Academia.eduAcademia.edu

Outline

Title
Abstract
Chapter 1 Introduction 1.1 Motivation
Phase 1: Environmental Analysis
Phase 2: Design and Implementation
Conceptual Design
Embodiment Design
Detail Design and Implementation
Experimental Approach
Environmental Analysis
Requirement Analysis
Summary
Chapter 5 Conclusion
Review of This Work
References
All Topics
Computer Science
Distributed Systems

Environmental Analysis for Application Layer Networks

Profile image of Mauro NapoletanoMauro NapoletanoProfile image of Torsten EymannTorsten Eymann

2005

Abstract
sparkles

AI

The paper discusses the development and analysis of Application Layer Networks as overlay networks built on top of TCP/IP, focusing on resource allocation and scheduling mechanisms. It highlights the limitations of existing static optimization algorithms and advocates for the implementation of market mechanisms to achieve more economically efficient resource management. The envisioned scenario involves agents demanding services that require various computational resources, emphasizing the interplay of supply and demand for effective resource allocation in a decentralized framework.

Related papers

Resource Management Systems: Scheduling of Resource-Intensive Multi-Component Applications
Salvatore Orlando

2004

A Grid is a dynamic, seamless, integrated computational and collaborative environment. Grids integrate networking, communication, computation and information to provide a virtual platform for computing and data management. Machines in a Grid are typically grouped into autonomous administrative domains that communicate via high-speed communication links. For a good description of Grid technology refer to [14, 6].

View PDFchevron_right
Enlightened computing: an architecture for co-Allocating network, compute, and other grid resources for high-End applications
syam sundar

2007

Many emerging high performance applications require distributed infrastructure that is significantly more powerful and flexible than traditional Grids. Such applications require the optimization, close integration, and control of all Grid resources, including networks. The EnLIGHTened (ENL) Computing Project has designed an architectural framework that allows Grid applications to dynamically request (inadvance or on-demand) any type of Grid resource: computers, storage, instruments, and deterministic, high-bandwidth network paths, including lightpaths. Based on application requirements, the ENL middleware communicates with Grid resource managers and, when availability is verified, co-allocates all the necessary resources. ENL's Domain Network Manager controls all network resource allocations to dynamically setup and delete dedicated circuits using Generalized Multiprotocol Label Switching (GMPLS) control plane signaling. In order to make optimal brokering decisions, the ENL middleware uses near-real-time performance information about Grid resources. A prototype of this architectural framework on a national-scale testbed implementation has been used to demonstrate a small number of applications. Based on this, a set of changes for the middleware have been laid out and are being implemented.

View PDFchevron_right
Network Aware Scheduling in Grids
Bart Dhoedt

2004

Computational Grids consist of an aggregation of data and computing resources, which can be co-allocated to execute large data-intensive tasks which cannot be handled by an individual resource. The vast amounts of (possibly remote) data these tasks process give rise to the need for a high-capacity network interconnecting the various resources. In this paper, it is argued that, in order

View PDFchevron_right
Performance Measurement and Management for Two-Level Optimization of Networks and Peer-to-Peer Applications
Joseph Sventek

2008

interests include programmable networks, embedded systems, closed-loop network management, and distributed system architectures. He has several publications on these topics, as well as holds four patents (with three other patents pending) in these particular areas. He has been the general chair for TINA99 and Middleware 2001, programme chair for COOTS98, TINA99, and Middleware 2000, and a member of programme committees too numerous to mention. He is an advisor to the TeleManagement Forum Board, is an adviser to the Wiley Series in Communications Networking and Distributed Systems, and was on the editorial board of the IEE/BCS/IOP Distributed System Engineering Journal.

View PDFchevron_right
Scheduling And Resource Allocation In Autonomous Service Networks
Neal Sample

2005

View PDFchevron_right
KOALA: a co-allocating grid scheduler
Hashim Mohamed

Concurrency and Computation: Practice and Experience, 2008

In multicluster systems, and more generally in grids, jobs may require co-allocation, that is, the simultaneous or coordinated access of single applications to resources of possibly multiple types in multiple locations managed by different resource managers. Co-allocation presents new challenges to resource management in grids, such as locating sufficient resources in geographically distributed sites, allocating and managing resources in multiple, possibly heterogeneous sites for single applications, and coordinating the execution of single jobs at multiple sites. Moreover, as single jobs now may have to rely on multiple resource managers, co-allocation introduces reliability problems. In this paper, we present the design and implementation of a co-allocating grid scheduler named KOALA that meets these co-allocation challenges. In addition, we report on the results of an analysis of the performance in our multicluster testbed of the co-allocation policies built into KOALA. We also include the results of a performance and reliability test of KOALA while our testbed was unstable.

