Analysis of Economic Theory Based Modeling for Distributed Resource Allocation

doi:10.13190/j.jbupt.2014.s1.004

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2014, Vol. 37 ›› Issue (s1): 18-22.doi: 10.13190/j.jbupt.2014.s1.004

• Papers • Previous Articles Next Articles

Analysis of Economic Theory Based Modeling for Distributed Resource Allocation

WANG En-ze, QIAO Jian-zhong, LIN Shu-kuan

College of Information Science and Engineering, Northeastern University, Shenyang 100819, China

Received:2013-11-06 Online:2014-06-28 Published:2014-06-28
Supported by:

Abstract

Abstract:

Research on economic theory to solve distributed resource allocation problem, especially its characteristics in distributed computing is ignored. The difference between two environments is analyzed. It is pointed out that the key elements of ignored features in distributed computing is resource, task, individual and price. The framework in neoclassical economics to make economic analysis for distributed resource allocation is given by comparing the resource allocation process in distributed market environment. The modeling method of how to use economic theory to solve distributed resource allocation is described.

Key words: distributed computing, resource allocation, economic theory

CLC Number:

TN302.7

WANG En-ze, QIAO Jian-zhong, LIN Shu-kuan. Analysis of Economic Theory Based Modeling for Distributed Resource Allocation[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2014, 37(s1): 18-22.

References

[1] 张联峰,刘乃安,钱秀槟,等. 综述: 对等网(P2P) 技术[J]. 计算机工程与应用,2005, 41(12): 142-145.

[2] 周文莉,吴晓非. P2P技术综述[J]. 计算机工程与设计, 2006,27(1): 76-79.

[3] Foster I, Kesselman C. The grid 2: blueprint for a new computing infrastructure[M]. Access Online via Elsevier, 2003.

[4] Korpela E, Werthimer D, Anderson D, et al. SETI@ home—massively distributed computing for SETI[J]. Computing in Science & Engineering, 2001, 3(1): 78-83.

[5] Weiss A. Computing in the clouds[J]. Networker, 2007, 11(4): 16-25.

[6] Brian H, Brunschwiler T, Dill H, et al. Cloud computing[J]. Communications of the ACM, 2008, 51(7): 9-11.

[7] Sutherlaind I E. A futures market in computer time[J]. Communications of the ACM, 1968, 11(6): 449-451.

[8] Waldspurger C A. A distributed computational economy for utilizing idle resources[D]. Doctoral Dissertation, Massachusetts Institute of Technology, 1989.

[9] Waldspurger C A, Hogg T, Huberman B A, et al. Spawn: a distributed computational economy[J]. Software Engineering, IEEE Transactions on, 1992, 18(2): 103-117.

[10] Mackie M J K, Varian H R. Pricing the internet[J]. Public Access to the Internet, 1995: 269-314.

[11] Cheng J Q. The WALRAS algorithm: a convergent distributed implementation of general equilibrium outcomes[J]. Computational Economics, 1998, 12(1): 1-24.

[12] Wolski R, Plank J S, Brevik J, et al. Analyzing market-based resource allocation strategies for the computational grid[J]. International Journal of High Performance Computing Applications, 2001, 15(3): 258-281.

[13] Buyya R, Abramson D. The grid economy[J]. Proceedings of the IEEE, 2005, 93(3): 698-714.

[14] Stuer G, Vanmechelen K. A commodity market algorithm for pricing substitutable grid resources[J]. Future Generation Computer Systems, 2007, 23(5): 688-701.

[15] Grosu D. Auction-based resource allocation protocols in grids[C]//Proceedings of the 16^th IASTED International Conference on Parallel and Distributed Computing and Systems, 2004: 20-27.

[16] De A M D, Buyya R. An evaluation of communication demand of auction protocols in grid environments[C]//Proceedings of the 3^rd International Workshop on Grid Economics & Business (GECON 2006), 2006:1-12.

