The Experts below are selected from a list of 6540 Experts worldwide ranked by ideXlab platform
Zongpeng Li - One of the best experts on this subject based on the ideXlab platform.
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An Online Auction Mechanism for Dynamic Virtual Cluster Provisioning in Geo-Distributed Clouds
IEEE Transactions on Parallel and Distributed Systems, 2017Co-Authors: Weijie Shi, Chuan Wu, Zongpeng LiAbstract:It is common for cloud users to require clusters of inter-connected virtual machines (VMs) in a geo-distributed IaaS cloud, to run their services. Compared to isolated VMs, key challenges on dynamic virtual cluster (VC) provisioning (computation + communication resources) lie in two folds: (1) optimal placement of VCs and inter-VM traffic routing involve NP-hard problems, which are non-trivial to solve offline, not to mention if an Online efficient algorithm is sought; (2) an efficient pricing mechanism is missing, which charges a market-driven price for each VC as a whole upon request, while maximizing system efficiency or provider revenue over the entire span. This paper proposes efficient Online Auction mechanisms to address the above challenges. We first design SWMOA, a novel Online algorithm for dynamic VC provisioning and pricing, achieving truthfulness, individual rationality, computation efficiency, and (1 + 2 log ¿¿)-competitiveness in social welfare, where ¿¿ is related to the problem size. Next, applying a randomized reduction technique, we convert the social welfare maximizing Auction into a revenue maximizing Online Auction, PRMOA, achieving O(log ¿¿)-competitiveness in provider revenue, as well as truthfulness, individual rationality and computation efficiency. We validate the efficacy of the mechanisms through solid theoretical analysis and trace-driven simulations.
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An Online Auction Mechanism for Dynamic Virtual Cluster Provisioning in Geo-Distributed Clouds
IEEE Transactions on Parallel and Distributed Systems, 2017Co-Authors: Chuan Wu, Zongpeng LiAbstract:It is common for cloud users to require clusters of inter-connected virtual machines (VMs) in a geo-distributed IaaS cloud, to run their services. Compared to isolated VMs, key challenges on dynamic virtual cluster (VC) provisioning (computation + communication resources) lie in two folds: (1) optimal placement of VCs and inter-VM traffic routing involve NP-hard problems, which are non-trivial to solve offline, not to mention if an Online efficient algorithm is sought; (2) an efficient pricing mechanism is missing, which charges a market-driven price for each VC as a whole upon request, while maximizing system efficiency or provider revenue over the entire span. This paper proposes efficient Online Auction mechanisms to address the above challenges. We first design SWMOA, a novel Online algorithm for dynamic VC provisioning and pricing, achieving truthfulness, individual rationality, computation efficiency, and (1 + 2 log μ)-competitiveness in social welfare, where m is related to the problem size. Next, applying a randomized reduction technique, we convert the social welfare maximizing Auction into a revenue maximizing Online Auction, PRMOA, achieving O(log μ)-competitiveness in provider revenue, as well as truthfulness, individual rationality and computation efficiency. We investigate Auction design in different cases of resource cost functions in the system. We validate the efficacy of the mechanisms through solid theoretical analysis and trace-driven simulations.
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An Online Auction Framework for Dynamic Resource Provisioning in Cloud Computing
IEEE ACM Transactions on Networking, 2016Co-Authors: Linquan Zhang, Chuan Wu, Zongpeng LiAbstract:Auction mechanisms have recently attracted substantial attention as an efficient approach to pricing and allocating resources in cloud computing. This work, to the authors' knowledge, represents the first Online combinatorial Auction designed for the cloud computing paradigm, which is general and expressive enough to both: 1) optimize system efficiency across the temporal domain instead of at an isolated time point; and 2) model dynamic provisioning of heterogeneous virtual machine (VM) types in practice. The final result is an Online Auction framework that is truthful, computationally efficient, and guarantees a competitive ratio ≈ 3.30 in social welfare in typical scenarios. The framework consists of three main steps: 1) a tailored primal-dual algorithm that decomposes the long-term optimization into a series of independent one-shot optimization problems, with a small additive loss in competitive ratio; 2) a randomized subframework that applies primal-dual optimization for translating a centralized cooperative social welfare approximation algorithm into an Auction mechanism, retaining the competitive ratio while adding truthfulness; and 3) a primal-dual algorithm for approximating the one-shot optimization with a ratio close to e. We also propose two extensions: 1) a binary search algorithm that improves the average-case performance; 2) an improvement to the Online Auction framework when a minimum budget spending fraction is guaranteed, which produces a better competitive ratio. The efficacy of the Online Auction framework is validated through theoretical analysis and trace-driven simulation studies. We are also in the hope that the framework can be instructive in Auction design for other related problems.