View PDFchevron_right
Scheduling Algorithms for Grid Computing: State of the Art and Open Problems
Gustavo Sla

Thanks to advances in wide-area network technologies and the low cost of computing resources, Grid computing came into being and is currently an active research area. One motivation of Grid computing is to aggregate the power of widely distributed resources, and provide non-trivial services to users. To achieve this goal, an efficient Grid scheduling system is an essential part of the Grid. Rather than covering the whole Grid scheduling area, this survey provides a review of the subject mainly from the perspective of scheduling algorithms. In this review, the challenges for Grid scheduling are identified. First, the architecture of components involved in scheduling is briefly introduced to provide an intuitive image of the Grid scheduling process. Then various Grid scheduling algorithms are discussed from different points of view, such as static vs. dynamic policies, objective functions, applications models, adaptation, QoS constraints, strategies dealing with dynamic behavior of resources, and so on. Based on a comprehensive understanding of the challenges and the state of the art of current research, some general issues worthy of further exploration are proposed. 4 infrastructure (e.g., it is not included in the Globus Toolkit [125], the defacto standard in the Grid computing community), there is no doubt that such a scheduling component is crucial for harnessing the potential of Grids as they are expanding quickly, incorporating resources from supercomputers to desktops. Our discussion on scheduling algorithms is based on the assumption that there are such schedulers in a Grid.

View PDFchevron_right
A hybrid branch-and-bound and evolutionary approach for allocating strings of applications to heterogeneous distributed computing systems
Edwin K P Chong

Providing efficient workload management is an important issue for a large-scale heterogeneous distributed computing environment where a set of periodic applications is executed. The considered shipboard distributed system is expected to operate in an environment where the input workload is likely to change unpredictably, possibly invalidating a resource allocation that was based on the initial workload estimate. The tasks consist of multiple strings, each made up of an ordered sequence of applications. There is a quality of service (QoS) minimum throughput constraint that must be satisfied for each application in a string, and a maximum utilization constraint that must be satisfied on each of the hardware resources in the system. The challenge, therefore, is to efficiently and robustly manage both computation and communication resources in this unpredictable environment to achieve high performance while satisfying the imposed constraints. This work addresses the problem of finding a robust initial allocation of resources to strings of applications that is able to absorb some level of unknown input workload increase without rescheduling. The proposed hybrid two-stage method of finding a near-optimal allocation of resources incorporates two specially designed mapping techniques: (1) the Permutation Space Genitor-Based heuristic, and (2) the follow-up Branch-and-Bound heuristic based on an Integer Linear Programming (ILP) problem formulation. The performance of the proposed resource allocation method is evaluated under different simulation scenarios and compared to an iteratively computed upper bound. and Technology Center (ISTeC), a universitywide organization for promoting, facilitating, and enhancing CSU's research, education, and outreach activities pertaining to the design and innovative application of computer, communication, and information systems. From 1976 to 2001, he was a professor at Purdue University. Professor Siegel is a Fellow of the IEEE and a Fellow of the ACM. He received two B.S. degrees from the Massachusetts Institute of Technology (MIT), and the M.A., M.S.E., and Ph.D. degrees from Princeton University. He has coauthored over 340 technical papers. His research interests include heterogeneous parallel and distributed computing, parallel algorithms, and parallel machine interconnection networks. He was a Coeditor-in-Chief of the Journal of Parallel and Distributed Computing and was on the Editorial Boards of both the IEEE Transactions on Parallel and Distributed Systems and the IEEE Transactions on Computers. He has been an international keynote speaker and tutorial lecturer and has consulted for industry and government. For more information, see www.engr.colostate.edu/∼hj.

View PDFchevron_right
Application-Oriented Scheduling in Multicluster Grids
Ömer Sönmez

2010

Grid computing appeared in the mid 1990s with the vision of sharing geographically dispersed large computational resources for executing computation-intensive scientific applications. Today, we can name numerous grid projects that run successfully to solve challenging scientific problems such as the grid project of European Organization for Nuclear Research (CERN), which combines thousands of computers worldwide (over 200 sites in about

View PDFchevron_right
Self-adjustment of resource allocation for grid applications
Fabrizio Granelli

Computer Networks, 2008

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / c o m n e t 2. Procedure for self-adjustment of resource allocation

View PDFchevron_right

References (129)

