocation.To solve these issues,researchers have focused on a combinatorial auction in which the resources are offered by the providers in bundles so that the user bids for their required bundle.Still,in this allocation mechanism,some drawbacks need to be tackled,such as due to the lower average bid price the users are dropped from the auction process.To solve this problem,we proposed a“Negotiation based Combinatorial Double Auction Mechanism for Resource Allocation(N-CDARA)in cloud computing”.The proposed method negotiates with dropped users.Lower average bid price users are asked by our proposed mechanism to increase their bids,as by the quoted bids they will be dropped by the auctioneer.Most of the users that are close to winning accept the proposal and increase their bid prices.The proposed mechanism is implemented in a CloudSim simulation toolkit.Results are compared with the latest model and performance study shows that in our proposed scheme more users win and get their requested services and the utilization of offered services is increased up to 18.4%than the existing schemes.展开更多
With the emergence of ambient sensing technologies which combine mobile crowdsensing and Internet of Things,large amount of people-centric data can be obtained and utilized to build people-centric services.Note that t...With the emergence of ambient sensing technologies which combine mobile crowdsensing and Internet of Things,large amount of people-centric data can be obtained and utilized to build people-centric services.Note that the service quality is highly related to the privacy level of the data.In this paper,we investigate the problem of privacy-aware service subscription in people-centric sensing.An efficient resource allocation framework using a combinatorial auction(CA)model is provided.Specifically,the resource allocation problem that maximizes the social welfare in view of varying requirements of multiple users is formulated,and it is solved by a proposed computationally tractable solution algorithm.Furthermore,the prices of allocated resources that winners need to pay are figured out by a designed scheme.Numerical results demonstrate the effectiveness of the proposed scheme.展开更多
Auctions are important market mechanisms for the allocation of goods and services. Combinatorial auctions are those auctions in which buyers can place bids on combinations of items. Combinatorial auctions have many ap...Auctions are important market mechanisms for the allocation of goods and services. Combinatorial auctions are those auctions in which buyers can place bids on combinations of items. Combinatorial auctions have many applications. The paper presents the CRAB software system. CRAB is a non-commercial software system for generating, solving, and testing of combinatorial auction problems. The system solves problems by Balas’ method or by the primal-dual algo-rithm. CRAB is implemented in Ruby and it is distributed as the file crab.rb. The system is freely available on web pag-es for all interested展开更多
The complexity of cloud environments challenges secure resource management,especially for intrusion detection systems(IDS).Existing strategies struggle to balance efficiency,cost fairness,and threat resilience.This pa...The complexity of cloud environments challenges secure resource management,especially for intrusion detection systems(IDS).Existing strategies struggle to balance efficiency,cost fairness,and threat resilience.This paper proposes an innovative approach to managing cloud resources through the integration of a genetic algorithm(GA)with a“double auction”method.This approach seeks to enhance security and efficiency by aligning buyers and sellers within an intelligent market framework.It guarantees equitable pricing while utilizing resources efficiently and optimizing advantages for all stakeholders.The GA functions as an intelligent search mechanism that identifies optimal combinations of bids from users and suppliers,addressing issues arising from the intricacies of cloud systems.Analyses proved that our method surpasses previous strategies,particularly in terms of price accuracy,speed,and the capacity to manage large-scale activities,critical factors for real-time cybersecurity systems,such as IDS.Our research integrates artificial intelligence-inspired evolutionary algorithms with market-driven methods to develop intelligent resource management systems that are secure,scalable,and adaptable to evolving risks,such as process innovation.展开更多
Winner determination is one of the main challenges in combinatorial auctions. However, not much work has been done to solve this problem in the case of reverse auctions using evolutionary techniques. This has motivate...Winner determination is one of the main challenges in combinatorial auctions. However, not much work has been done to solve this problem in the case of reverse auctions using evolutionary techniques. This has motivated us to propose an improvement of a genetic algorithm based method, we have previously proposed, to address two important issues in the context of combinatorial reverse auctions: determining the winner(s) in a reasonable processing time, and reducing the procurement cost. In order to evaluate the performance of our proposed method in practice, we conduct several experiments on combinatorial reverse auctions instances. The results we report in this paper clearly demonstrate the efficiency of our new method in terms of processing time and procurement cost.展开更多