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An Online Auction for Deadline-Aware Dynamic Cloud Resource Provisioning
2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS), 2016Co-Authors: Kai He, Chuanhe Huang, Zongpeng LiAbstract:Auction mechanisms have recently been studied as an efficient approach for dynamic resource allocation in a cloud market. Existing mechanisms are mostly limited to the offline setting or execute jobs in continuous time slots. This work focuses on a practical case of Online Auction design, where users bid for future cloud resources for executing their batch processing jobs with hard deadline constraints. We design an Online primal-dual Auction framework for Virtual Machine (VM) allocation with social welfare maximization, which is truthful, computationally efficient, and guarantees a small competitive ratio. We leverage the framework of post price Auctions to design our Online primal-dual algorithm, where a bid is accepted if its expected execution cost in future time slots is smaller than its bidding price. We interpret the dual variables as marginal prices per unit of resource, and iteratively update it according to the allocated amount of resource. Theoretical analysis and trace-driven simulation studies validate the efficacy of the Online Auction framework, including both its computational efficiency and economic efficiency.
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an Online Auction framework for dynamic resource provisioning in cloud computing
Measurement and Modeling of Computer Systems, 2014Co-Authors: Linquan Zhang, Chuan Wu, Zongpeng LiAbstract:Auction mechanisms have recently attracted substantial attention as an efficient approach to pricing and resource allocation in cloud computing. This work, to the authors' knowledge, represents the first Online combinatorial Auction designed in the cloud computing paradigm, which is general and expressive enough to both (a) optimize system efficiency across the temporal domain instead of at an isolated time point, and (b) model dynamic provisioning of heterogeneous Virtual Machine (VM) types in practice. The final result is an Online Auction framework that is truthful, computationally efficient, and guarantees a competitive ratio ~ e+ 1 over e-1 ~ 3.30 in social welfare in typical scenarios. The framework consists of three main steps: (1) a tailored primal-dual algorithm that decomposes the long-term optimization into a series of independent one-shot optimization problems, with an additive loss of 1 over e-1 in competitive ratio, (2) a randomized Auction sub-framework that applies primal-dual optimization for translating a centralized co-operative social welfare approximation algorithm into an Auction mechanism, retaining a similar approximation ratio while adding truthfulness, and (3) a primal-dual update plus dual fitting algorithm for approximating the one-shot optimization with a ratio λ close to e. The efficacy of the Online Auction framework is validated through theoretical analysis and trace-driven simulation studies. We are also in the hope that the framework, as well as its three independent modules, can be instructive in Auction design for other related problems.
Chuan Wu - One of the best experts on this subject based on the ideXlab platform.
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An Online Auction Mechanism for Dynamic Virtual Cluster Provisioning in Geo-Distributed Clouds
IEEE Transactions on Parallel and Distributed Systems, 2017Co-Authors: Weijie Shi, Chuan Wu, Zongpeng LiAbstract:It is common for cloud users to require clusters of inter-connected virtual machines (VMs) in a geo-distributed IaaS cloud, to run their services. Compared to isolated VMs, key challenges on dynamic virtual cluster (VC) provisioning (computation + communication resources) lie in two folds: (1) optimal placement of VCs and inter-VM traffic routing involve NP-hard problems, which are non-trivial to solve offline, not to mention if an Online efficient algorithm is sought; (2) an efficient pricing mechanism is missing, which charges a market-driven price for each VC as a whole upon request, while maximizing system efficiency or provider revenue over the entire span. This paper proposes efficient Online Auction mechanisms to address the above challenges. We first design SWMOA, a novel Online algorithm for dynamic VC provisioning and pricing, achieving truthfulness, individual rationality, computation efficiency, and (1 + 2 log ¿¿)-competitiveness in social welfare, where ¿¿ is related to the problem size. Next, applying a randomized reduction technique, we convert the social welfare maximizing Auction into a revenue maximizing Online Auction, PRMOA, achieving O(log ¿¿)-competitiveness in provider revenue, as well as truthfulness, individual rationality and computation efficiency. We validate the efficacy of the mechanisms through solid theoretical analysis and trace-driven simulations.