  1. Nachiket Acharya, Chaitanya Chokkaredd, Pinkeshkumar Desai, Ra- jesh Devrajan, Michael P. Frank, SriramKumar Nallan Chakravarthul, Murali Nagendranath, Pradeep Padala, Heeyong Park, Gargi Sur, Mark Tobias, Chi-Kin Wong, and BongJu Yu. The open computation ex- change & auctioning network (ocean). Technical report, Department of Computer & Information Science & Engineering, University of Florida, 2001.
  2. Deutsche Boerse AG. Europe's premier listing platform. Fact sheet, Deutsche Boerse AG, 2005.
  3. Eytan Adar and Bernardo A. Huberman. Free riding on gnutella. First Monday, 5(10), 2000. [AHL + 96] W. Brian Arthur, John H. Holland, Blake LeBaron, Richard Palmer, and Paul Tayler. Asset pricing under endogenous expectations in an artificial stock market. Technical report, Santa Fe Institute, December 1996.
  4. Lawrence M. Ausubel and Paul Milgrom. Ascending auctions with package bidding. Frontiers of Theoretical Economics, 1(1):1-43, 2002.
  5. R. Axelrod. The Evolution of Cooperation. Basic, New York, 1984.
  6. Rob Axtell. The complexity of exchange. Technical report, Society for Computational Economics, March 1999.
  7. Rajkumar Buyya, David Abramson, Jonathan Giddy, and Heinz Stockinger. Economic models for resource management and schedul- ing in grid computing. The Journal of Concurrency and Computation: Practice and Experience, 14(13-15):1507-1542, 2002.
  8. Rajkumar Buyya, David Abramson, and Srikumar Venugopal. The grid economy. In Proceedings of the IEEE, Grid Computing, 2004. [BBC + 04] Andy Bavier, Mic Bowman, Brent Chun, David Culler, Scott Karlin, Steve Muir, Larry Peterson, Timothy Roscoe, Tammo Spalink, and Mike Wawrzoniak. Operating systems support for planetary-scale net- work services. In Proceedings of the First Symposium on Networked Systems and Design and Implementation, San Fransisco, 2004.
  9. T.A. Byrd, K.L. Cossick, and R.W. Zmud. A synthesis of research on requirements analysis and knowledge acquisition techniques. MIS Quarterly, 16(1):117-138, 1992.
  10. Shantanu Biswas. Design of Iterative Mechanisms for Combinatorial Auctions and Exchanges. PhD thesis, Computer Science & Automa- tion, Indian Institute of Science, Bangalore 560 012 (India), 2004.
  11. Rajkumar Buyya and Manzur Murshed. Gridsim: A toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. The Journal of Concurrency and Com- putation: Practice and Experience (CCPE), 14(13-15), 2002.
  12. Shantanu Biswas and Y. Narahari. Iterative dutch combinatorial auc- tions. To appear in: Special issue of Annals of Mathematics and Arti- ficial Intelligence on the Foundations of Electronic Commerce, 2003.
  13. Sushil Bikhchandani and Josep M. Ostroy. The package assignment model. Journal of Economic Theory, 107:377-406, 2000.
  14. Miroslav Budimir. Informationsverarbeitung auf Wertpapiermärk- ten: Neue Marktformen, Design und experimentelle Evaluation. PhD thesis, Lehrstuhl für Informationsbetriebswirtschaftslehre, Universität Karlsruhe, 2003. [CAB + 94] Derek Coleman, Patrick Arnold, Stephanie Bodoff, Chris Dollin, He- lena Gilchrist, Fiona Hayes, and Paul Jeremaes. Object-Oriented De- velopment -The FUSION Method. Prentice Hall, Englewood Cliffs, NJ, 1994.
  15. Madhu Chetty and Rajkumar Buyya. Weaving computational grids: How analogous are they with electrical grids? Computing -Science and Engineering, 4(4):61-71, 2002.
  16. Karl Czajkowski, Ian Foster, and Carl Kesselman. Resource and Ser- vice Management, volume 2, chapter 18, pages 259-283. Elsevier, 2004. In: The Grid 2 -Blueprint for a New Computing Infrastructure. CATNETS/2005/D1/v1.0 Revised Date
  17. Brian F. Cooper and Hector Garcia-Molina. Bidding for storage space in a peer-to-peer data preservation system. In Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02), page 372. IEEE Computer Society, 2002.
  18. Edmund Chattoe. Just how (un)realistic are evolutionary algorithms as representations of social processes? Journal of Artificial Societies and Social Simulation, 1(3), 1998.
  19. Henri Casanova, Graziano Obertelli, Francine Berman, and Rich Wol- ski. The apples parameter sweep template: user-level middleware for the grid. In Proceedings of the 2000 ACM/IEEE conference on Super- computing. IEEE Computer Society, 2000.
  20. Wolfram Conen. Economically coordinated job shop scheduling and decision point bidding -an example for economic coordination in man- ufacturing and logistics. In Proceedings of the 16th Workshop on Pla- nen, Scheduling und Konfigurieren, Entwerfen, 2002.
  21. Peter Cramton. Electricity market design: The good, the bad, and the ugly. Proceedings of the Hawaii International Conference on System Sciences, 2003.
  22. D. Chatterjee and W. Samuelson. Bargaining under incomplete infor- mation. Operations Research, 31(5):835-851, 1983.