[17] Vickrey W. Counter speculation, auctions, and competitive sealed tenders[J]. The Journal of Finance, 1961, 16(1): 8-37.

[18] 魏立佳. 从微观理论到社会实践:市场设计的最新进展综述[J]. 世界经济文汇, 2013(3): 89-104.

[19] 杨小凯. 新兴古典经济学与超边际分析[M]. 北京: 社会科学文献出版社, 2003.

[20] 王恩泽,乔建忠,林树宽. 一种基于超边际分析的分布式资源分配方法[J]. 东北大学学报: 自然科学版, 2011, 32(2): 219-222.

[21] 哈尔 R 范里安. 微观经济学:现代观点[M]. 费方域, 译. 上海:上海人民出版社,2001: 38-53.

Analysis of Economic Theory Based Modeling for Distributed Resource Allocation

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	. Research on Resource Allocation and Task Offloading Joint Optimization for Mobile Edge Computing in Ultra-Dense Networks [J]. Journal of Beijing University of Posts and Telecommunications, 2023, 46(2): 50-56.
[2]	. An Ensemble Learning-Aided DDPG Resource Optimization Algorithm [J]. Journal of Beijing University of Posts and Telecommunications, 2023, 46(2): 29-36.
[3]	. Cooperative Offloading and Resource Allocation Algorithm of Multi-edge Nodes in VEC [J]. Journal of Beijing University of Posts and Telecommunications, 2023, 46(2): 78-83.
[4]	. Resource Allocation Based on Alternating Direction Multiplier Method and Deep Reinforcement Learning Algorithm [J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(6): 126-130.
[5]	YU Xiaosong, ZHU Qingcheng, GU Jiaming, ZHAO Yongli, ZHANG Jie. Architecture and Resource Allocation of Cloud-Edge Collaboration Based Optical-Quantum Internet of Things [J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(3): 50-56.
[6]	DU Mei, ZHOU Junhua, LI Dunqiao, CHEN Shizhao, WEI Yifei. A Joint Intelligent Optimization Scheme of Computation Offloading and Resource Allocation for MEC [J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(2): 65-71.
[7]	JIA Yuning, WEI Yifei, ZHOU Junhua. Service Function Chain Orchestrating Algorithm Based on SDN and NFV [J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(2): 85-90.
[8]	GAO Xiao-na, LU Guang-yue, YE Ying-hui, ZAN Jin-mei. Throughput Fairness Guarantee in Cognitive Backscattering Networks [J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(6): 26-32.
[9]	YAN Shi, PENG Mu-gen, WANG Wen-bo. Integration of Communication, Sensing and Computing: the Vision and Key Technologies of 6G [J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(4): 1-11.
[10]	HUANG Xiao-ge, WANG Yong-sheng, LAI Chen-bin, CHEN Qian-bin. Trust Degree Based RSU Recognition and Resource Allocation in Vehicular Networks [J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(2): 109-115.
[11]	TIAN Hui, NI Wan-li, WANG Wen, ZHENG Jing-heng, HE Shuo. Data-Importance-Aware Resource Allocation in IRS-Aided Edge Intelligent System [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(6): 51-58.
[12]	XUE Jian-bin, DING Xue-qian, LIU Xing-xing. Offloading Decision and Resource Optimization for Cache-Assisted Edge Computing [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(3): 32-37.
[13]	GUO Shao-xiong, LIU Yu-tao, Lü Yu-jing, ZHANG Zhong-zhao. Resource Allocation Algorithm for Simultaneous Wireless Information and Power Transfer in Multi-Carrier Cognitive Radio [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(3): 19-23.
[14]	LI Qiang, LU Chang-long, CAO Bin, ZHANG Qin-yu. High Spectrum Efficiency Wireless Resource Management Based on Network Slicing [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(4): 50-56.
[15]	TIAN Xin-ji, ZHANG Hao-tian, LI Xiao-jing, ZHANG Chang-sen. JR-CoMP-Based Clustering and Resource Allocation Scheme in UDN [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(3): 72-78.