Method and system for the dynamic allocation of resources based on fairness, throughput, and user behavior measurement
First Claim
1. A method for dynamically allocating resources in a process, said method comprising:
- estimating a fairness coefficient and a throughput coefficient that respectively represent a significance of a fairness and a throughput utilizing a reinforcement learning algorithm in order to thereafter vary a degree of said fairness coefficient and said throughput coefficient while allocating a resource;
computing a user behavior coefficient with respect to a user to determine a degree of cooperativeness of said user with a plurality of other users and updating said user behavior coefficient thereof in order to dynamically allocate said resource in a process with a high user satisfaction and a retention rate;
implementing an exploration and exploitation as a function of a temperature parameter by said reinforcement learning algorithm; and
determining an optimal value of said fairness coefficient and said throughput coefficient for successive iterations utilizing a probability function based on a selection index and said temperature parameter.
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Abstract
A system and method for the dynamic allocation of resources based on fairness, throughput, and user behavior measurement. A resource allocation decision can be made based on an index value computed by a selection index function. A fairness coefficient and a throughput coefficient, which represent the significance of fairness and throughput can be computed utilizing a reinforcement learning algorithm. The degree of fairness and throughput coefficient can be varied while allocating resources. A user behavior coefficient with respect to a user can be computed to determine the degree of cooperativeness of the user with other users and the value of user behavior coefficient can be updated each time it interacts with the system.
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Citations
17 Claims
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1. A method for dynamically allocating resources in a process, said method comprising:
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estimating a fairness coefficient and a throughput coefficient that respectively represent a significance of a fairness and a throughput utilizing a reinforcement learning algorithm in order to thereafter vary a degree of said fairness coefficient and said throughput coefficient while allocating a resource; computing a user behavior coefficient with respect to a user to determine a degree of cooperativeness of said user with a plurality of other users and updating said user behavior coefficient thereof in order to dynamically allocate said resource in a process with a high user satisfaction and a retention rate; implementing an exploration and exploitation as a function of a temperature parameter by said reinforcement learning algorithm; and determining an optimal value of said fairness coefficient and said throughput coefficient for successive iterations utilizing a probability function based on a selection index and said temperature parameter. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system for dynamically allocating resources in a process, said system comprising:
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a processor; a data bus coupled to said processor; and a computer-usable medium embodying computer code, said computer-usable medium being coupled to said data bus, said computer program code comprising instructions executable by said processor and configured for; estimating a fairness coefficient and a throughput coefficient that respectively represent a significance of a fairness and a throughput utilizing a reinforcement learning algorithm in order to thereafter vary a degree of said fairness coefficient and said throughput coefficient while allocating a resource; computing a user behavior coefficient with respect to a user to determine a degree of cooperativeness of said user with a plurality of other users and updating said user behavior coefficient thereof in order to dynamically allocate said resource in a process with a high user satisfaction and a retention rate; implementing an exploration and exploitation as a function of a temperature parameter by said reinforcement learning algorithm; and determining an optimal value of said fairness coefficient and said throughput coefficient for successive iterations utilizing a probability function based on a selection index and said temperature parameter. - View Dependent Claims (12, 13, 14, 15)
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16. A non-transitory processor-readable medium storing code representing instructions to cause a process to perform a process to dynamically allocate resources in a process, said code comprising code to:
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estimate a fairness coefficient and a throughput coefficient that respectively represent a significance of a fairness and a throughput utilizing a reinforcement learning algorithm in order to thereafter vary a degree of said fairness coefficient and said throughput coefficient while allocating a resource; compute a user behavior coefficient with respect to a user to determine a degree of cooperativeness of said user with a plurality of other users and updating said user behavior coefficient thereof in order to dynamically allocate said resource in a process with a high user satisfaction and a retention rate; implement an exploration and exploitation as a function of a temperature parameter by said reinforcement learning algorithm; and determine an optimal value of said fairness coefficient and said throughput coefficient for successive iterations utilizing a probability function based on a selection index and said temperature parameter. - View Dependent Claims (17)
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Specification