  23. K. Czajkowski and V. Sander. Grid resource management protocol: Requirements. Ggf scheduling work group proposal, University of Southern California, John von Neumann Institut for Computing, 2001.
  24. Andreu Mas Colell, Mark D. Whinston, and John R. Green. Microeco- nomic Theory. Oxford University Press, 1995.
  25. P. Coad and E. Yourdon. Object-Oriented Analysis. Prentice-Hall, Englewood Cliffs, N.J, 1991.
  26. P. Coad and E. Yourdon. Object-Oriented Design. Prentice-Hall, En- glewood Cliffs, N.J, 1991.
  27. Alan R Dennis, Glenda S Hayes, and Robert M. Daniels Jr. Business process modeling with group support systems. Journal of Management Information Systems, 15(4):115-142, 1999.
  28. D. Duxbury. Experimental asset markets within finance. Journal of Economic Surveys, 9(4):331-371, 1995.
  29. Carsten Ernemann, Volker Hamscher, and Ramin Yahyapour. Eco- nomic scheduling in grid computing. In Proceedings of the 8th Interna- tional Workshop on Job Scheduling Strategies for Parallel Processing, pages 128-152. Springer-Verlag, 2002.
  30. D. Easley and J. Ledyard. Theories of price formation and exchange in double oral auctions. In Daniel Friedman and John Rust, editors, The Double Auction Market: Institutions, Theory, and Evidence. Perseus Publishing, Cambridge, MA, 1993.
  31. T. Eymann, M. Reinicke, O. Ardaiz, P. Artigas, L. Díaz de Cerio, F. Freitag, R. Messeguer, and D. Royo L. Navarro. Decentralized vs. centralized economic coordination of resource allocation in grids. In Proceedings of the 1st European Across Grids Conference February, 13th-14th, 2003. Universidad de Santiago de Compostela, Spain, 2003.
  32. Geraldine Fennell and G. M. Allenby. Market definition, segmentation, and positioning: An integrated approach. Working paper, 2003.
  33. Thomas Friese, Bernd Freisleben, Steffen Rusitschka, and Alan Southall. A framework for resource management in peer-to-peer net- works. In Mehmet Aksit, Mira Mezini, and Rainer Unland, editors, NetObjectDays, volume 2591 of Lecture Notes in Computer Science, pages 4-21. Springer, 2002.
  34. I. Foster, J. Gieraltowski, S. Gose, N. Maltsev, E. May, A. Rodriguez, D. Sulakhe, and the other Grid2003 Project Members. The grid2003 production grid:principles and practice. In Proceedings of the Thir- teenth IEEE International Symposium on High-Performance Distrib- uted Computing (HPDC13), 2004.
  35. Ian Foster and Carl Kesselman. The Grid 2 -Blueprint for a New Computing Infrastructure, volume 2. Elsevier, 2004.
  36. D Fudenberg and David K. Levine. The Theory of Learning in Games. MIT Press, Cambridge, MA, 1999.
  37. D. Friedman. The Double Auction Market -Institutions, Theories, and Evidence, chapter The Double Auction Market Institution: A Survey, pages 3-26. Cambridge MA, Perseus Publishing, 1991.
  38. Drew Fudenberg and Jean Tirole. Game Theory. The MIT Press, Cam- bridge, MA and London, England, 7th edition edition, 2000. [FTF + 01] J. Frey, T. Tannenbaum, I. Foster, M. Livny, and S. Tuecke. Condor-G: A Computation Management Agent for Multiinstitutional Grids. In In- ternational Symposium on High Performance Distributed Computing, pages 55-67, San Francisco, CA, 2001.
  39. Jerry Green and Jean-Jacques Laffont. Characterization of satisfactory mechanisms for the revelation of preferences for public goods. Econo- metrica, 45(2):427-438., 1977.
  40. Jacob K. Goeree and Theo Offerman. Efficiency in auc- tions with private and common values: An experimental study. American Economic Review, 92(3):625-643, 2002. available at http://ideas.repec.org/a/aea/aecrev/v92y2002i3p625-643.html.
  41. Peter Gradwell. Distributed combinatorial resource scheduling. Dis- cussion paper, Department of Computer Science, University of Bath, 2004.
  42. S. Gode and K. Sunders. Lower bounds for efficiency or surplus ex- traction in double auctions. In David Friedman and John Rust, editors, The Double Action Market: Institution, Theories , and evidence, vol- ume 15 of Santa Fe Institute Series in The Science of Complexity, pages 199-219. SFI, Addison Wedsley, 1993.
  43. M. Grunenberg, D. Veit, and C. Weinhardt. Elektronische fi- nanzmärkte und bundle trading. In Proceedings der 66. wis- senschaftlichen Jahrestagung des Verbandes der Hochschullehrer für Betriebswirtschaft,Graz, pages 310-313, 2004.
  44. F.A. Hayek. The use of knowledge in society. American Economic Review, 35:519-30, 1945.
  45. Karen Holtzblatt and Hugh R. Beyer. Requirements gathering: the human factor. Communications of the ACM, 38(5):31 -32, 1995.
  46. Carsten Holtmann. Organisation von Maerkten -Market Engineering fuer den Wertpapierhandel. PhD thesis, Economics and Business En- gineering. Karlsruhe, Germany, University of Karlsruhe (TH), 2004.
