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Dynamic resource allocation platform and method for time related resourcesRelated Patent Categories: Data Processing: Financial, Business Practice, Management, Or Cost/price Determination, Automated Electrical Financial Or Business Practice Or Management ArrangementDynamic resource allocation platform and method for time related resources description/claimsThe Patent Description & Claims data below is from USPTO Patent Application 20060167703, Dynamic resource allocation platform and method for time related resources. Brief Patent Description - Full Patent Description - Patent Application Claims
FIELD OF THE INVENTION [0001] The present invention relates to a dynamic resource allocation platform and method for allocation of time related resources and more particularly but not exclusively to a platform for supporting dynamic allocation of data communication capacity. BACKGROUND OF THE INVENTION [0002] Data networks supply enormous amounts of data capacity to large numbers of users. The usage patterns on the network tend to change very rapidly and dynamic allocation of the network capacity is a huge computational problem. [0003] A commonly used method of managing such a network is to provide certain users with guaranteed bandwidth levels, at a certain cost, and to leave the remaining capacity to be shared between smaller users without any guarantee of availability. However, such a method avoids entering into the complexity of the problem. Individual users rarely use all of their capacity all of the time, but rather tend to fall into certain usage patterns having peak usage times and minimal usage times. [0004] Furthermore if a new service is developed at a given location, the sudden appearance of the new service may upset existing network resource allocations, and one of the key issues associated with a managed data network is that the traffic distribution of a new service is initially an unknown variable which dynamically changes as a function of usage patterns and service growth. Therefore, commercial launches of new services require smart mechanisms that enable cost-efficient dynamic resource trade and allocation. Without such a mechanism, there is a risk of undermining the commercial viability of the service deployment. [0005] Today, different technologies, such as traffic shapers, policy-based management and dynamic provisioning attempt to engineer and smooth the network traffic. These technologies however focus on network management and therefore obscure the underlying commercial aspect of the problem. [0006] Reference is now made to FIG. 1, which illustrates the current situation. A provider 10 (i.e. carrier, service provider, etc.) is the entity that provides the network services to its customers. The provider may sell its own services, or other providers' services. [0007] The customer is the entity that receives services from the provider. A customer may be a subscriber, business organization, SOHO, or another service provider that buys/leases communication services from the provider. Two customers, 12 and 14, are shown in FIG. 1. [0008] As shown in FIG. 1, the provider 10 runs a provider network 16. The service networks 18 and which represent service allocations over the provider network 16 to the individual users, for example as a WAN for a very large multi-location user or a VPN. The service networks 18 and 20 are set up as sub-domains within the provider's network 16 that may be allocated to the individual customer. Different 'service networks' can each be assigned to a different customer, although all are carried by the provider network. [0009] Nowadays the business/commercial relationships are handled manually, which means that it can take days to weeks to set up an individual service network to the satisfaction of both parties. The current slow commercial process leads to static allocation and therefore to inefficiency and consequent loss of potential additional revenue. The system is unable to respond to situations such as the sudden popularity of a given server due to the presence of a new website. [0010] Reference is made to the following documents, the contents of which are hereby incorporated within by reference: [0011] J. Collins, C. Bilot, M. Gini, B. Mobasher "Mixed-Initiative Decision Support in Agent-Based Automated Contracting"; In Proc. Of the Fourth Int'l Conf. On Autonomous Agents, pages 247-254, June 2000, [0012] J. Collins, R. Sundareswara, M. Tsvetovat, M. Gini, B. Mobasher "Search Strategies for Bid Selection in Multi-Agent Contracting"; [0013] J. Collins, C. Bilot, M. Gini, B. Mobasher "Mixed-Initiative Decision Support in Agent-Based Automated Contracting", and [0014] F. Stzidarovszky, M. E. Gershon, L. Duckstein "Techniques for Multi-Objective Decision Making in System Management"; Elsevier Science Publishers, B.V. 1998. [0015] Nemo Semert, Raymond R. -F. Liao, Andrew T. Campbell, Aurel A. lazar "Pricing, provisioning and peering: Dynamic Markets for differentiated internet Services and Implications for network Interconnections", Colombia University and InvisibleHand Inc. SUMMARY OF THE INVENTION [0016] The background art does not teach or suggest an automated mechanism for resource allocation. The background art also does not teach or suggest such an automated mechanism for allocating bandwidth on a communications network. [0017] The present invention overcomes these deficiencies of the background art, by providing (according to a first aspect of the present invention) a resource allocation platform for allocating resources between a provider and a plurality of users for a resource allocation price, the resources being duration dependent resources, the platform comprising: [0018] an agent-based interaction mechanism for allowing the provider and the plurality of users to indicate required and surplus resources, and [0019] a pricing engine, associated with the interaction mechanism, for ascertaining a resource allocation price. [0020] Preferably, the pricing engine comprises a learning mechanism for learning demand behavior of individual users, therefrom to provide the price. [0021] Preferably, the demand behavior is an observed demand price curve for a respective user. 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