Academia.eduAcademia.edu

Outline

Title
Abstract
Data Staging of Grid Jobs
Introduction Background
Scope of Paper
Summary of the Model Features and Parameters
Some Preliminary Results
Problem Best Results
Ongoing and Future Work
ST200 Vliw Compiler Experimental Results
Summary and Conclusions
Depth of Results: Number and Range
Conclusions and Future Work
The Model with Search Procedure for Etjssp
Related Work
Experimental Result
Results
Conclusion and Future Work
Future Work
Data Representation
Constraints Related to Currents
Experimental Results
Conclusion
Introduction
Related Works
Conclusions
References
All Topics
Languages and Linguistics
Other Languages, Societies, and Cultures

VLEPpO: A Visual Language for Problem Representation

Profile image of Dimitris VrakasDimitris Vrakas

2007

Abstract

Service level agreements (SLAs) are powerful instruments for describing all obligations and expectations in a business relationship. It is of focal importance for deploying Grid technology to commercial applications. The EC-funded project HPC4U (Highly Predictable Clusters for Internet Grids) aimed at introducing SLA-awareness in local resource management systems, while the EC-funded project AssessGrid introduced the notion of risk, which is associated with every business contract. This paper highlights the concept of planning based resource management and describes the SLA-aware scheduler developed and used in these projects.

Related papers

Usage SLA-based scheduling in Grids
Ioan Raicu

Concurrency and Computation: Practice and Experience, 2007

Managing usage service level agreements (uSLAs) within environments that integrate participants and resources spanning multiple physical institutions is a challenging problem. Running workloads in such environments is often a similarly challenging problem owing to the scale of the environment, and to the resource partitioning based on various sharing strategies. Also, a resource may be taken down during a job execution, be improperly set up or fail job execution. Such elements have to be taken into account whenever targeting a Grid environment for problem solving. In this paper we explore uSLA-based scheduling on a real Grid, Grid3, by means of a specific workload (the BLAST workload) and a specific scheduling framework, GRUBER (an architecture and toolkit for resource uSLA specification and enforcement). The paper provides extensive experimental results and comparisons with other scheduling strategies. We also address, in great detail, the performance of different uSLA-based site selection strategies and the overall performance in scheduling workloads over Grid3 with workload sizes ranging from 10 to 10 000 jobs. USAGE SLA-BASED SCHEDULING IN GRIDS 959

View PDFchevron_right
Grids and Service-oriented Architectures for Service Level Agreements
Philipp Wieder

2010

, except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.

View PDFchevron_right
A grid-qos decision support system using service level agreements
Ronaldo Salles

2009

The availability of better services and products has become a challenge in all segments, from economics to high technology. This paper proposes an approach to Quality of Service for Grid Computing that involves the analysis of the grid resources and the categorization of types of services into Service Level Agreements (SLAs). This work focuses on the definition of grid indicators to create a Decision Support System that helps to identify the Quality of Service offered by the resources available in the grid, as well as the improvement needed for these services. The system also includes benefits for grid users, such as the quality of the information provided about the grid resources, which allows users to manage and use the available resources in an easy way, improving the system's Quality of Service from the user's point of view.

View PDFchevron_right
GRID SERVICE LEVEL AGREEMENTS Grid Resource Management with Intermediaries
Ian Foster

We present a reformulation of the well-known GRAM architecture based on the Service-Level Agreement (SLA) negotiation protocols defined within the Ser- vice Negotiation and Access Protocol (SNAP) framework. We illustrate how a range of local, distributed, and workflow scheduling mechanisms can be viewed as part of a cohesive yet open system, in which new scheduling strategies and management policies can evolve without disrupting the infrastructure. This ar- chitecture remains neutral to, and in fact strives to mediate, the potentially con- flicting resource, community, and user policies.

View PDFchevron_right
Grid Service Level Agreements Combining Resource Reservation and Predictive Run-time Adaptation
James Padgett

Resource reservation and performance prediction of execution run-times are key components in delivering timely application services for decision support systems. The provision of Service Level Agreements (SLA) and components to manage tasks such as resource negotiation, monitoring and policing are needed to help meet this requirement. This paper presents an SLA management architecture for use in Grid environments focusing on resource negotiation, monitoring and policing strategies for Grid application services. Included are methods for resource negotiation and generating execution run-time predictions for tracking application progress. Experiments to test the prediction model are presented and compared with a reference execution schedule.

View PDFchevron_right
SLA Management and Resource Modelling for Grid Computing
Adrian Li

Current implementations of grids exist in the research and academic communities, where applications are generally characterised as being computationally intensive usually involving large amounts of data. For these purposes and in this environment a 'best effort' resource guarantee is sufficient. However, as grid uses mature in both the academic and commercial arenas, grid providers will require some form of service level management to address these issues. This paper presents a service l evel approach to grid resource management for SLAs, focussing on the management of SLAs and modelling of resources required by these processes.

View PDFchevron_right
Concepts and Algorithms of Mapping Grid-Based Workflow to Resources Within an SLA Context
Dang Ba Quan

Computer Communications and Networks, 2011

With the popularity of Grid-based workflow, ensuring the Quality of Service (QoS) for workflow by Service Level Agreements (SLAs) is an emerging trend in the business Grid. Among many system components for supporting SLAaware Grid-based workflow, the SLA mapping mechanism is allotted an important position as it is responsible for assigning sub-jobs of the workflow to Grid resources in a way that meets the user's deadline and minimizes costs. With many different kinds of sub-jobs and resources, the process of mapping a Grid-based workflow within an SLA context defines an unfamiliar and difficult problem. To solve this problem, this chapter describes related concepts and mapping algorithms.

View PDFchevron_right
A SERVICE-ORIENTED APPROACH TO ENFORCE GRID RESOURCE ALLOCATIONS
Olle Mulmo

International Journal of Cooperative Information Systems, 2006

We present the SweGrid Accounting System (SGAS) -a decentralized and standardsbased system for Grid resource allocation enforcement that has been developed with an emphasis on a uniform data model and easy integration into existing scheduling and workload management software.