  47. J. D. Hlavacek and N. M. Reddy. Identifying and qualifying industrial market segments. European Journal of Marketing, 20(2):8-21, 1986.
  48. Cengiz Haksever, Barry Render, Roberta S. Russell, and Robert G. Murdick. Service Management and Operations. Prentice-Hall, Upper Saddle River, NJ, 2000.
  49. Leonid Hurwicz. On informationally decentralized systems. In C.B. McGuire and R. Radner, editors, Decision and Organization. North- Holland Publishing Company, Amsterdam, NL, 1972.
  50. L. Hurwicz. The design of mechanisms for resource allocation. Amer- ican Economic Review, 69(2):395-402, 1973.
  51. Adriana Iamnitchi and Ian T. Foster. On fully decentralized resource discovery in grid environments. In Proceedings of the Second Inter- national Workshop on Grid Computing, pages 51-62. Springer-Verlag, 2001.
  52. Matthew O. Jackson. Encyclopedia of Life Support Systems (EOLSS), chapter Mechanism theory. Eolss Publishers, Oxford UK, 2002.
  53. Matthew O. Jackson. Mechanism theory. In Encyclopedia of Life Sup- port Systems (EOLSS), Oxford ,UK, 2002. Eolss Publishers.
  54. Thomas O. Jones and Earl W. Sasser. Why satisfied customers defect. Harvard Business Review, 73(6):88-94, 1995.
  55. Rahul Jain and Pravin Varaiya. Combinatorial exchange mechanisms for efficient bandwidth allocation. Communications in Information and Systems, 3(4):305-324, 2004.
  56. Klaus Krauter, Rajkumar Buyya, and Muthucumaru Maheswaran. A taxonomy and survey of grid resource management systems. Interna- tional Journal of Software: Practice and Experience, 32(2):135-164, 2002.
  57. Chris Kenyon and Giorgos Cheliotis. Architecture requirements for commercializing grid resources. In Proceedings of the 11 th IEEE International Symposium on High Performance Distributed Comput- ing HPDC-11 20002 (HPDC'02), page 215. IEEE Computer Society, 2002.
  58. H. Lim Choi Keung, Justin R.D. Dyson, Stephen A. Jarvis, and Gra- ham.
  59. R Nudd. Performance evaluation of a grid resource monitoring and discovery service. IEEE Proceedings -Software, 150(4):243-251, 2003.
  60. Jayant Kalagnanam and David C. Parkes. Auctions, Bidding and Ex- change Design. Kluwer Academic Publishing, 2003.
  61. Vijay Krishna. Auction theory. Academic Press, San Diego, 2002.
  62. Ramayya Krishnan, Michael D. Smith, Zhulei Tang, and Rahul Telang. The impact of free-riding on peer-to-peer networks. In Proceedings of the 37th Hawaii International Conference on System Sciences, 2004.
  63. J. Ledyard. The design of coordination mechanisms and organiza- tional computing. Journal of Organizational Computing, 3(1):121- 134, 1993.
  64. Edna Tusak Loehman and D. Marc Kilgour. Introduction: Social design for environmental and resource management. In Edna Tusak Loehman and D. Marc Kilgour, editors, Designing Institutions for En- vironmental and Resource Management, New Horizons in Environ- mental Economics, pages 1-25. Edward Elgar, Cheltenham, UK, 1998.
  65. C. Loeser. Demand forecasting in p2p and grid systems. In Proceed- ings of the 10th International Conference on Cybernetics and Informa- tion Technologies, Systems and Applications (CITSA-2004), Orlando, Florida, 2004.
  66. L. Daniel Maxim, Daniel E. Cullen, and John G. Mardo. Optimal acceptance test plans with grouping. Technometrics, 17(3):315-320, 1975.
  67. Paul Milgrom. Auctions and bidding: A primer. Journal of Ecnomic Perspectives, 108(2):245-272, 1989.
  68. Paul Milgrom. Putting Auction Theory to Work. Cambridge University Press, 2004.
  69. Preston R. McAfee and John McMillan. Auctions and bidding. Journal of Economic Literature, 25(2):699-728, 1987.
  70. R.P. McAfee and J. McMillan. Auctions and bidding. Journal of Eco- nomic Literatur, 25(2):699-738, 1987.
  71. Jeffrey MacKie-Mason, A. Osepayshvili, D.M. Reeves, and M. P. Wellman. Price prediction strategies for market-based scheduling. In International Conference on Automated Planning and Scheduling,, page forthcoming, 2004.
  72. Richard D. McKelvey, Andrew McLennan, and Theodore Turocy. Gambit graphics user interface -an interactive extensive form game program. Documentation, 2000.
  73. K.A. McCabe, S. Rassenti, and V. Smith. Designing a uniform-price double auction: An experimental evaluation. In Daniel Friedman and J. Rust, editors, The Double Auction Market: Institutions, Theory, and Evidence, pages 307-332. Perseus Publishing, Cambridge, MA, 1993.
  74. Robert B. Myerson and Mark A. Satterthwaite. Efficient mechanisms for bilateral trading. Journal of Economic Theory, 28:265-281, 1983.
  75. Merriam-Webster. Merriam-webster online. http://www.m-w.com/, 2002.
  76. H. Mizuta and Y. Yamagata. Agent-based simulation and greenhouse gas emissions trading. In B.A. Peters, J.S. Smith, D.J. Medeiros, and M.W. Rohrer, editors, Proceedings of the 2001 Winter Simulation Con- ference, pages 535-540, 2001.