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An Online Auction Mechanism for Dynamic Virtual Cluster Provisioning in Geo-Distributed Clouds
IEEE Transactions on Parallel and Distributed Systems, 2017Co-Authors: Chuan Wu, Zongpeng LiAbstract:It is common for cloud users to require clusters of inter-connected virtual machines (VMs) in a geo-distributed IaaS cloud, to run their services. Compared to isolated VMs, key challenges on dynamic virtual cluster (VC) provisioning (computation + communication resources) lie in two folds: (1) optimal placement of VCs and inter-VM traffic routing involve NP-hard problems, which are non-trivial to solve offline, not to mention if an Online efficient algorithm is sought; (2) an efficient pricing mechanism is missing, which charges a market-driven price for each VC as a whole upon request, while maximizing system efficiency or provider revenue over the entire span. This paper proposes efficient Online Auction mechanisms to address the above challenges. We first design SWMOA, a novel Online algorithm for dynamic VC provisioning and pricing, achieving truthfulness, individual rationality, computation efficiency, and (1 + 2 log μ)-competitiveness in social welfare, where m is related to the problem size. Next, applying a randomized reduction technique, we convert the social welfare maximizing Auction into a revenue maximizing Online Auction, PRMOA, achieving O(log μ)-competitiveness in provider revenue, as well as truthfulness, individual rationality and computation efficiency. We investigate Auction design in different cases of resource cost functions in the system. We validate the efficacy of the mechanisms through solid theoretical analysis and trace-driven simulations.
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An Online Auction Framework for Dynamic Resource Provisioning in Cloud Computing
IEEE ACM Transactions on Networking, 2016Co-Authors: Linquan Zhang, Chuan Wu, Zongpeng LiAbstract:Auction mechanisms have recently attracted substantial attention as an efficient approach to pricing and allocating resources in cloud computing. This work, to the authors' knowledge, represents the first Online combinatorial Auction designed for the cloud computing paradigm, which is general and expressive enough to both: 1) optimize system efficiency across the temporal domain instead of at an isolated time point; and 2) model dynamic provisioning of heterogeneous virtual machine (VM) types in practice. The final result is an Online Auction framework that is truthful, computationally efficient, and guarantees a competitive ratio ≈ 3.30 in social welfare in typical scenarios. The framework consists of three main steps: 1) a tailored primal-dual algorithm that decomposes the long-term optimization into a series of independent one-shot optimization problems, with a small additive loss in competitive ratio; 2) a randomized subframework that applies primal-dual optimization for translating a centralized cooperative social welfare approximation algorithm into an Auction mechanism, retaining the competitive ratio while adding truthfulness; and 3) a primal-dual algorithm for approximating the one-shot optimization with a ratio close to e. We also propose two extensions: 1) a binary search algorithm that improves the average-case performance; 2) an improvement to the Online Auction framework when a minimum budget spending fraction is guaranteed, which produces a better competitive ratio. The efficacy of the Online Auction framework is validated through theoretical analysis and trace-driven simulation studies. We are also in the hope that the framework can be instructive in Auction design for other related problems.