View PDFchevron_right
Provision of fault tolerance with grid-enabled and SLA-aware resource management systems
Odej Kao

2005

View PDFchevron_right
Managing service level agreement contracts in OGSA-based Grids
Sotirios Chatzis

Future Generation Computer Systems, 2008

View PDFchevron_right

References (226)

  1. Andrieux, A.; Czajkowski, K.; Dan, A.; Keahey, K.; Ludwig, H.; Nakata, T.; Pruyne, J.; Rofrano, J.; Tuecke, S.; and Xu, M. 2004. Web Services Agree- ment Specification (WS-Agreement). http://www. gridforum.org/Meetings/GGF11/Documents/ draft-ggf-graap-agreement.pdf.
  2. Audsley, N. 1993. Deadline monotonic scheduling theory and application. Control Engineering Practice 1:71-78. Business Experiments in Grid (BeInGrid), EU-funded Project. http://www.beingrid.eu.
  3. Buttazzo, G. C., and Stankovic, J. 1993. Red: A robust ear- liest deadline scheduling algorithm. In 3rd intl. workshop on responsive computing systems.
  4. Czajkowski, K.; Foster, I.; Kesselman, C.; Sander, V.; and Tuecke, S. 2002. SNAP: A Protocol for Nego- tiating Service Level Agreements and Coordinating Re- source Management in Distributed Systems. In D.G. Feit- elson, L. Rudolph, U. S. E., ed., Job Scheduling Strategies for Parallel Processing, 8th InternationalWorkshop, Edin- burgh,.
  5. De Roure (edt.), D. 2006. Future for European Grids: GRIDs and Service Oriented Knowledge Utilities. Techni- cal report, Expert Group Report for the European Commis- sion, Brussel.
  6. Foster, I.; Kesselman, C.; Lee, C.; Lindell, B.; Nahrstedt, K.; and Roy, A. 1999. A Distributed Resource Manage- ment Architecture that Supports Advance Reservations and Co-Allocation. In 7th International Workshop on Quality of Service (IWQoS), London, UK. GGF Open Grid Services Architecture Working Group (OGSA WG). 2003. Open Grid Services Architecture: A Roadmap. Globus Alliance: Globus Toolkit. http://www. globus.org. Highly Predictable Cluster for Internet-Grids (HPC4U), EU-funded project IST-511531. http://www.hpc4u. org. Hovestadt, M.; Kao, O.; Keller, A.; and Streit, A. 2003. Scheduling in HPC Resource Management Systems: Queuing vs. Planning. In Job Scheduling Strategies for Parallel Processing: 9th International Workshop, JSSPP, Seattle, WA, USA.
  7. Jackson, D.; Snell, Q.; and Clement, M. 2001. Core Algorithms of the Maui Scheduler. In D. G. Feitelson and L. Rudolph., ed., Proceddings of 7th Workshop on Job Scheduling Strategies for Parallel Processing, vol- ume 2221 of Lecture Notes in Computer Science, 87-103. Springer Verlag.
  8. Jeffery (edt.), K. 2004. Next Generation Grids 2: Require- ments and Options for European Grids Research 2005- 2010 and Beyond. ftp://ftp.cordis.lu/pub/ ist/docs/ngg2_eg_final.pdf.
  9. Keller, A., and Reinefeld, A. 2001. Anatomy of a resource management system for hpc clusters. Annual Review of Scalable Computing 3:1-31.
  10. Lifka, D. A. 1995. The ANL/IBM SP Scheduling System. In D. G. Feitelson and L. Rudolph., ed., Proc. of 1st Work- shop on Job Scheduling Strategies for Parallel Processing, volume 949 of Lecture Notes in Computer Science, 295- 303. Springer Verlag.
  11. MacLaren, J. 2003. Advanced Reservations -State of the Art. Technical report, GRAAP Working Group, Global Grid Forum, http://www.fz-juelich.de/zam/ RD/coop/ggf/graap/sched-graap-2.0.html.
  12. Mu'alem, A., and Feitelson, D. G. 2001. Utilization, Predictability, Workloads, and User Runtime Estimates in Scheduling the IBM SP2 with Backfilling. In IEEE Trans. Parallel & Distributed Systems 12(6), 529-543. Open Grid Forum. http://www.ogf.org.
  13. Pfister, G. 1997. In Search of Clusters. Prentice Hall. Priol, T., and Snelling, D. 2003. Next Genera- tion Grids: European Grids Research 2005-2010. ftp://ftp.cordis.lu/pub/ist/docs/ngg_ eg_final.pdf.
  14. Sahai, A.; Graupner, S.; Machiraju, V.; and van Moorsel, A. 2002. Specifying and Monitoring Guarantees in Com- mercial Grids through SLA. Technical Report HPL-2002- 324, Internet Systems and Storage Laboratory, HP Labora- tories Palo Alto.
  15. Windisch, K.; Lo, V.; Feitelson, D.; and Nitzberg, B. 1996. A Comparison of Workload Traces from Two Production Parallel Machines. In 6th Symposium Frontiers Massively Parallel Computing, 319-326. References
  16. Broder, S., Final Examination Scheduling, Comm. of the ACM 7 (1964), 494-498.
  17. Brelaz, D., New methods to color the vertices of a graph. Comm. of the ACM 22 (1979), 251-256.
  18. Burke, E.K., Hart, E., Kendall, G., Newall, J., Ross, P. and Schulenburg, S.: Hyperheuristics: an Emerging Di- rection in Modern Search Technology. In: Glover, F. and Kochenberger, G.: Handbook of Metaheuristics, 457- 474, 2003.
  19. Burke, E. K., Kingston, J. H., and de Werra, D., Applica- tions to Timetabling, In: J. L. Gross and J. Yellen (eds.) The Handbook of Graph Theory, Chapman Hall/CRC Press, (2004), 445-474.
  20. Burke, E.K., McCollum, B., Meisels, A., Petrovic, S. and Qu, R.: A Graph-Based Hyper Heuristic for Timetabling Problems. European Journal of Operational Research, 176 (2007) 177-192.
  21. Burke, E.K., Petrovic, S., and Qu R., Case Based Heuris- tic Selection for Timetabling Problems. Journal of Scheduling, 9 (2006) 115-132.
  22. Carter, M. W., A Survey of Practical Applications of Examination Timetabling Algorithms, Operations Re- search 34 (1986), 193-201.
  23. Carter, M. W., Laporte, G., and Lee, S., Examination Timetabling: Algorithmic Strategies and Applications, J. of the Operations Research Society 47 (1996), 373-383.
  24. de Werra, D., An Introduction to Timetabling, Euro. J. Oper. Res. 19 (1985), 151-162.
  25. Kiaer, L., and Yellen, J., Weighted Graphs and Univer- sity Timetabling, Computers and Operations Research Vol. 19, No. 1 (1992a), 59-67.
  26. Kiaer, L., and Yellen, J., Vertex Coloring for Weighted Graphs With Application to Timetabling, Technical Re- port Series -RHIT, MS TR 92-12 (1992b).
  27. Krarup, J., and de Werra, D., Chromatic Optimisation: Limitations, Objectives, Uses, References, Euro. J. Oper. Res. 11 (1982), 1-19.
  28. Mehta, N. K., The Application of a Graph Coloring Method to an Examination Scheduling Problem, Inter- faces 11 (1981), 57-64.
  29. Neufeld, G. A. and Tartar, J., Graph Coloring Conditions for the Existence of Solutions to the Timetable Problem, Comm. of the ACM 17 (1974), 450-453.
  30. Papadimitriou, C. H. and Steiglitz, K., Combinatorial Optimization: Algorithms and Complexity, Prentice- Hall, 1982.
  31. Qu, R., Burke, E.K., McCollum, B., Merlot, L. T. G., and Lee, S. Y., A survey of Search Methodologies and Automated Approaches for Examination Timetabling, Technical Report, NOTT-CS-TR-2006-4 (2006).