  77. Dirk Neumann. Knowledge-based auction design. Working paper, 2004.
  78. Dirk Neumann. Market Engineering -A Structured Design Process for Electronic Markets. PhD thesis, Economics and Business Engineering. Karlsruhe, Germany, University of Karlsruhe (TH), 2004.
  79. N. Nisan, S. London, O. Regev, and N. Camiel. Globally distributed computation over the internet -the popcorn project. In Proceedings of the 18th International Conference on Distributed Computing Systems, 26 -29 May, 1998, Amsterdam, The Netherlands. IEEE Computer So- ciety, 1988.
  80. Noam Nisan and Ilya Segal. The communication complexity of effi- cient allocation problems. Unpublished Paper, November 2001.
  81. James Odell and Martin Fowler. Advanced object-oriented analysis and design using UML. Cambridge Univ. Press, Cambridge, 1999.
  82. Shmuel S. Oren. Market based risk mitigation: Risk management vs. risk avoidance. In White House OSTP/NSF Workshop on Critical In- frastructure Interdependencies, Washington DC, 2001.
  83. David Christopher Parkes. ibundle: An efficient ascending price bun- dle auction. In Proceedings of the 1st ACM conference on Electronic commerce (EC99), pages 148-157. ACM Press, 1999.
  84. David Christopher Parkes. Iterative Combinatorial Auctions: Achiev- ing Economic and Computational Efficiency. PhD thesis, Department of Computer and Information Science, University of Pennsylvania, 2001.
  85. David Christopher Parkes. Iterative Combinatorial Auctions: Achiev- ing Economic and Computational Efficiency. PhD thesis, Department of Computer and Information Science, University of Pennsylvania, 2001.
  86. G. Pahl and W. Beitz. Engineering Design. The Pitman Press, Bath, UK, 1984. [PHP + 03] Pradeep Padala, Cyrus Harrison, Nicholas Pelfort, Erwin Jansen, Michael Frank, and Chaitanya Chokkareddy. Ocean: The open com- putation exchange and arbitration network, a market approach to meta computing. In In Proceedings of the International Symposium on Par- allel and Distributed Computing 2003, 2003.
  87. David C. Parkes, Jayant Kalagnanam, and Marta Eso. Achieving budget-balance with vickrey-based payment schemes in exchanges. In Proceedings of the Seventeenth International Joint Conference on Ar- tificial Intelligence, pages 1161-1168, 2001.
  88. Charles Plott. Market architectures, institutional landscapes and test- bed experiments. Economic Theory, 4(1):3-10, 1994.
  89. Tony Curzon Price. Using co-evolutionary programming to simulate strategic behavior in markets. Journal of Evolutionary Economics, 7:219-254, 1997.
  90. J. Rumbaugh, Ivar Jacobson, and Grady Booch. The Unified Modeling Language Reference Model. Addison-Wesley, Reading, MA, 1999.
  91. Ori Regev and Noam Nisan. The popcorn market Ů an online market for computational resources. In Proceedings of the first international conference on Information and computation economies, pages 148- 157. ACM Press, 1998.
  92. Ali Roumani and David Skillicorn. Large-scale resource selection in grids. In Proceedings of the First International Workshop on Grid Computing and its Application to Data Analysis, 2004.
  93. A. Rustichini, M. Satterthwaite, and S.R. Williams. Convergence to efficiency in a simple market with incomplete information. Economet- rica, 62(5):1041-1063, 1994.
  94. Aldo Rustichini, Mark A Satterthwaite, and Steven R Williams. Convergence to efficiency in a simple market with incomplete in- formation.
  95. Econometrica, 62(5):1041-63, 1994. available at http://ideas.repec.org/a/ecm/emetrp/v62y1994i5p1041-63.html.
  96. Daniel M. Reeves, M. P. Wellman, Jeffrey MacKie-Mason, and A. Os- epayshvili. Exploring bidding strategies for market-based scheduling. Decision Support Systems, forthcoming, 2004.
  97. Tuomas Sandholm. Multiagent Systems: A Modern Introduction to Distributed Artificial Intelligence, volume 1, chapter 5, Distributed Ra- tional Decision Making, pages 201-258. MIT Press, 1999.
  98. Seti@Home. User statistics. Website, accessed: 12.02.2005, Seti @ Home website (http://setiathome.ssl.berkeley.edu/numusers.html), 2005.
  99. K. Steiglitz, M.L. Honig, and L.M. Cohen. A computational market model based on individual action. In Scott Clear- water, editor, Market-Based Control: A Paradigm for Distrib- uted Resource Allocation, Hong Kong, 1996. World Scientific. http://www.cs.princeton.edu/ ken/scott.ps (abgerufen am 11.02.2002).
  100. G. Lynn Shostack. How to design a service. European Journal of Marketing, 16(1):49-63, 1982.
  101. G. Lynn Shostack. Designing services that deliver. Harvard Business Review, 62(1):133-139, 1984.