The option of organizing E-auctions to purchase electricity required for anticipated peak load period is a new one for utility companies.To meet the extra demand load,we develop electricity combinatorial reverse aucti...The option of organizing E-auctions to purchase electricity required for anticipated peak load period is a new one for utility companies.To meet the extra demand load,we develop electricity combinatorial reverse auction(CRA)for the purpose of procuring power from diverse energy sources.In this new,smart electricity market,suppliers of different scales can participate,and homeowners may even take an active role.In our CRA,an item,which is subject to several trading constraints,denotes a time slot that has two conflicting attributes,electricity quantity and price.To secure electricity,we design our auction with two bidding rounds:round one is exclusively for variable energy,and round two allows storage and nonintermittent renewable energy to bid on the remaining items.Our electricity auction leads to a complex winner determination(WD)task that we represent as a resource procurement optimization problem.We solve this problem using multi-objective genetic algorithms in order to find the trade-off solution that best lowers the price and increases the quantity.This solution consists of multiple winning suppliers,their prices,quantities and schedules.We validate our WD approach based on large-scale simulated datasets.We first assess the time-efficiency of our WD method,and we then compare it to well-known heuristic and exact WD techniques.In order to gain an exact idea about the accuracy of WD,we implement two famous exact algorithms for our constrained combinatorial procurement problem.展开更多
Scheduling projects at the activity level increases the complexity of decision making of project portfolio selection but also expands the search space to include better project portfolios. An integer programming model...Scheduling projects at the activity level increases the complexity of decision making of project portfolio selection but also expands the search space to include better project portfolios. An integer programming model is formulated for the project portfolio selection and scheduling problem. An iterative multi-unit combinatorial auction algorithm is proposed to select and schedule project portfolios through a distributed bidding mechanism. Two price update schemes are designed to adopt either a standard or an adaptive Walrasian tatonnement process. Computational tests show that the proposed auction algorithm with the adaptive price update scheme selects and schedules project portfolios effectively and maximizes the total net present value. The price profile generated by the algorithm also provides managerial insights for project managers and helps to manage the scarce resources efficiently.展开更多
This article proposes a novel grid resource allocation model, in which the users and the grid service providers participate in the combinatorial double auction for the resource allocation. To obtain the detailed resou...This article proposes a novel grid resource allocation model, in which the users and the grid service providers participate in the combinatorial double auction for the resource allocation. To obtain the detailed resource allocation status and the price information, a novel pricing algorithm is designed for the allocation model. Simulation results demonstrate that the proposed algorithm completes the resource allocation and pricing efficiently, and exhibits incentive compatible characteristic. Moreover, users with the higher average price and providers with the lower average price get compensation during the pricing process.展开更多
In practice,we experience low efficiency of search and rescue(SAR)frequently in disaster relief.Here,we will optimize the SAR through agent-based simulation.In the kind of cases described here,rescue teams are charact...In practice,we experience low efficiency of search and rescue(SAR)frequently in disaster relief.Here,we will optimize the SAR through agent-based simulation.In the kind of cases described here,rescue teams are characterized by different capabilities,and the tasks often require different capabilities to complete.To this end,a combinatorial auction-based task allocation scheme is used to develop a cooperative rescue plan for the heterogeneous rescue teams.Then,we illustrate the proposed cooperative rescue plan in different scenarios with the case of landslide disaster relief.The simulation results indicate that the combinatorial auction-based cooperative rescue plan would increase victims’relative survival probability by 13.8–16.3%,increase the ratio of survivors getting rescued by 10.7–12.7%,and decrease the average elapsed time for one site getting rescued by 19.0–26.6%.The proposed rescue plan outperforms the rescue plan based on the F-Max-Sum a little bit.The robustness analysis shows that the proposed rescue plan is relatively reliable on condition that both the search radius and scope of cooperation are larger than thresholds.Furthermore,we have investigated how the number of rescue teams influences the rescue efficiency.展开更多
In this paper, we present a novel, dynamic collaboration cloud platform in which a Combinatorial Auction（CA）-based market model enables the platform to run effectively. The platform can facilitate expense reduction ...In this paper, we present a novel, dynamic collaboration cloud platform in which a Combinatorial Auction（CA）-based market model enables the platform to run effectively. The platform can facilitate expense reduction and improve the scalability of the cloud, which is divided into three layers： The user-layer receives requests from end-users, the auction-layer matches the requests with the cloud services provided by the Cloud Service Provider（CSP）, and the CSP-layer forms a coalition to improve serving ability to satisfy complex requirements of users.In fact, the aim of the coalition formation is to find suitable partners for a particular CSP. However, identifying a suitable combination of partners to form the coalition is an NP-hard problem. Hence, we propose approximation algorithms for the coalition formation. The Breadth Traversal Algorithm（BTA） and Revised Ant Colony Algorithm（RACA） are proposed to form a coalition when bidding for a single cloud service in the auction. The experimental results show that RACA outperforms the BTA in bid price. Other experiments were conducted to evaluate the impact of the communication cost on coalition formation and to assess the impact of iteration times for the optimal bidding price. In addition, the performance of the market model was compared to the existing CA-based model in terms of economic efficiency.展开更多
在云环境下,各种闲置资源可以通过池化形成资源池,进而利用虚拟化技术将资源池中的不同资源组合以服务的形式提供给用户使用,因此需要合理而有效的机制来分配资源.针对云环境下资源的特点,将经济学和智能方法相结合,提出了一种基于双向...在云环境下,各种闲置资源可以通过池化形成资源池,进而利用虚拟化技术将资源池中的不同资源组合以服务的形式提供给用户使用,因此需要合理而有效的机制来分配资源.针对云环境下资源的特点,将经济学和智能方法相结合,提出了一种基于双向组合拍卖的智能资源分配机制.在该机制中,提出了基于体验质量(quality of experience,简称QoE)的威望系统,引入威望衰减系数和用户信誉度,降低拍卖中恶意行为造成的影响,为资源交易提供QoE支持.对拍卖中的竞价决策,综合考虑多种因素,提出了基于BP神经网络的竞标价格决策机制,不仅可以合理确定竞标价,而且使价格可以动态适应市场变化.最后,由于组合拍卖胜标确定问题是NP完全的,因此引入群搜索优化算法,以市场盈余和总体威望为优化目标,得到资源分配方案.仿真研究结果表明,该机制是可行和有效的.展开更多
基金Sponsored by Excellent Young Scholars Research Fund of Beijing Institute of Technology(00Y03-13)
文摘An improved genetic algorithm is proposed to solve the problem of bad real-time performance or inability to get a global optimal/better solution when applying single-item auction （SIA） method or combinatorial auction method to multi-robot task allocation. The genetic algorithm based combinatorial auction （GACA） method which combines the basic-genetic algorithm with a new concept of ringed chromosome is used to solve the winner determination problem （WDP） of combinatorial auction. The simulation experiments are conducted in OpenSim, a multi-robot simulator. The results show that GACA can get a satisfying solution in a reasonable shot time, and compared with SIA or parthenogenesis algorithm combinatorial auction （PGACA） method, it is the simplest and has higher search efficiency, also, GACA can get a global better/optimal solution and satisfy the high real-time requirement of multi-robot task allocation.
基金This publication was supported by the Deanship of Scientific Research at Prince Sattam bin Abdulaziz University,Alkharj,Saudi Arabia.
文摘Cloud computing is a demanding business platform for services related to the field of IT.The goal of cloud customers is to access resources at a sustainable price,while the goal of cloud suppliers is to maximize their services utilization.Previously,the customers would bid for every single resource type,which was a limitation of cloud resources allocation.To solve these issues,researchers have focused on a combinatorial auction in which the resources are offered by the providers in bundles so that the user bids for their required bundle.Still,in this allocation mechanism,some drawbacks need to be tackled,such as due to the lower average bid price the users are dropped from the auction process.To solve this problem,we proposed a“Negotiation based Combinatorial Double Auction Mechanism for Resource Allocation(N-CDARA)in cloud computing”.The proposed method negotiates with dropped users.Lower average bid price users are asked by our proposed mechanism to increase their bids,as by the quoted bids they will be dropped by the auctioneer.Most of the users that are close to winning accept the proposal and increase their bid prices.The proposed mechanism is implemented in a CloudSim simulation toolkit.Results are compared with the latest model and performance study shows that in our proposed scheme more users win and get their requested services and the utilization of offered services is increased up to 18.4%than the existing schemes.
基金This work was partially supported by National Natural Science Foundation of China under Grant No.61801167Natural Science Foundation of Jiangsu Province of China under Grant No.BK20160874.