  32. Schaerf, A., A Survey of Automated Timetabling, Artifi- cial Intelligence Review 13 (1999), 87-127.
  33. Schmidt, G., and Strohlein, T., Timetable Construction-- an Annotated Bibliography, The Computer Journal 23 (1980), 307-316.
  34. Welsh, D. J. A., and Powell, M. B., An Upper Bound for the Chromatic Number of a Graph and its Application to Timetabling Problems, The Computer Journal 10 (1967), 85-86.
  35. Wood, D. C., A System for Computing University Ex- amination Timetables, The Computer Journal 11 (1968), 41-47. References
  36. Allen, J. 1983. Maintaining knowledge about temporal intervals. Communications of the ACM 26(11):832-843.
  37. Alur, R., and Dill, D. L. 1994. A theory of timed automata. Theor. Comput. Sci. 126(2):183-235.
  38. Bartak, R. 2002. Visopt ShopFloor: On the edge of planning and scheduling. In van Hentenryck, P., ed., Proceedings of the 8th In- ternational Conference on Principles and Practice of Constraint Programming (CP2002), LNCS 2470, 587-602. Springer Verlag.
  39. Cesta, A., and Oddi, A. 1996. DDL.1: A Formal Description of a Constraint Representation Language for Physical Domains. In M.Ghallab, M., and Milani, A., eds., New Directions in AI Planning. IOS Press.
  40. Cesta, A.; Cortellessa, G.; Fratini, S.; Oddi, A.; and Policella, N. 2007. An innovative product for space mission planning -an a posteriori evaluation. In Proceedings of the 17th International Conference on Automated Planning & Scheduling (ICAPS-07).
  41. Cesta, A.; Oddi, A.; and Smith, S. F. 2002. A Constraint-based method for Project Scheduling with Time Windows. Journal of Heuristics 8(1):109-136.
  42. Frank, J., and Jónsson, A. 2003. Constraint-based attribute and interval planning. Constraints 8(4):339-364.
  43. Fratini, S., and Cesta, A. 2005. The Integration of Planning into Scheduling with OMP. In Proceedings of the 2nd Workshop on Integrating Planning into Scheduling (WIPIS) at AAAI-05, Pitts- burgh, USA.
  44. Fratini, S. 2006. Integrating Planning and Scheduling in a Component-Based Perspective: from Theory to Practice. Ph.D. Dissertation, University of Rome "La Sapienza", Faculty of En- gineering, Department of Computer and System Science.
  45. Ghallab, M., and Laruelle, H. 1994. Representation and Control in IxTeT, a Temporal Planner. In Proceedings of the Second Inter- national Conference on Artifial Intelligence Planning Scheduling Systems. AAAI Press.
  46. Jonsson, A.; Morris, P.; Muscettola, N.; Rajan, K.; and Smith, B. 2000. Planning in Interplanetary Space: Theory and Practice. In Proceedings of the Fifth Int. Conf. on Artificial Intelligence Planning and Scheduling (AIPS-00).
  47. Laborie, P. 2003. Algorithms for Propagating Resource Con- straints in AI Planning and Scheduling: Existing Approaches and new Results. Artificial Intelligence 143:151-188.
  48. Muscettola, N.; Smith, S.; Cesta, A.; and D'Aloisi, D. 1992. Co- ordinating Space Telescope Operations in an Integrated Planning and Scheduling Architecture. IEEE Control Systems 12(1):28-37.
  49. Muscettola, N. 1994. HSTS: Integrating Planning and Schedul- ing.
  50. In Zweben, M. and Fox, M.S., ed., Intelligent Scheduling. Morgan Kauffmann.
  51. Smith, D.; Frank, J.; and Jonsson, A. 2000. Bridging the gap be- tween planning and scheduling. Knowledge Engineering Review 15(1):47-83. References
  52. Ali, H. H., and El-Rewini, H. 1992. Scheduling Inter- val Ordered Tasks on Multiprocessor Architecture. In SAC '92: Proceedings of the 1992 ACM/SIGAPP Symposium on Applied computing, 792-797. New York, NY, USA: ACM.
  53. Brucker, P.; Drexl, A.; Möhring, R.; Neumann, K.; and Pesch, E. 1999. Resource-Constrained Project Scheduling: Notation, Classification, Models and Methods. European Journal of Operational Research 112:3-41.
  54. Brucker, P. 2004. Scheduling Algorithms, 4th edition. SpringerVerlag.
  55. Chaudhuri, S.; Walker, R. A.; and Mitchell, J. E. 1994. An- alyzing and Exploiting the Structure of the Constraints in the ILP Approach to the Scheduling Problem. IEEE Trans- actions on VLSI 2(4).
  56. Dupont de Dinechin, B. 2004. From Machine Scheduling to VLIW Instruction Scheduling. ST Journal of Research 1(2).
  57. Dupont de Dinechin, B. 2007. Time-Indexed Formulations and a Large Neighborhood Search for the Resource-Constrained Modulo Scheduling Prob- lem. In 3rd Multidisciplinary International Schedul- ing conference: Theory and Applications (MISTA). http://www.cri.ensmp.fr/classement/2007.html.
  58. Garey, M. R., and Johnson, D. S. 1976. Scheduling Tasks with Nonuniform Deadlines on Two Processors. J. ACM 23(3):461-467.
  59. Jaffe, J. M. 1980. Bounds on the Scheduling of Typed Task Systems. SIAM J. Comput. 9(3):541-551.
  60. Jansen, K. 1994. Analysis of Scheduling Problems with Typed Task Systems. Discrete Applied Mathematics 52(3):223-232.
  61. Kolisch, R., and Hartmann, S. 1999. Algorithms for Solv- ing the Resource-Constrained Project Scheduling Prob- lem: Classification and Computational Analysis. In J., W., ed., Handbook on Recent Advances in Project Scheduling. Kluwer Academic. chapter 7.
  62. Leung, A.; Palem, K. V.; and Pnueli, A. 2001. Schedul- ing Time-Constrained Instructions on Pipelined Proces- sors. ACM Trans. Program. Lang. Syst. 23(1):73-103.
  63. Palem, K. V., and Simons, B. B. 1993. Scheduling Time- Critical Instructions on RISC Machines. ACM Trans. Pro- gram. Lang. Syst. 15(4):632-658.
  64. Papadimitriou, C. H., and Yannakakis, M. 1979. Schedul- ing Interval-Ordered Tasks. SIAM J. Comput. 8(3):405- 409. Verriet, J. 1996. Scheduling Interval Orders with Re- lease Dates and Deadlines. Technical Report UU-CS-1996- 12, Department of Information and Computing Sciences, Utrecht University.
  65. Verriet, J. 1998. The Complexity of Scheduling Typed Task Systems with and without Communication Delays. Tech- nical Report UU-CS-1998-26, Department of Information and Computing Sciences, Utrecht University. References
  66. Blum, A., and Furst, M. 1995. Fast planning through plan-graph analysis. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI95), 1636-1642.
  67. Fox, M., and Long, D. 1998. The automatic inference of state invariants in TIM. Journal of AI Research 9.
  68. Fox, M., and Long, D. 2003. PDDL2.1: An ex- tension of PDDL for expressing temporal plan- ning domains. Journal of AI Research 20:61- 124. Gazen, B., and Knoblock, C. 1997. Combining the expressivity of UCPOP with the efficiency of Graphplan. In ECP-97, 221-233.
  69. Halsey, K.; Long, D.; and Fox, M. 2004. Crikey - a planner looking at the integration of scheduling and planning. In Proceedings of the Workshop on Integration Scheduling Into Planning at 13th In- ternati onal Conference on Automated Planning and Scheduling (ICAPS'03), 46-52.