  102. W. Smith, I.Foster, and V. Taylor. Scheduling with advanced reser- vations. In Proceedings of the International Parallel and Distributed Processing Symposium (IPDPS) Conference, 2000.
  103. Eberhard E. Scheuning and Eugene M. Johnson. A proposed model for new service development. Journal of Services Marketing, 3(2):25-34, 1989.
  104. Wendell R. Smith. Product differentiation and market segmentation as alternative marketing strategies. Journal of Marketing, 21(1):3-8, 1956. CATNETS/2005/D1/v1.0 Revised Date
  105. Veron Smith. Theory, experiment and economics. Journal of Economic Perspective, 72(5):151-169, 1989.
  106. V. Smith. Economics in the laboratory. Journal of Economic Perspec- tives, 8(1):113-131, 1994.
  107. Kumaran Subramoniam, Muthucumaru Maheswaran, and Michel Toulouse. Towards a micro-economic model for resource allocation in grid computing systems. In Proceedings of the 2002 IEEE Cana- dian Conference on Electrical & Computer Engineering, 2002.
  108. Björn Schnizler, Dirk Neumann, and Christof Weinhardt. Resource allocation in computational grids -a market engneering approach. In Proceedings of the WeB 2004, Washington, pages 19-31, 2004.
  109. Ian Sommerville. Software Engineering. Pearson Studium, München, 6 edition, 2001.
  110. Tuomas Sandholm, Subhash Suri, Andrew Gilpin, and David Levine. Winner determination in combinatorial auction generalizations. In Proceedings of the first international joint conference on Autonomous agents and multiagent systems, pages 69-76. ACM Press, 2002.
  111. Katrina Stanoevska-Slabeva and Beat F. Schmid. Requirements analy- sis for community supporting platforms based on the media reference model. Electronic Markets, 10(4):250-257, 2000.
  112. M. Satterthwaite and S.R. Williams. The optimality of a simple market mechanism. Econometrica, 70:1841-1863, 2002.
  113. L. Tesfatsion. Agent-based computational economics: Growing economies from the bottom-up. Isu economics working paper no. 1, Department of Economics, Iowa State University, Ames, February 2002.
  114. D. S. W. Tansley and C. C. Hayball. Knowledge-based systems analysis and design : a KADS developer's handbook. Prentice Hall, New York, 1993.
  115. Srikumar Venugopal, Rajkumar Buyya, and Lyle Winton. A grid ser- vice broker for scheduling distributed data-oriented applications on global grids. Technical report, grids-tr-2004-1, University of Mel- bourne, Australia, 2004.
  116. Daniel Veit. Matchmaking in Electronic Markets. Springer Verlag, 2003.
  117. R. Wolski, J. Brevik, J. S. Plank, and T. Bryan. Grid resource allo- cation and control using computational economies, chapter 32, pages 747-772. Number ISBN: 0-470-85319-0. John Wiley & Sons, 2003. Booktitle: Grid Computing: Making The Global Infrastructure a Real- ity.
  118. Ilka Weber, Clemens Czernohous, and Christof Weinhardt. Simulation of ending rules in online auctions. In Stefan Klein, editor, Proceed- ings of the 11th Research Symposium on Emerging Electronic Markets (RSEEM), University College Dublin, Ireland, 2004.
  119. C.A. Waldspurger, T. Hogg, B.A. Huberman, J.O. Kephart, and W.S. Stornetta. Spawn: A distributed computational economy. IEEE Trans- actions on Software Engineering, 18(2):103-117, 1992.
  120. Christof Weinhardt, Carsten Holtmann, and Dirk Neumann. Market engineering. Wirtschaftsinformatik, 45(6):635-640, 2003.
  121. R. Wilson. Incentive efficiency of double auctions. Econometrica, 53(5):1101-1116, 1985.
  122. Michael P. Wellman, Jeffrey MacKie-Mason, Daniel M. Reeves, and Sowmya Swaminathan. Exploring bidding strategies for market-based scheduling. In 4th ACM conference on Electronic commerce, pages 115 -124, San Diego, CA, USA, 2003. ACM Press.
  123. Rich Wolski. Dynamically forecasting network performance using the network weather service. Cluster Computing, 1(1):119-132, 1998.
  124. R. Wolski, J. Plank, J. Brevik, and T. Bryan. G-commerce: Market for- mulations controlling resource allocation on the computational grid. In Proceedings of the International Parallel and Distributed Processing Symposium (IPDPS), 2001.
  125. R. Wolski, J.S. Plank, J. Brevik, and T. Bryan. Analyzing market-based resource allocation strategies for the computational grid. International Journal of High Performance Computing Applications, 15(3):258- 281, 2001.
  126. Peter R. Wurman, Michael P. Wellman, and William E. Walsh. A para- metrization of the auction design space. Games and Economic Behav- ior, 35(1-2):304-338, 2001.
  127. CATNETS/2005/D1/v1.0 Revised Date
  128. Michael P. Wellman, William E. Walsh, Peter R. Wurman, and Jef- frey K. MacKie-Mason. Auction protocols for decentralized schedul- ing. Games and Economic Behavior, 35:271-303, 2001.
  129. R.W. Zmud. Information Systems in Organizations. Scott, Foresman and Company, Glenview, IL, 1983.