文摘With the emergence of ambient sensing technologies which combine mobile crowdsensing and Internet of Things,large amount of people-centric data can be obtained and utilized to build people-centric services.Note that the service quality is highly related to the privacy level of the data.In this paper,we investigate the problem of privacy-aware service subscription in people-centric sensing.An efficient resource allocation framework using a combinatorial auction(CA)model is provided.Specifically,the resource allocation problem that maximizes the social welfare in view of varying requirements of multiple users is formulated,and it is solved by a proposed computationally tractable solution algorithm.Furthermore,the prices of allocated resources that winners need to pay are figured out by a designed scheme.Numerical results demonstrate the effectiveness of the proposed scheme.
基金supported by Grants No.402/07/0166No.P402/10/0197 from the Grant Agency of Czech Republic.
文摘Auctions are important market mechanisms for the allocation of goods and services. Combinatorial auctions are those auctions in which buyers can place bids on combinations of items. Combinatorial auctions have many applications. The paper presents the CRAB software system. CRAB is a non-commercial software system for generating, solving, and testing of combinatorial auction problems. The system solves problems by Balas’ method or by the primal-dual algo-rithm. CRAB is implemented in Ruby and it is distributed as the file crab.rb. The system is freely available on web pag-es for all interested
文摘The complexity of cloud environments challenges secure resource management,especially for intrusion detection systems(IDS).Existing strategies struggle to balance efficiency,cost fairness,and threat resilience.This paper proposes an innovative approach to managing cloud resources through the integration of a genetic algorithm(GA)with a“double auction”method.This approach seeks to enhance security and efficiency by aligning buyers and sellers within an intelligent market framework.It guarantees equitable pricing while utilizing resources efficiently and optimizing advantages for all stakeholders.The GA functions as an intelligent search mechanism that identifies optimal combinations of bids from users and suppliers,addressing issues arising from the intricacies of cloud systems.Analyses proved that our method surpasses previous strategies,particularly in terms of price accuracy,speed,and the capacity to manage large-scale activities,critical factors for real-time cybersecurity systems,such as IDS.Our research integrates artificial intelligence-inspired evolutionary algorithms with market-driven methods to develop intelligent resource management systems that are secure,scalable,and adaptable to evolving risks,such as process innovation.
文摘Winner determination is one of the main challenges in combinatorial auctions. However, not much work has been done to solve this problem in the case of reverse auctions using evolutionary techniques. This has motivated us to propose an improvement of a genetic algorithm based method, we have previously proposed, to address two important issues in the context of combinatorial reverse auctions: determining the winner(s) in a reasonable processing time, and reducing the procurement cost. In order to evaluate the performance of our proposed method in practice, we conduct several experiments on combinatorial reverse auctions instances. The results we report in this paper clearly demonstrate the efficiency of our new method in terms of processing time and procurement cost.
文摘The option of organizing E-auctions to purchase electricity required for anticipated peak load period is a new one for utility companies.To meet the extra demand load,we develop electricity combinatorial reverse auction(CRA)for the purpose of procuring power from diverse energy sources.In this new,smart electricity market,suppliers of different scales can participate,and homeowners may even take an active role.In our CRA,an item,which is subject to several trading constraints,denotes a time slot that has two conflicting attributes,electricity quantity and price.To secure electricity,we design our auction with two bidding rounds:round one is exclusively for variable energy,and round two allows storage and nonintermittent renewable energy to bid on the remaining items.Our electricity auction leads to a complex winner determination(WD)task that we represent as a resource procurement optimization problem.We solve this problem using multi-objective genetic algorithms in order to find the trade-off solution that best lowers the price and increases the quantity.This solution consists of multiple winning suppliers,their prices,quantities and schedules.We validate our WD approach based on large-scale simulated datasets.We first assess the time-efficiency of our WD method,and we then compare it to well-known heuristic and exact WD techniques.In order to gain an exact idea about the accuracy of WD,we implement two famous exact algorithms for our constrained combinatorial procurement problem.
文摘Scheduling projects at the activity level increases the complexity of decision making of project portfolio selection but also expands the search space to include better project portfolios. An integer programming model is formulated for the project portfolio selection and scheduling problem. An iterative multi-unit combinatorial auction algorithm is proposed to select and schedule project portfolios through a distributed bidding mechanism. Two price update schemes are designed to adopt either a standard or an adaptive Walrasian tatonnement process. Computational tests show that the proposed auction algorithm with the adaptive price update scheme selects and schedules project portfolios effectively and maximizes the total net present value. The price profile generated by the algorithm also provides managerial insights for project managers and helps to manage the scarce resources efficiently.