  70. Long, D., and Fox, M. 2003. Exploiting a graph- plan framework in temporal planning. In Pro- ceedings of ICAPS'03.
  71. Nebel, B. 2000. On the expressive power of planning formalisms: Conditional effects and boolean preconditions in the STRIPS formalism. In Minker, J., ed., Logic-Based Artificial Intelli- gence. Kluwer. 469-490.
  72. Rintanen, J. 2007. Complexity of concurrent temporal planning. In Proceedings of Interna- tional Conference on Automated Planning and Scheduling, 280-287. References
  73. Aler, R.; Borrajo, D.; and Isasi, P. 2002. Using genetic program- ming to learn and improve control knowledge. Artificial Intelli- gence 141:29-56.
  74. Baader, F.; Calvanese, D.; McGuinness, D.; Nardi, D.; and Patel- Schneider, P. 2003. The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press.
  75. Bacchus, F., and Kabanza, F. 2000. Using temporal logics to ex- press search control knowledge for planning. Artificial Intelligence 116:123-191.
  76. Bertsekas, D. P., and Tsitsiklis, J. N. 1996. Neuro-Dynamic Pro- gramming. Athena Scientific.
  77. Fern, A.; Yoon, S.; and Givan, R. 2006. Approximate policy iter- ation with a policy language bias: Solving relational markov deci- sion processes. Journal of Artificial Intelligence Research 25:75- 118. Hoffmann, J., and Nebel, B. 2001. The FF planning system: Fast plan generation through heuristic search. Journal of Artificial In- telligence Research 14:263-302.
  78. Khardon, R. 1999. Learning action strategies for planning do- mains. Artificial Intelligence 113:125-148.
  79. Koza, J. R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Bradford Book, The MIT Press.
  80. Leckie, C., and Zukerman, I. 1998. Inductive learning of search control rules for planning. Artificial Intelligence 101:63-98.
  81. Martin, M., and Geffner, H. 2004. Learning generalized policies from planning examples using concept languages. Applied Intelli- gence 20:9-19.
  82. Miller, B. L., and Goldberg, D. E. 1995. Genetic algorithms, tournament selection, and the effects of noise. Technical Report 95006, Department of General Engineering, University of Illinois at Urbana-Champaign, Urbana, IL.
  83. Pednault, E. 1987. Toward a Mathematical Theory of Plan Syn- thesis. Phd, Stanford University, USA.
  84. Ramsey, C. L.; Schultz, A. C.; and Grefenstette, J. J. 1990. Simulation-assisted learning by competition: Effects of noise dif- ferences between training model and target environment. In Proc. 7th International Conference on Machine Learning, 211-215.
  85. Spector, L. 1994. Genetic programming and AI planning systems. In Proc. 12th National Conference on Artificial Intelligence (AAAI- 94), 1329-1334.
  86. Tesauro, G., and Galperin, G. 1996. On-line policy improvement using Monte-Carlo search. In Advances in Neural Information Pro- cessing 9. References
  87. Ackoff, R. 1989. From Data to Wisdom. Journal of Applied Systems Analysis, 16, 3-9.
  88. Allen, J.F. 1983. Maintaining Knowledge about Temporal Intervals. Communications of the ACM, 26, 11, 832-843.
  89. Chen, P.P-S. 1976. The Entity-Relationship Model: Towards a unified view of data. ACM Transactions on Database Systems, 1, 9-36.
  90. DesJardins, M. 1994. Knowledge Development Methods for Planning Systems. Proceedings, AAAI-94 Fall Symposium series, Planning and Learning: On to real applications. New Orleans, LA, USA.
  91. Fikes, R., & Nilsson, N.J. 1971. STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence Journal, 2, 189- 208. Garland, A., & Lesh, N. 2002. Plan Evaluation with Incomplete Action Descriptions. TR2002-05, Mitsubishi Electric Research Laboratories, Cambridge, Massachusetts, USA.
  92. Gil, Y. 1992. Acquiring Domain Knowledge for Planning by Experimentation. PhD thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
  93. Goldman, R., & Boddy, M. 1996. Expressive Planning and Explicit Knowledge. Proceedings, AIPS-96, 110- 117, AAAI Press.
  94. Grant, T.J. 1995. Generating Plans from a Domain Model. Proceedings, 14th workshop of the UK Planning and Scheduling Special Interest Group, 22-23 November 1995, University of Essex, Colchester, UK.
  95. Grant, T.J. 1996. Inductive Learning of Knowledge- Based Planning Operators. PhD thesis, University of Maastricht, The Netherlands.
  96. Grant, T.J. 2001. Towards a Taxonomy of Erroneous Planning. Proceedings, 20th workshop of the U K Planning and Scheduling Special Interest Group, 13-14 December 2001, University of Edinburgh, Scotland.
  97. Grant, T.J. 2005. Integrating Sensemaking and Response using Planning Operator Induction. In Van de Walle, B. & Carlé, B. (eds.), Proceedings, 2nd International Conference on Information Systems for Crisis Response and Management (ISCRAM), Royal Flemish Academy of Science and the Arts, Brussels, Belgium, 18-20 April 2005. SCK.CEN and University of Tilburg, 89-96.
  98. Grant, T.J. 2006. Measuring the Potential Benefits of NCW: 9/11 as case study. In Proceedings, 11 th International Command & Control Research & Technology Symposium (ICCRTS06), Cambridge, UK, paper I-103.
  99. Grant, T.J., Herik, H.J. van den, & Hudson, P.T.W. 1994. Which Blocks World is the Blocks World? Proceedings, 13th workshop of the UK Planning and Scheduling Special Interest Group, University of Strathclyde, Glasgow, Scotland.
  100. Hansen, M. T. 1999. The Search-Transfer Problem: The role of weak ties in sharing knowledge across organization subunits. Administrative Science Quarterly, 44 (1), 82-111.
  101. Kambhampati, S. 2006. Lectures on Learning and Planning. 2006 Machine Learning Summer School (MLSS'06), Canberra, Australia.
  102. Kambhampati, S. 2007. Model-lite Planning for the Web Age Masses: The challenges of planning with incomplete and evolving domain models. Proceedings, American Association for Artificial Intelligence.
  103. Krogt, R. van der. 2005. Plan Repair in Single-Agent and Multi-Agent Systems. PhD thesis, TRAIL Thesis - series T2005/18, TRAIL Research School, Netherlands.
  104. Lefkowitz, L. S., and Lesser, V. R. 1988. Knowledge Acquisition as Knowledge Assimilation. International Journal of Man-Machine Studies, 29, 215-226.
  105. Levine, G., & DeJong, G. 2006. Explanation-Based Acquisition of Planning Operators. Proceedings, ICAPS 2006.
  106. McCluskey, T.L., & Porteus, J.M. 1997. Engineering and Compiling Planning Domain Models to Promote Validity and Efficiency. Artificial Intelligence Journal, 95, 1-65.
  107. McCluskey, T.L., Richardson, N.E., & Simpson, R.M. 2002. An Interactive Method for Inducing Operator Descriptions. Proceedings, ICAPS 2002.
  108. Mitchell, T.M. 1982. Generalization as Search. Artificial Intelligence Journal, 18, 203-226.
  109. Mukherji, P., & Schubert, L.K. 2005. Discovering Planning Invariants as Anomalies in State Descriptions. Proceedings, ICAPS 2005.
  110. Nijssen, G.M., & Halpin, T.A. 1989. Conceptual Schema and Relational Database Design: A fact-oriented approach. Prentice-Hall Pty Ltd, Sydney, Australia.
  111. Nilsson, N.J. 1980. Principles of Artificial Intelligence. Tioga Publishing Company, Palo Alto, California, USA.
  112. Oates, T., & Cohen, P.R. 1996. Searching for Planning Operators with Context-Dependent and Probabilistic Effects. Proceedings, AAAI, 865-868.
  113. Porter, B., & Kibler, D. 1986. Experimental Goal Regression: A method for learning problem-solving heuristics. Machine Learning, 1, 249-284.
  114. Shannon, C.E. 1948. A Mathematical Theory of Communication. Bell System Technical Journal, 27, 379- 423 (July) & 623-646 (October).
  115. Shen, W.-M. 1994. Discovery as Autonomous Learning from the Environment. Machine Learning, 12, 143-156.
  116. Slaney, J., & Thiébaux, S. 2001. Blocks World Revisited. Artificial Intelligence Journal, 125, 119-153.
  117. Szulanski, G. 1996. Exploring Internal Stickiness: Impediments to the transfer of best practice within the firm. Strategic Management Journal, 17, 27-43.
  118. Wang, X. 1994. Learning Planning Operators by Observation and Practice. PhD thesis, Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA.
  119. Weick, K. 1995. Sensemaking in Organizations. Sage, Thousand Oaks, CA, USA. ISBN 0-8039-7178-1.
  120. Yang, Q., Wu, K., & Jiang, Y. 2005. Learning Action Models from Plan Examples with Incomplete Knowledge. Proceedings, ICAPS 2005, 241-250.
  121. Zimmerman, T., & Kambhampati, S. 2003. Learning- Assisted Automated Planning: Looking back, taking stock, going forward. AI magazine, 73-96 (Summer 2003). References
  122. Chen, Y.; Wah, B. W.; and Hsu, C. 2006. Temporal Plan- ning using Subgoal Partitioning and Resolution in SGPlan. Journal of Artificial Intelligence Research 26:323-369.
  123. Gerevini, A., and Serina, I. 2002. LPG: A Planner Based on Local Search for Planning Graphs with Action Costs. In AIPS, 13-22.
  124. Hoffmann, J., and Nebel, B. 2001. The FF planning sys- tem: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research.
  125. Koehler, J. 1999. RIFO within IPP. Technical report, Albert-Ludwigs University at Freiburg.
  126. Long, D., and Fox, M. 2003. The 3rd international plan- ning competition: Results and analysis. Journal of AI Re- search 20:1-59.
  127. Slaney, J., and Thiébaux, S. 2001. Blocks world revisited. Artif. Intell. 125(1-2):119-153. References
  128. T. L. McCluskey, D. Liu, Ron M. Simpson, "GIPO II: HTN Planning in a Tool-supported Knowledge Engineering Environment", International Conference on Automated Planning and Scheduling (ICAPS), 2003
  129. Wilkins, D. E., Lee, T. J. and Berry, P., Interactive Execution Monitoring of Agent Teams, Journal of Artificial Intelligence Research, 18 (2003), pp. 217-261.
  130. D. Vrakas, I. Vlahavas, "A Visualization Environment for Planning", International Journal on Artificial Intelligence Tools", Vol. 14 (6), 2005, pp. 975-998, World Scientific.
  131. Ghallab, M., Howe, A., Knoblock, C., McDermott, D., Ram, A., Veloso, M., Weld, D. and Wilkins, D., "PDDL --the planning domain definition language". Technical report, Yale University, New Haven, CT (1998).
  132. Fox, M. and Long, D., "PDDL2.1: An extension to PDDL for expressing temporal planning domains". Journal of Artificial Intelligence Research, 20 (2003), 61-124.
  133. Gerevini, A. and Long, D., "Plan Constraints and Preferences in PDDL3", Technical Report R.T. 2005-08-47, Department of Electronics for Automation, University of Brescia, Italy.
  134. Fikes, R. and Nilsson, N. J., STRIPS: A new approach to the application of theorem proving to problem solving, Artificial Intelligence, Vol 2 (1971), 189-208.
  135. Liu, D., and McCluskey, T. L. 2000. The OCL Language Manual, Version 1.2. Technical report, Department of Computing and Mathematical Sciences, University of Huddersfield References
  136. Baker, K. R., and Scudder, G. D. 1990. Sequencing with earliness and tardiness penalties: A review. Operations Research 38(1):22-36.
  137. Baptiste, P.; Flamini, M.; and Sourd, F. To appear in 2008. Lagrangian bounds for just-in-time job-shop scheduling. Computers & Operations Research 35(3):906-915.
  138. Barták, R. 1999. Constraint programming -what is be- hind? In Proc. of CPDC99 Workshop.
  139. Beck, J. C., and Perron, L. 2000. Discrepancy-bounded depth first search. In Second International Workshop on Integration of AI and OR Technologies for Combinatorial Optimization Problems (CP-AI-OR'00).
  140. Beck, J. C., and Refalo, P. 2001. A hybrid approach to scheduling with earliness and tardiness costs. In Third In- ternational Workshop on Integration of AI and OR Tech- niques (CP-AI-OR'01).
  141. Beck, J. C., and Refalo, P. 2002. Combining local search and linear programming to solve earliness/tardiness scheduling problems. In Fourth International Workshop on Integration of AI and OR Techniques (CP-AI-OR'02).
  142. Beck, J. C., and Refalo, P. 2003. A hybrid approach to scheduling with earliness and tardiness costs. Annals of Operations Research 118(1-4):49-71.
  143. Brucker, P.; Heitmann, S.; Hurink, J.; and Nieberg, T. 2006. Job-shop scheduling with limited capacity buffers. OR Spectrum 28(2):151-176.
  144. Carlier, J., and Pinson, E. 1990. A practical use of jack- son's pre-emptive schedule for solving the job-shop prob- lem. Annals of Operations Research 26:269-287.
  145. Danna, E., and Perron, L. 2003. Structured vs. unstruc- tured large neighborhood search: A case study on job-shop References
  146. Akturk, M. S.; Ghosh, J. B.; and Gunes, E. D. 2003. Scheduling with tool changes to minimize total com- pletion time: A study of heuristics and their perfor- mance. Naval Research Logistics 50:15-30.
  147. Akturk, M. S.; Ghosh, J. B.; and Gunes, E. D. 2004. Scheduling with tool changes to minimize total com- pletion time: Basic results and SPT performance. Eu- ropean Journal of Operational Research 157:784-790.
  148. Akturk, M. S.; Ghosh, J. B.; and Kayan, R. K. 2007. Scheduling with tool changes to minimize total com- pletion time under controllable machining conditions. Computers and Operations Research 34:2130-2146.
  149. Baptiste, P.; Le Pape, C.; and Nuijten, W. 2001. Constraint-based Scheduling. Kluwer Academic Pub- lishers.
  150. Barták, R. 2003. Constraint-based scheduling: An introduction for newcomers. In Intelligent Manufac- turing Systems 2003, 69-74.