Related papers

Characterizing Resource Allocation Heuristics for Heterogeneous Computing Systems
Shoukat Ali

Advances in Computers, 2005

View PDFchevron_right
Optimal application-oriented resource brokering in a high performance computing Grid
Vincent Keller

2008

In the world of High Performance Computing (newly renamed "High Productivity Computing"), where the race for performance is on, where the hunt for the last Flop rages and where the developers swear by the Cult of Power, the different applications have different needs. Some of them are single threaded, others are parallel, some are memory bandwidth, others processor speed dominated. Some are embarrassingly parallel, point-to-point or multicast dominated, all depending on the algorithms and numerical methods they implement. Computing resources are also different, some are low-cost clusters of PlayStations 3, clusters with more or less efficient networks, others are expensive vector supercomputers. To express that diversity, a parameterization of the applications and the resources is proposed. Based on this parameterization, it is possible to quantify the needs of the different types of applications in terms of memory, processor and network requirements. On the other hand, the parameterization of the resources quantifies what a resource "offers" in term of memory, processor or network performance. Comparing the needs of the application and the offers of the resources, we present a model that predicts the computation and the communication times, and consequently the execution times of an application on different resources. We define a cost model that finds the best matches between an application and the available resources in an HPC Grid. It takes into account the cost of execution time of the application (based on the prediction previously mentioned), the waiting time cost (based on the information given by the local resource management systems), the cost of the licenses of the different software the application may use, the cost of the data transfer, and the ecological cost. The choice is made on a QOS (Quality of Service) given by the user, such as minimum turnaround time or minimum cost. Computing these different costs requires a fine knowledge of the behavior of the application on the resource. Thus we present a detailed application-oriented monitoring that consists in mapping information from the hardware monitoring and the different middleware the application (or the resource) uses, such as MPI detailed information, the hardware counters or the information of the execution on a local resource management system. The monitored data is stored in an archive system. The archived monitoring data concerning the behavior of the applications on the different resources, can then be used to help deciders purchasing new best suited resources for the current (and future) applications. In the second part of this work, we go further in matching the applications and resources: if it is possible to predict the behavior of an application on a given resource, it is also possible to fit the resource to the needs of the application. Knowing the processor performance needs of the application during its execution, it is possible to adjust its frequency through the frequency stepping mechanism that authorizes an operating system kernel or a program to modify on the fly the frequency (and the voltage) of the present days processors. Hence the resource exactly fits the needs of the application and the energy consumption of the processor is reduced. Finally, we propose a methodology to determine poorly implemented applications and thus giving users and developers hints and advice to detect flaws in an automatic manner. We present a real-life example, a spectral element code in CFD, for which the complexity changed when more than 1024 processors were used.

View PDFchevron_right
Quality of Service Scheduling of Computation and Communication Resources in Grid Networks
Manos Varvarigos

Grids offer a transparent interface to geographically scattered computation, communication, storage and other resources. In this chapter we propose and evaluate QoS-aware and fair scheduling algorithms for Grid Networks, which are capable of optimally or near-optimally assigning tasks to resources, while taking into consideration the task characteristics and QoS requirements. We categorize Grid tasks according to whether or not they demand hard performance guarantees. Tasks with one or more hard requirements are referred to as Guaranteed Service (GS) tasks, while tasks with no hard requirements are referred to as Best Effort (BE) tasks. For GS tasks, we propose scheduling algorithms that provide deadline or computational power guarantees, or offer fair degradation in the QoS such tasks receive in case of congestion. Regarding BE tasks our objective is to allocate resources in a fair way, where fairness is interpreted in the max-min fair share sense. Though, we mainly address scheduling problems on computation resources, we also look at the joint scheduling of communication and computation resources and propose routing and scheduling algorithms aiming at co-allocating both resource type so as to satisfy their respective QoS requirements.

View PDFchevron_right
SCHEDULING IN VIRTUAL INFRASTRUCTURE FOR HIGHTHROUGHPUT COMPUTING
R. Lakshman Naik

For the execution of the scientific applications, different methods have been proposed to dynamically provide execution environments for such applications that hide the complexity of underlying distributed and heterogeneous infrastructures. Recently virtualization has emerged as a promising technology to provide such environments. Virtualization is a technology that abstracts away the details of physical hardware and provides virtualized resources for high-level scientific applications. Virtualization offers a cost-effective and flexible way to use and manage computing resources. Such an abstraction is appealing in Grid computing and Cloud computing for better matching jobs (applications) to computational resources. This work applies the virtualization concept to the Condor dynamic resource management system by using Condor Virtual Universe to harvest the existing virtual computing resources to their maximum utility. It allows existing computing resources to be dynamically provisioned at run-time by users based on application requirements instead of statically at design-time thereby lay the basis for efficient use of the available resources, thus providing way for the efficient use of the available resources.

View PDFchevron_right
Special Issue on Algorithms for the Resource Management of Large Scale Infrastructures
Claudia Canali

Algorithms, 2018

Modern distributed systems are becoming increasingly complex as virtualization is being applied at both the levels of computing and networking. Consequently, the resource management of this infrastructure requires innovative and efficient solutions. This issue is further exacerbated by the unpredictable workload of modern applications and the need to limit the global energy consumption. The purpose of this special issue is to present recent advances and emerging solutions to address the challenge of resource management in the context of modern large-scale infrastructures. We believe that the four papers that we selected present an up-to-date view of the emerging trends, and the papers propose innovative solutions to support efficient and self-managing systems that are able to adapt, manage, and cope with changes derived from continually changing workload and application deployment settings, without the need for human supervision.

View PDFchevron_right

Related topics

  • Grid Computing
  • Environmental Analysis
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved