Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The IETF's Integrated Services (IntServ) architecture together with reservation aggregation provide a mechanism to support the quality-of-service demands of real-time flows in a scalable way, i.e., without requiring that each router be signaled with the arrival or departure of each new flow for which it will forward data. Hover, reserving resources in "bulk" implies that the reservation will not precisely match the true demand. Consequently, if the flows' demanded bandwidth varies rapidly and dramatically, aggregation can incur significant performance penalties of under-utilization and unnecessarily rejected flows. On the other hand, if demand varies moderately and at slor time scales, aggregation can provide an accurate and scalable approximation to IntServ. ,  develop a simple analytical model and perform extensive trace-driven simulations to explore the efficacy of aggregation under a broad class of factors. Example findings include (1) a simple single-time-scale model with random noise can capture the essential behavior of surprisingly complex scenarios; (2) with a twoorder-of-magnitude separation beten the dominant time scale of demand and the time scale of signaling and moderate levels of secondary noise, aggregation achieves performance that closely approximates that of IntServ.
['Huirong Fu', 'Edward W. Knightly']
I am writing a research paper on a idea Aggregation and Scalable QoS: A Performance Study
[{"from": "human", "value": "I am writing a research paper on a idea Aggregation and Scalable QoS: A Performance Study"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The IETF's Integrated Services (IntServ) architecture together with reservation aggregation provide a mechanism to support the quality-of-service demands of real-time flows in a scalable way, i.e., without requiring that each router be signaled with the arrival or departure of each new flow for which it will forward data. Hover, reserving resources in \"bulk\" implies that the reservation will not precisely match the true demand. Consequently, if the flows' demanded bandwidth varies rapidly and dramatically, aggregation can incur significant performance penalties of under-utilization and unnecessarily rejected flows. On the other hand, if demand varies moderately and at slor time scales, aggregation can provide an accurate and scalable approximation to IntServ. ,  develop a simple analytical model and perform extensive trace-driven simulations to explore the efficacy of aggregation under a broad class of factors. Example findings include (1) a simple single-time-scale model with random noise can capture the essential behavior of surprisingly complex scenarios; (2) with a twoorder-of-magnitude separation beten the dominant time scale of demand and the time scale of signaling and moderate levels of secondary noise, aggregation achieves performance that closely approximates that of IntServ."}]