  151. Chen, J.-S. 2006a. Single-machine scheduling with flexible and periodic maintenance. Journal of the Op- erational Research Society 57:703-710.
  152. Chen, W. J. 2006b. Minimizing total flow time in the single-machine scheduling problem with periodic maintenance. Journal of the Operational Research So- ciety 57:410-415.
  153. Chen, J.-S. 2007a. Optimization models for the tool change scheduling problem. Omega (to appear).
  154. Chen, J.-S. 2007b. Scheduling of nonresumable jobs and flexible maintenance activities on a single machine to minimize makespan. European Journal of Opera- tional Research (to appear).
  155. Goemans, M. X.; Queyranne, M.; Schulz, A. S.; Skutella, M.; and Wang., Y. 2002. Single machine scheduling with release dates. SIAM Journal on Dis- crete Mathematics 15(2):165-192.
  156. Gray, E.; Seidmann, A.; and Stecke, K. E. 1993. A syn- thesis of decision models for tool management in au- tomated manufacturing. Management Science 39:549- 567. Ji, M.; He, Y.; and Cheng, T. C. E. 2007. Single- machine scheduling with periodic maintenance to min- imize makespan. Computers & Operations Research 34:1764-1770.
  157. Karakayalı, I., and Azizoglu, M. 2006. Minimizing to- tal flow time on a single flexible machine. International Journal of Flexible Manufacturing Systems 18:55-73.
  158. Kovács, A., and Beck, J. C. 2007. A global constraint for total weighted completion time. In Proceedings of CPAIOR'07, 4th Int. Conf. on Integration of AI and OR Techniques in Constraint Programming for Com- binatorial Optimization Problems (LNCS 4510), 112- 126. Liao, C. J., and Chen, W. J. 2003. Single-machine scheduling with periodic maintenance and nonresum- able jobs. Computers & Operations Research 30:1335- 1347.
  159. Qi, X.; Chen, T.; and Tu, F. 1999. Scheduling the maintenance on a single machine. Journal of the Op- erational Research Society 50:1071-1078.
  160. Tang, C. S., and Denardo, E. V. 1988. Models arising from a flexible manufacturing machine, Part I: Mini- mization of the number of tool switches. Operations Research 36:767-777. References
  161. S. Abdennadher and M. Marte. University course timetablingusing constraint handling rules. Journal of Applied Artificial Intelligence, 14(4):311-326, 2000.
  162. The ECLiPSe Constraint Programming System, http://eclipse.crosscoreop.com/
  163. W. Legierski. Search strategy for constraint- based class-teacher timetabling. In Practice and Theory of Automated Timetabling IV, volume 2740 of Lecture Notes in Computer Science, pages 247-261.
  164. W. Legierski and R. Widawski. System of automated timetabling. In Proceedings of the 25th International Conference Information Technology Interfaces ITI 2003, Lecture Notes in Computer Science, pages 495-500, 2003.
  165. W. Legierski. Automated timetabling via Constraint Programming, PhD Thesis, Silesian University of Technology, Gliwice, 2006.
  166. M. Marte. Models and Algorithms for School Timetabling A Constraint-Programming Approach. PhD thesis, Ludwig-Maximilians-Universitat Munchen, 2002.
  167. T. Muller. Constraint-based Timetabling. PhD thesis, Charles University in Prague, Faculty of Mathematics and Physics, 2005.
  168. S. Petrovic and E.K. Burke, Edmund K, Handbook of Scheduling: Algorithms, Models, and Performance Analysis, Chapter 45: University Timetabling,CRC Press,Edt: J. Leung, 2004
  169. Oliveira E. Reise L.P. A language for specifying complete timetabling problem. In Practice and Theory of Automated Timetabling III, volume 2079 of Lecture Notes in Computer Science, pages 322-341.
  170. H. Rudova. Constraint Satisfaction with Preferences. PhD thesis, Masaryk University Brno, 2001.
  171. A. Schaerf. A survey of automated timetabling. In Wiskunde en Informatica, TR CS-R9567. CWICent, 1995.
  172. J. Werner. Timetabling in Germany: A survey. Interfaces, 16(4):66 74, 1986.
  173. Chih-Wei;
  174. Wah, B.; Ruoyun; and Chen, Y. 2006. Handling soft constraints and goal preferences in SG- PLAN. In ICAPS Workshop on Preferences and Soft Constraints in Planning. ICAP.
  175. Cresswell, S., and Coddington, A. 2003. Planning with timed literals and deadlines. In Porteous, J., ed., Proceedings of the 22nd Workshop of the UK Planning and Scheduling Special Interest Group, 22-35. Univer- sity of Strathclyde. ISSN 1368-5708.
  176. Dean, T., and Kambhampati, S. 1997. Planning and scheduling. In The Computer Science and Engineering Handbook 1997. CRC Press. 614-636.
  177. Dimopoulos, Y.; Gerevini, A.; Haslum, P.; and Saetti, A. 2006. The benchmark domains of the determin- istic part of IPC-5. In Booklet of the 2006 Planning Competition, ICAPS'06.
  178. Edelkamp, S., and Hoffman, J. 2003. PDDL2.2: The language for the classical part of the 4th international planning competition.
  179. Fox, M., and Long, D. 2003. An extension of PDDL for expressing temporal planning domains. Journal of Artificial Intelligence Research 20:61-124.
  180. Fox, M.; Long, D.; and Halsey, K. 2004. An inves- tigation into the expressive power of PDDL2.1. In Proceedings of ECAI'04.
  181. Gerevini, A., and Long, D. 2006. Plan constraints and preferences in PDDL3. In ICAPS Workshop on Preferences and Soft Constraints in Planning. ICAPS. Gerevini, A., and Serina, I. 2002. LPG: A planner based on local search for planning graphs. In Proceed- ings of the Sixth International Conference on Artificial Intelligence Planning and Scheduling.
  182. Gerevini, A.; Saetti, A.; and Serina, I. 2004. Planning with numerical expressions in LPG. In Proceedings of the 16th European Conference on Artificial Intelli- gence (ECAI-04). IOS-Press, Valencia, Spain. Halsey, K.; Long, D.; and Fox, M. 2004. CRIKEY - a planner looking at the integration of scheduling and planning. In Proceedings of the Workshop on Integra- tion Scheduling Into Planning at 13th International Conference on Automated Planning and Scheduling (ICAPS'03), 46-52.
  183. Halsey, K. 2004. CRIKEY! Its Co-ordination in Temporal Planning: Minimising Essential Planner- Scheduler Communication in Temporal Planning. Ph.D. Dissertation, Ph. D. Dissertation, University of Durham. Long, D., and Fox, M. 2003. The 3rd international planning competition: Results and analysis. Journal of Artificial Intelligence Research 20:1-59.
  184. Smith, D. E.; Frank, J.; and Jonsson, A. 2000. Bridg- ing the gap between planning and scheduling. The Knowledge Engineering Review 15:47-83.
  185. Weld, D. S. 1994. An introduction to least commit- ment planning. AI Magazine 4. References
  186. Borrow, J. E. 1988. Optimal robot path planning using the minimum-time criterion. Journal of Robotics and Automa- tion 4:443-450.
  187. Canny, J. 1988. The Complexity of Robot Motion Planning. MIT Press.