基金supported by the Project EC-GIN,European Union(FP6-2006-IST-045256)the National Natural Science Foundation of China(60802033,60873190)
文摘This article proposes a novel grid resource allocation model, in which the users and the grid service providers participate in the combinatorial double auction for the resource allocation. To obtain the detailed resource allocation status and the price information, a novel pricing algorithm is designed for the allocation model. Simulation results demonstrate that the proposed algorithm completes the resource allocation and pricing efficiently, and exhibits incentive compatible characteristic. Moreover, users with the higher average price and providers with the lower average price get compensation during the pricing process.
基金National Natural Science Foundation of China[Grant Nos.71473232,71573237]Research Foundation of Humanities and Social Sciences of Ministry of Education of China[Grant No.15YJA630019].
文摘In practice,we experience low efficiency of search and rescue(SAR)frequently in disaster relief.Here,we will optimize the SAR through agent-based simulation.In the kind of cases described here,rescue teams are characterized by different capabilities,and the tasks often require different capabilities to complete.To this end,a combinatorial auction-based task allocation scheme is used to develop a cooperative rescue plan for the heterogeneous rescue teams.Then,we illustrate the proposed cooperative rescue plan in different scenarios with the case of landslide disaster relief.The simulation results indicate that the combinatorial auction-based cooperative rescue plan would increase victims’relative survival probability by 13.8–16.3%,increase the ratio of survivors getting rescued by 10.7–12.7%,and decrease the average elapsed time for one site getting rescued by 19.0–26.6%.The proposed rescue plan outperforms the rescue plan based on the F-Max-Sum a little bit.The robustness analysis shows that the proposed rescue plan is relatively reliable on condition that both the search radius and scope of cooperation are larger than thresholds.Furthermore,we have investigated how the number of rescue teams influences the rescue efficiency.
基金supported by the National Natural Science Foundation of China (Nos. 61070133, 61170201, and 61472344)the Collegiate Natural Science Foundation of Jiangsu Province (Grant No. 11KJD520011)+1 种基金Six talent peaks project in Jiangsu Province (No. 2011-DZXX-032)the Scientific Research Foundation of Graduate School of Jiangsu Province (No. CXZZ13 0901)
文摘In this paper, we present a novel, dynamic collaboration cloud platform in which a Combinatorial Auction（CA）-based market model enables the platform to run effectively. The platform can facilitate expense reduction and improve the scalability of the cloud, which is divided into three layers： The user-layer receives requests from end-users, the auction-layer matches the requests with the cloud services provided by the Cloud Service Provider（CSP）, and the CSP-layer forms a coalition to improve serving ability to satisfy complex requirements of users.In fact, the aim of the coalition formation is to find suitable partners for a particular CSP. However, identifying a suitable combination of partners to form the coalition is an NP-hard problem. Hence, we propose approximation algorithms for the coalition formation. The Breadth Traversal Algorithm（BTA） and Revised Ant Colony Algorithm（RACA） are proposed to form a coalition when bidding for a single cloud service in the auction. The experimental results show that RACA outperforms the BTA in bid price. Other experiments were conducted to evaluate the impact of the communication cost on coalition formation and to assess the impact of iteration times for the optimal bidding price. In addition, the performance of the market model was compared to the existing CA-based model in terms of economic efficiency.
文摘在云环境下,各种闲置资源可以通过池化形成资源池,进而利用虚拟化技术将资源池中的不同资源组合以服务的形式提供给用户使用,因此需要合理而有效的机制来分配资源.针对云环境下资源的特点,将经济学和智能方法相结合,提出了一种基于双向组合拍卖的智能资源分配机制.在该机制中,提出了基于体验质量(quality of experience,简称QoE)的威望系统,引入威望衰减系数和用户信誉度,降低拍卖中恶意行为造成的影响,为资源交易提供QoE支持.对拍卖中的竞价决策,综合考虑多种因素,提出了基于BP神经网络的竞标价格决策机制,不仅可以合理确定竞标价,而且使价格可以动态适应市场变化.最后,由于组合拍卖胜标确定问题是NP完全的,因此引入群搜索优化算法,以市场盈余和总体威望为优化目标,得到资源分配方案.仿真研究结果表明,该机制是可行和有效的.
