Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The backpressure algorithm has been widely used as a distributed solution to the problem of joint rate control and routing in multi-hop data networks. By controlling a parameter $V$ in the algorithm, the backpressure algorithm can achieve an arbitrarily small utility optimality gap. Hover, this in turn brings in a large queue length at each node and hence causes large network delay. This phenomenon is known as the fundamental utility-delay tradeoff. The best known utility-delay tradeoff for general networks is $[O(1/V), O(V)]$ and is attained by a backpressure algorithm based on a drift-plus-penalty technique. This may suggest that to achieve an arbitrarily small utility optimality gap, the existing backpressure algorithms necessarily yield an arbitrarily large queue length. Hover, this paper proposes a new backpressure algorithm that has a vanishing utility optimality gap, so utility converges to exact optimality as the algorithm keeps running, while queue lengths are bounded throughout by a finite constant. The technique uses backpressure and drift concepts with a new method for convex programming.
['Hao Yu', 'Michael J. Neely']
I am writing a research paper on a idea A New Backpressure Algorithm for Joint Rate Control and Routing with Vanishing Utility Optimality Gaps and Finite Queue Lengths
[{"from": "human", "value": "I am writing a research paper on a idea A New Backpressure Algorithm for Joint Rate Control and Routing with Vanishing Utility Optimality Gaps and Finite Queue Lengths"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The backpressure algorithm has been widely used as a distributed solution to the problem of joint rate control and routing in multi-hop data networks. By controlling a parameter $V$ in the algorithm, the backpressure algorithm can achieve an arbitrarily small utility optimality gap. Hover, this in turn brings in a large queue length at each node and hence causes large network delay. This phenomenon is known as the fundamental utility-delay tradeoff. The best known utility-delay tradeoff for general networks is $[O(1/V), O(V)]$ and is attained by a backpressure algorithm based on a drift-plus-penalty technique. This may suggest that to achieve an arbitrarily small utility optimality gap, the existing backpressure algorithms necessarily yield an arbitrarily large queue length. Hover, this paper proposes a new backpressure algorithm that has a vanishing utility optimality gap, so utility converges to exact optimality as the algorithm keeps running, while queue lengths are bounded throughout by a finite constant. The technique uses backpressure and drift concepts with a new method for convex programming."}]