  188. Carlsson, M.; Ottosson, G.; and Carlson, B. 1997. An open-ended finite domain constraint solver. In Proceedings of Programming Languages: Implementations, Logics, and Programs.
  189. Fortune, S. 1986. A sweepline algorithm for voronoi dia- grams. In Proceedings of the second annual symposium on Computational geometry, 313-322.
  190. Garau, B.; Alvarez, A.; and Oliver, G. 2005. Path plan- ning of autonomous underwater vehicles in current fields with complex spatial variability: an a * approach. In Pro- ceedings of the International Conference on Robotics and Automation, 194-198.
  191. Ju, M.-Y.; Liu, J.-H.; and Hwang, K.-S. 2002. Real- time velocity alteration strategy for collision-free trajectory planning of two articulated robots. Journal of Intelligent and Robotic Systems 33:167-186.
  192. Kant, K., and Zucker, S. W. 1986. Toward efficient trajec- tory planning: the path-velocity decomposition. The Inter- national Journal of Robotics Research 5:72-89.
  193. Khatib, O. 1986. Real-time obstacle avoidance for manip- ulators and mobile robots. In Proceedings of the Interna- tional Conference on Robotics and Automation, volume 2, 500-5005.
  194. LaValle, S. M. 1998. Rapidly-exploring random trees: A new tool for path planning. TR 98-11, Computer Science Dept., Iowa State Univ.
  195. Nilsson, N. J. 1969. A mobile automation: An applica- tion of artificial intelligence techniques. Proceedings of the International Joint Conference on Artifical Intelligence 509-520.
  196. Park, S.; Deyst, J.; and How, J. 2004. A new nonlinear guidance logic for trajectory tracking. Proceedings of the AIAA Guidance, Navigation and Control Conference.
  197. Petres, C.; Pailhas, Y.; Patron, P.; Petillot, Y.; Evans, J.; and Lane, D. 2007. Path planning for autonomous underwater vehicles. Transactions on Robotics 23:331-341.
  198. Soulignac, M., and Taillibert, P. 2006. Fast trajectory plan- ning for multiple site surveillance through moving obsta- cles and wind. In Proceedings of the Workshop of the UK Planning and Scheduling Special Interest Group, 25-33.
  199. Zhao, Q., and Yan, S. 2005. Collision-free path planning for mobile robots using chaotic particle swarm optimiza- tion. In Proceedings of the International Conference on Advances in Natural Computation, 632-635. References Adobe. 2007. Flash. http://www.adobe.com/.
  200. News, A. 2006. Classic paper award. AI Magazine 27(3)
  201. AGRE, P., and CHAPMAN, D. 1987. Pengi: An implemen- tation of a theory of activity. In Proceedings of 6 th AAAI, 268-272.
  202. AGRE, P. 1988. The Dynamics of Everyday life. Ph.D. Dissertation, MIT AI Lab Tech Report 1085. AGRE, P. 1993. The symbolic worldview: Reply to vera and simon. Cognitive Science 17(1) 61-69.
  203. AGRE, P. 1997. Computation and Human Experience. Cambridge University Press.
  204. AMBROS-INGERSON, J., and STEEL, S. 1988. Integrating planning, execution and monitoring. In Proceedings of 7 th AAAI, 83-88.
  205. BARTLETT, N.; SIMKIN, S.; and STRANC, C. 1996. Java Game Programming. Coriolis Group Books. BRATMAN, M. 1987. Intentions, Plans and Practical Rea- son. Harvard University Press.
  206. CHAPMAN, D. 1990. Vision, Instruction and Action. Ph.D. Dissertation, MIT AI Lab Tech Report 1204. CHARRIER, D. 2007. Super sokoban 2.0. http://d.- ch.free.fr/.
  207. DROGOUL, A.; FERBER, J.; and JACOPIN, E. 1991. Viewing cognitive modelling as eco-problem solving: The PENGI experience. In Proceedings of the 1991 European Conference on Modelling and Simulation Multiconference, 337-342.
  208. FOX, M., and LONG, D. 1999. The detection and exploita- tion of symmetry in planning problems. In Proceedings of 16 th IJCAI, 956-961.
  209. HORNELL, K. 1996. Iceblox. http://www.tdb.uu.- se/˜karl.
  210. MILLER, G.; GALANTER, E.; and PRIBRAM, K. 1986. Plans and the Structure of Behavior. Adams-Bannister- Cox. References
  211. Aickelin, U. 1999. Genetic Algorithms for Multiple-Choice Optimisation Algorithms. Ph.d. diss., European Business Management School University of Swansea.
  212. Azaiez, M. N., and Sharif, S. S. 2005. A 0-1 goal program- ming nodel for nurse scheduling. Computers & Operations Research 32.
  213. Baumelt, Z. 2007. Hospital Nurse Scheduling. Master the- sis, Department of Control Engineering, Faculty of Electri- cal Engineering, Czech Technical University in Prague. Berghe, G. V. 2002. An Advanced Model and Novel Meta-Heuristic Solution Methods to Personnel Scheduling in Healthcare. Ph.d. diss., University of Gent.
  214. Burke, E. K.; de Causmaecker, P.; Berghe, G. V.; and van Landeghem, H. 2004. The state of the art of nurse roster- ing. Journal of Scheduling 7:441-499.
  215. Cheang, B.; Li, B.; Lim, A.; and Rodrigues, B. 2002. Nurse rostering problems -a bibliographic survey. Euro- pean Journal of Operations Research.
  216. Chen, J., and Yeung, T. 1993. Hybrid expert system ap- proach to nurse scheduling. Computers in Nursing.
  217. Eiselt, H. A., and Sandblom, C. L. 2000. Integer Program- ming and Netwotk Models. Springer-Verlag Berlin and Hei- delberg, 1st edition.
  218. Hung, R. 1995. Hospital nurse scheduling. Journal of Nursing Administration 25.
  219. Okada, M. 1992. An approach to the generalised nurse scheduling problem -generation of a declarative program to represent institution-speciffic knowledge. Computers and Biomedical Research 25. References
  220. Brucker, P., Drexl, A., Mohring, R., Neumann, K., and Pesh, E., 1999. Resource-constrained project scheduling: Notation, classification, models, and methods, European Journal of Operational Research 112
  221. Herroelen, W., De Reyck, B., and Demeulemeester, E., 1998. Resource-constrained project scheduling: a survey of recent developments, Computers Ops Res. Vol. 25
  222. Blazewicz, J., Cellary W., Slowinski R., and Weglarz J., 1986. Scheduling under Resource Constraints: Deterministic Models, in: Annals of operations research, vol. 7., J.C. Baltzer
  223. Kolisch, R. and Hartmann, S. eds. 1999. Heuristic Algorithms for Solving the Resource-Constrained Project Scheduling Problem: Classification and Computational Analysis, in: Project scheduling: Recent models, algorithms and applications, Kluwer
  224. Kolisch, R., and Sprecher, A. 1997. PSPLIB A Project Scheduling Problem Library, European Journal of Operational Research 96
  225. Gansner, E., and E., North, 2000. An open graph visualization system and its applications to software engineering, Software Practice and Experience Vol.30, 11
  226. Valls, V., Ballestín, F., Quintanilla, S., 2006. Justification Technique Generalizations, in: Perspectives in Modern Project Scheduling, Kluwer

