• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于真实在线双边拍卖的IaaS云多目标权衡动态资源供应

Truthful online double auction based dynamic resource provisioning for multi-objective trade-offs in IaaS clouds.

作者信息

Patel Yashwant Singh, Malwi Zahra, Nighojkar Animesh, Misra Rajiv

机构信息

Department of Computer Science and Engineering, Indian Institute of Technology Patna, Bihar, India.

ValueLabs LLP, Indore, India.

出版信息

Cluster Comput. 2021;24(3):1855-1879. doi: 10.1007/s10586-020-03225-9. Epub 2021 Jan 11.

DOI:10.1007/s10586-020-03225-9
PMID:33456318
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7799171/
Abstract

Auction designs have recently been adopted for static and dynamic resource provisioning in IaaS clouds, such as Microsoft Azure and Amazon EC2. However, the existing mechanisms are mostly restricted to simple auctions, single-objective, offline setting, one-sided interactions either among cloud users or cloud service providers (CSPs), and possible misreports of cloud user's private information. This paper proposes a more realistic scenario of online auctioning for IaaS clouds, with the unique characteristics of elasticity for time-varying arrival of cloud user requests under the time-based server maintenance in cloud data centers. We propose an online truthful double auction technique for balancing the multi-objective trade-offs between energy, revenue, and performance in IaaS clouds, consisting of a weighted bipartite matching based winning-bid determination algorithm for resource allocation and a Vickrey-Clarke-Groves (VCG) driven algorithm for payment calculation of winning bids. Through rigorous theoretical analysis and extensive trace-driven simulation studies exploiting Google cluster workload traces, we demonstrate that our mechanism significantly improves the performance while promising truthfulness, heterogeneity, economic efficiency, individual rationality, and has a polynomial-time computational complexity.

摘要

拍卖设计最近已被用于IaaS云（如Microsoft Azure和Amazon EC2）中的静态和动态资源供应。然而，现有机制大多局限于简单拍卖、单目标、离线设置、云用户或云服务提供商（CSP）之间的单边交互，以及云用户私有信息的可能误报。本文提出了一种更现实的IaaS云在线拍卖场景，具有云数据中心基于时间的服务器维护下云用户请求随时间变化到达的弹性这一独特特征。我们提出了一种在线真实双拍卖技术，用于平衡IaaS云中能源、收益和性能之间的多目标权衡，该技术由基于加权二分匹配的中标确定算法用于资源分配，以及由维克瑞-克拉克-格罗夫斯（VCG）驱动的算法用于中标支付计算。通过严格的理论分析和利用谷歌集群工作负载跟踪进行的广泛跟踪驱动模拟研究，我们证明我们的机制在保证真实性、异构性、经济效率、个体合理性的同时显著提高了性能，并且具有多项式时间计算复杂度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/0b3037a6a4df/10586_2020_3225_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/7b4e647e4506/10586_2020_3225_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/85ba4515e65c/10586_2020_3225_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/9b0b2671275f/10586_2020_3225_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/3aabb30cf32f/10586_2020_3225_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/6dfb3f2a23b7/10586_2020_3225_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/5478df6f9c6f/10586_2020_3225_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/fcd1db6b1102/10586_2020_3225_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/811eb576fda5/10586_2020_3225_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/054430b8337a/10586_2020_3225_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/e6cf249cfe66/10586_2020_3225_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/cd69d5d8807a/10586_2020_3225_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/8deaadc54963/10586_2020_3225_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/0b3037a6a4df/10586_2020_3225_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/7b4e647e4506/10586_2020_3225_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/85ba4515e65c/10586_2020_3225_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/9b0b2671275f/10586_2020_3225_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/3aabb30cf32f/10586_2020_3225_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/6dfb3f2a23b7/10586_2020_3225_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/5478df6f9c6f/10586_2020_3225_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/fcd1db6b1102/10586_2020_3225_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/811eb576fda5/10586_2020_3225_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/054430b8337a/10586_2020_3225_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/e6cf249cfe66/10586_2020_3225_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/cd69d5d8807a/10586_2020_3225_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/8deaadc54963/10586_2020_3225_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c26/7799171/0b3037a6a4df/10586_2020_3225_Fig13_HTML.jpg

相似文献

1
Truthful online double auction based dynamic resource provisioning for multi-objective trade-offs in IaaS clouds.基于真实在线双边拍卖的IaaS云多目标权衡动态资源供应
Cluster Comput. 2021;24(3):1855-1879. doi: 10.1007/s10586-020-03225-9. Epub 2021 Jan 11.
2
Truthful Incentive Mechanisms for Social Cost Minimization in Mobile Crowdsourcing Systems.移动众包系统中用于最小化社会成本的真实激励机制
Sensors (Basel). 2016 Apr 6;16(4):481. doi: 10.3390/s16040481.
3
A Truthful Incentive Mechanism for Online Recruitment in Mobile Crowd Sensing System.移动人群感知系统中在线招募的真实激励机制
Sensors (Basel). 2017 Jan 1;17(1):79. doi: 10.3390/s17010079.
4
Design of online combinatorial auction mechanism for urban land transfer.城市土地出让的在线组合拍卖机制设计。
PLoS One. 2023 Oct 31;18(10):e0284775. doi: 10.1371/journal.pone.0284775. eCollection 2023.
5
A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques.一种使用深度学习和优化技术的混合云负载均衡与主机利用率预测方法。
Sci Rep. 2024 Jan 16;14(1):1337. doi: 10.1038/s41598-024-51466-0.
6
VM Capacity-Aware Scheduling within Budget Constraints in IaaS Clouds.IaaS 云中预算约束内的虚拟机容量感知调度
PLoS One. 2016 Aug 8;11(8):e0160456. doi: 10.1371/journal.pone.0160456. eCollection 2016.
7
An Incentive Mechanism in Mobile Crowdsourcing Based on Multi-Attribute Reverse Auctions.基于多属性逆向拍卖的移动众包中的激励机制。
Sensors (Basel). 2018 Oct 14;18(10):3453. doi: 10.3390/s18103453.
8
A DRL-based online VM scheduler for cost optimization in cloud brokers.一种基于深度强化学习的在线虚拟机调度器，用于云代理中的成本优化。
World Wide Web. 2023 Mar 14:1-27. doi: 10.1007/s11280-023-01145-3.
9
Adaptive and Economically-Robust Group Selling of Spectrum Slots for Cognitive Radio-Based Networks.基于认知无线电的网络中频谱时隙的自适应和经济稳健的分组售卖
Sensors (Basel). 2018 Aug 1;18(8):2490. doi: 10.3390/s18082490.
10
Comparative Double Auction Approach for Peer-to-Peer Energy Trading on Multiple microgrids.多微电网间对等能源交易的比较双拍卖方法
Smart Grids Sustain Energy. 2023;8(4):21. doi: 10.1007/s40866-023-00178-x. Epub 2023 Nov 21.