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an Online Auction framework for dynamic resource provisioning in cloud computing
Measurement and Modeling of Computer Systems, 2014Co-Authors: Linquan Zhang, Chuan Wu, Zongpeng LiAbstract:Auction mechanisms have recently attracted substantial attention as an efficient approach to pricing and resource allocation in cloud computing. This work, to the authors' knowledge, represents the first Online combinatorial Auction designed in the cloud computing paradigm, which is general and expressive enough to both (a) optimize system efficiency across the temporal domain instead of at an isolated time point, and (b) model dynamic provisioning of heterogeneous Virtual Machine (VM) types in practice. The final result is an Online Auction framework that is truthful, computationally efficient, and guarantees a competitive ratio ~ e+ 1 over e-1 ~ 3.30 in social welfare in typical scenarios. The framework consists of three main steps: (1) a tailored primal-dual algorithm that decomposes the long-term optimization into a series of independent one-shot optimization problems, with an additive loss of 1 over e-1 in competitive ratio, (2) a randomized Auction sub-framework that applies primal-dual optimization for translating a centralized co-operative social welfare approximation algorithm into an Auction mechanism, retaining a similar approximation ratio while adding truthfulness, and (3) a primal-dual update plus dual fitting algorithm for approximating the one-shot optimization with a ratio λ close to e. The efficacy of the Online Auction framework is validated through theoretical analysis and trace-driven simulation studies. We are also in the hope that the framework, as well as its three independent modules, can be instructive in Auction design for other related problems.
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Dynamic VM Scaling: Provisioning and Pricing through an Online Auction
IEEE Transactions on Cloud Computing, 1Co-Authors: Xiaoxi Zhang, Chuan Wu, Zhiyi Huang, Zongpeng LiAbstract:Today's IaaS clouds allow dynamic scaling of VMs allocated to a user, according to real-time demand of the user. There are two types of scaling: horizontal scaling (scale-out) by allocating more VM instances to the user, and vertical scaling (scale-up) by boosting resources of VMs owned by the user. It has been a daunting issue how to efficiently allocate the resources on physical servers to meet the scaling demand of users on the go, which achieves the best server utilization and user utility. An accompanying critical challenge is how to effectively charge the incremental resources, such that the economic benefits of both the cloud provider and cloud users are guaranteed. There has been Online Auction design dealing with dynamic VM provisioning, where the resource bids are not related to each other, failing to handle VM scaling where later bids may rely on earlier bids of the same user. As the first in the literature, this paper designs an efficient, truthful Online Auction for resource provisioning and pricing in the practical cases of dynamic VM scaling, where: (i) users bid for customized VMs to use in future durations, and can bid again in the following time to increase resources, indicating both scale-up and scale-out options; (ii) the cloud provider packs the demanded VMs on heterogeneous servers for energy cost minimization on the go. We carefully design resource prices maintained for each type of resource on each server to achieve threshold-based Online allocation and charging, as well as a novel competitive analysis technique based on submodularity of the offline objective, to show a good competitive ratio is achieved. The efficacy of the Online Auction is validated through solid theoretical analysis and trace-driven simulations.
Steven Walczak - One of the best experts on this subject based on the ideXlab platform.
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MARKET DECISION MAKING FOR Online Auction SELLERS: PROFIT MAXIMIZATION OR SOCIALIZATION
Journal of Electronic Commerce Research, 2006Co-Authors: Steven Walczak, Dawn G. Gregg, Joy L. BerrenbergAbstract:The purpose of this investigation is to identify factors in the decision making processes used by Online Auction sellers to select their Online Auction sales channel. Examining these decision factors will aid in creating a model of Online Auction seller channel evaluation mechanisms including economic and social factors and may be used by Online Auction services and intermediaries to maximize their market potential by increasing the perceived value of the various economic or social factors influencing seller outlet selection. An exploratory survey analysis is used to identify the components that Online seller’s use for Online channel selection.
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Auction advisor an agent based Online Auction decision support system
Decision Support Systems, 2006Co-Authors: Dawn G. Gregg, Steven WalczakAbstract:Online Auctions are proving themselves as a viable alternative in the C2C and B2C marketplace. Several thousand new items are placed for Auction every day and determining which items to bid on or when and where to sell an item are difficult questions to answer for Online-Auction participants. This paper presents a multiagent Auction Advisor system designed to collect data related to Online Auctions and use the data to help improve the decision making of Auction participants. A simulation of applied Auction Advisor recommendations and a small research study that used subjects making real purchases at Online Auctions both indicate that Online-Auction buyers and sellers achieve tangible benefit from the current information acquired by and recommendations made by the Auction Advisor agents.