Related papers

Grid-Based SLA Management
James Padgett

2005

This paper presents an architecture for specifying, monitoring and validating Service Level Agreements (SLA) for use in Grid environments. SLAs are an essential component in building Grid systems where commitments and assurances are specified, implemented and monitored. Targeting compute resources, an SLA manager reserves resources for user applications requiring resources on demand. Methods for automated monitoring and violation capture are discussed showing how Service Level Objectives (SLO) can be validated. A SLA for a compute service is specified and experiments carried out on the White Rose Grid. Results are presented in the form of a SLA document and show the violations that were captured during task execution.

View PDFchevron_right
SLA-Based Resource Management and Allocation
Philipp Wieder

Buyya/Market-Oriented Grid and Utility Computing, 2009

The aim of the chapter is to describe how service-level agreements (SLAs) could be utilized to provide the basis for resource trading based on economic models. SLAs enable a service user to identify their requirements, and a provider to identify their capabilities. Subsequently, the terms in an SLA are necessary to ensure that mutually agreeable quality is being delivered by the provider according to the agreement. The use of service-level agreements (SLAs) in a resource management system to support Grid computing applications is described. To this end, we provide an architecture that supports the creation and management of SLAs. The architecture of the system, in terms of the components and their interactions, is first presented, followed by a description of the specific requirements for a marketoriented Grid economy. We use SLAs as a means to support reliable quality of service for Grid jobs. The creation of such an SLA requires planning and orchestration mechanisms. We will discuss these functionalities and also consider the economic aspects such as dynamic pricing and negotiation mechanisms. These mechanisms are necessary to enable SLA formation and use, and to ensure that an SLA is being adhered to during service provision.

View PDFchevron_right
Towards Service Level Agreement Based Scheduling on the Grid
Jonathan M Garibaldi

2004

The orchestration of complex workflows on the Grid is emerging as a key goal for the Grid community. It is necessary for these workflows to be executed reliably, respecting any dependences; it is also desirable for the user to know (albeit approximately) when the workflow will complete. The authors argue that meeting these goals will necessitate a major shift in the underlying scheduling technology, which is ultimately used to execute any computational tasks contained in these workflows. This position paper describes a recently funded project that aims to establish a fundamental new infrastructure for efficient job scheduling on the Grid, based on a notion of Service Level Agreements. These are negotiated between the client (user, superscheduler, or broker) and the scheduler, contain information on acceptable job start and end times, and may be re-negotiated during runtime.

View PDFchevron_right
A SLA-based Framework with Support for Meta-scheduling in Advance for Grids
Carmen Carrion

Quality of Service (QoS) is one of the most important and active research topics within Grid technology. But the emerging transformation from a product oriented economy to a service oriented economy establishes a new scenario where actual QoS mechanisms need to be enforced and new QoS mechanisms needed. Service Level Agreements (SLAs) are considered the cornerstone born to fulfill those requirements and the key concept that boosts the Grid economic exploitation. Therefore mechanisms to negotiate and manage SLAs become necessary.

View PDFchevron_right
Service Level Agreement Based Grid Scheduling
Giullia Britto

Web Services, 2008. ICWS' …, 2008

In order to co-ordinate multiple resource providers in grid environment to meet a common objective, support for negotiation is needed to establish a contract between the user and the resource providers that clearly states the QoS required, restrictions on resource utilization ...

View PDFchevron_right

Related topics

  • Visual Language
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved