[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-228442-en":3,"doc-seo-228442-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},228442,549768072016,"WPS_1786070896","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Efficient Algorithms for Selecting Advanced Reservations","The report studies efficient selection and aggregation of Grid resources using constraint programming, motivated by advanced resource reservation (AR) in Grid systems. It defines AR through a broker-driven model that maximizes broker utility by choosing an optimal subset of customer orders based on prices and penalties. The demonstrator architecture, privacy considerations, and the temporal knapsack formulation are presented, followed by new algorithms, theoretical analysis, experimental results, and discussion of dynamic requirements.","Efficient Algorithms for Selecting Advanced Reservations  \nMark Bartlett1 Alan M. Frisch 1 Youssef Hamadi2 Ian Miguel3 Chris Unsworth4  \n1 Artificial Intelligence Group, Department of Computer Science, University of York, York, UK  \n2 Microsoft Research Ltd., 7 J J Thomson Avenue, Cambridge, UK  \n3 School of Computer Science, University of St Andrews, St Andrews, UK  \n4 Department of Computing Science, University of Glasgow, UK  \nDecember 2004  \nTechnical Report  \nMSR-TR-2004-132  \nMicrosoft Research  \nMicrosoft Corporation  \nOne Microsoft Way  \nRedmond, WA 98052  \n[http://www.research.microsoft.com](http://www.research.microsoft.com)  \n1 Introduction  \nGrid computing aggregates various distributed heterogeneous resources to efficiently solve a variety of large scale parallel applications. This involves the sharing ofresources distributed across multiple administrative domains. So far, the majority of Grid research has focused on the problems raised by accessing multiple domains (authentication, performance, etc) (4) . This has resulted in important standard definitions 1 and will eventually meet the world of web services (3) . However, resource sharing raises the problem of efficient selection and aggregation. The goal of the Gridline project is to study the applicability of constraint programming (1) in Grid resource optimisation. This technology has been very successful in industrial applications (11) . This success comes from its high level of expressiveness along with an easy integration with imperative programming. Domains of application include scheduling and allocating both human resources (e.g., crew rostering and nurse scheduling) and material resources (e.g., airport gates and transport fleets) . Gridline assumes that Grid resource sharing could greatly benefit from the application of constraint programming. The project employs constraint-based optimisation to produce three demonstrators.  \nThis paper presents the results obtained with the first demonstrator which considers advanced resources reservation (AR), taking the rationale of a Grid resource broker that maximises its utility by choosing the optimal set of customers orders. Advanced reservation will play a major role in Grid systems. This mechanism guarantees the availability ofresources to users at some specified future time. Such a contractual mechanism perfectly fits the requirements of complex Grid applications which usually require the combination of various Grid resources (2) .  \nIn the following, we first define advanced reservations. Section 2 formalizes the problem. Sections 3 and 4 present our new algorithms. Their theoretical analysis is presented in Section 5. Experimental results are presented in Section 6. Before giving an overall conclusion, Section 7 discusses dynamic requirements for this problem.  \n2 Advanced Resource Reservation  \nThe design of our AR demonstrator is largely influenced by GGF recommendations (10; 6) . In this Grid usage scenario, a broker manages some bounded yet divisible resource, such as network bandwidth or CPU nodes, etc. Since the resources are limited per time unit, the provider may be unable to meet all demands, so the broker must choose which requests are tobe accepted. The idea here is to maximise the utility of the broker by selecting an optimal subset of customers’ orders.  \nFigure 1 presents the architecture of this demonstrator. Gridline receives the customers’ priced requests via the broker. It also obtains the status of potential resources from the Grid or directly from the broker. Gridline then computes an optimal selection of orders according to prices and penalties. This selection is forwarded to the broker who notifies customers of selection/rejection.  \nFigure 1. Advanced Resource Reservation  \nWe can remark that there is no direct connection between customers and the Gridline service. This is an important privacy consideration: Gridline can be completely blind to customers’ identities and can receive fake but relati","cbCailYlYNMzzajs","https://ap.wps.com/l/cbCailYlYNMzzajs","pdf",91670,1,12,"English","en",105,"# Introduction\n## Grid resource sharing and selection\n## Goal and project overview\n# Advanced Resource Reservation\n## Demonstrator architecture\n## Customer order reservation pattern\n# The Problem\n## Temporal knapsack formulation\n# Algorithms and analysis (Sections 3-5)\n# Experimental results (Section 6)\n# Dynamic requirements and conclusion (Section 7)","[{\"question\":\"What is the main objective of the Gridline project described in the report?\",\"answer\":\"To study the applicability of constraint programming for Grid resource optimization, producing constraint-based optimization demonstrators for advanced resource reservation selection.\"},{\"question\":\"How does advanced reservation work in the proposed scenario?\",\"answer\":\"A broker chooses an optimal subset of customer orders so that resources are guaranteed at specified future times under contractual terms suited to complex Grid applications.\"},{\"question\":\"Why is the problem formulated as a temporal knapsack problem?\",\"answer\":\"Orders require quality of service over time intervals and the system has limited capacity per time unit, so selecting a subset to maximize utility while respecting hard constraints matches temporal knapsack reasoning.\"}]","Efficient Algorithms for Selecting Advanced Reservations | 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is the main objective of the Gridline project described in the report?","Question",{"text":75,"@type":76},"To study the applicability of constraint programming for Grid resource optimization, producing constraint-based optimization demonstrators for advanced resource reservation selection.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does advanced reservation work in the proposed scenario?",{"text":80,"@type":76},"A broker chooses an optimal subset of customer orders so that resources are guaranteed at specified future times under contractual terms suited to complex Grid applications.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is the problem formulated as a temporal knapsack problem?",{"text":84,"@type":76},"Orders require quality of service over time intervals and the system has limited capacity per time unit, so selecting a subset to maximize utility while respecting hard constraints matches temporal knapsack 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