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E‐commerce Auction Agents and Online‐Auction Dynamics
Electronic Markets, 2003Co-Authors: Dawn G. Gregg, Steven WalczakAbstract:Online‐Auctions are one of the most successful types of electronic markets. They bring together buyers and sellers on a massive scale. However, using an electronic medium for conducting Auctions has fundamental differences from traditional English‐style Auctions. One difference is the availability of software agents that can facilitate many aspects of Online‐Auction participation. The addition of software agents into Online‐Auctions is already having an impact on the dynamics of Online‐Auctions. This study examines existing agent technologies with regard to their effect on Online‐Auctions. In addition, future directions for research related to Online‐Auction agents and the possible benefits of these agents are also discussed.
Weijie Shi - One of the best experts on this subject based on the ideXlab platform.
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An Online Auction Mechanism for Dynamic Virtual Cluster Provisioning in Geo-Distributed Clouds
IEEE Transactions on Parallel and Distributed Systems, 2017Co-Authors: Weijie Shi, Chuan Wu, Zongpeng LiAbstract:It is common for cloud users to require clusters of inter-connected virtual machines (VMs) in a geo-distributed IaaS cloud, to run their services. Compared to isolated VMs, key challenges on dynamic virtual cluster (VC) provisioning (computation + communication resources) lie in two folds: (1) optimal placement of VCs and inter-VM traffic routing involve NP-hard problems, which are non-trivial to solve offline, not to mention if an Online efficient algorithm is sought; (2) an efficient pricing mechanism is missing, which charges a market-driven price for each VC as a whole upon request, while maximizing system efficiency or provider revenue over the entire span. This paper proposes efficient Online Auction mechanisms to address the above challenges. We first design SWMOA, a novel Online algorithm for dynamic VC provisioning and pricing, achieving truthfulness, individual rationality, computation efficiency, and (1 + 2 log ¿¿)-competitiveness in social welfare, where ¿¿ is related to the problem size. Next, applying a randomized reduction technique, we convert the social welfare maximizing Auction into a revenue maximizing Online Auction, PRMOA, achieving O(log ¿¿)-competitiveness in provider revenue, as well as truthfulness, individual rationality and computation efficiency. We validate the efficacy of the mechanisms through solid theoretical analysis and trace-driven simulations.
Dawn G. Gregg - One of the best experts on this subject based on the ideXlab platform.
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MARKET DECISION MAKING FOR Online Auction SELLERS: PROFIT MAXIMIZATION OR SOCIALIZATION
Journal of Electronic Commerce Research, 2006Co-Authors: Steven Walczak, Dawn G. Gregg, Joy L. BerrenbergAbstract:The purpose of this investigation is to identify factors in the decision making processes used by Online Auction sellers to select their Online Auction sales channel. Examining these decision factors will aid in creating a model of Online Auction seller channel evaluation mechanisms including economic and social factors and may be used by Online Auction services and intermediaries to maximize their market potential by increasing the perceived value of the various economic or social factors influencing seller outlet selection. An exploratory survey analysis is used to identify the components that Online seller’s use for Online channel selection.
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Auction advisor an agent based Online Auction decision support system
Decision Support Systems, 2006Co-Authors: Dawn G. Gregg, Steven WalczakAbstract:Online Auctions are proving themselves as a viable alternative in the C2C and B2C marketplace. Several thousand new items are placed for Auction every day and determining which items to bid on or when and where to sell an item are difficult questions to answer for Online-Auction participants. This paper presents a multiagent Auction Advisor system designed to collect data related to Online Auctions and use the data to help improve the decision making of Auction participants. A simulation of applied Auction Advisor recommendations and a small research study that used subjects making real purchases at Online Auctions both indicate that Online-Auction buyers and sellers achieve tangible benefit from the current information acquired by and recommendations made by the Auction Advisor agents.
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E‐commerce Auction Agents and Online‐Auction Dynamics
Electronic Markets, 2003Co-Authors: Dawn G. Gregg, Steven WalczakAbstract:Online‐Auctions are one of the most successful types of electronic markets. They bring together buyers and sellers on a massive scale. However, using an electronic medium for conducting Auctions has fundamental differences from traditional English‐style Auctions. One difference is the availability of software agents that can facilitate many aspects of Online‐Auction participation. The addition of software agents into Online‐Auctions is already having an impact on the dynamics of Online‐Auctions. This study examines existing agent technologies with regard to their effect on Online‐Auctions. In addition, future directions for research related to Online‐Auction agents and the possible benefits of these agents are also discussed.
